if_calibration_isimip2a.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import scipy\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "from climada.hazard.low_flow import LowFlow\n",
    "\n",
    "from climada.entity import ImpactFunc, ImpactFuncSet\n",
    "from climada.entity.exposures import Exposures\n",
    "from climada.engine import Impact\n",
    "import datetime\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get impact functions, using ISIMIP2a where the actual events should be seen as the data is forced with reanalysis data.\n",
    "\n",
    "\n",
    "\n",
    "###            1. load hazard & exposures for the years& regions found in the literature research\n",
    "###            2. change slope of impact function and calculate impacts until equivalent loss of production is found\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 12:57:06,925 - climada.util.coordinates - INFO - Setting geometry points.\n",
      "2020-07-02 12:57:07,129 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n",
      "2020-07-02 12:57:07,129 - climada.entity.exposures.base - INFO - tag metadata set to default value:  File: \n",
      " Description: \n",
      "2020-07-02 12:57:07,130 - climada.entity.exposures.base - INFO - ref_year metadata set to default value: 2018\n",
      "2020-07-02 12:57:07,130 - climada.entity.exposures.base - INFO - value_unit metadata set to default value: USD\n",
      "2020-07-02 12:57:07,130 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n",
      "2020-07-02 12:57:07,131 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n",
      "2020-07-02 12:57:07,131 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2020-07-02 12:57:07,132 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2020-07-02 12:57:07,132 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2020-07-02 12:57:07,133 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2020-07-02 12:57:07,134 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2020-07-02 12:57:07,165 - climada.util.coordinates - INFO - Setting geometry points.\n",
      "2020-07-02 12:57:07,171 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n",
      "2020-07-02 12:57:07,172 - climada.entity.exposures.base - INFO - tag metadata set to default value:  File: \n",
      " Description: \n",
      "2020-07-02 12:57:07,172 - climada.entity.exposures.base - INFO - ref_year metadata set to default value: 2018\n",
      "2020-07-02 12:57:07,172 - climada.entity.exposures.base - INFO - value_unit metadata set to default value: USD\n",
      "2020-07-02 12:57:07,173 - climada.entity.exposures.base - INFO - meta metadata set to default value: None\n",
      "2020-07-02 12:57:07,173 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n",
      "2020-07-02 12:57:07,174 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2020-07-02 12:57:07,174 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2020-07-02 12:57:07,174 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2020-07-02 12:57:07,175 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2020-07-02 12:57:07,175 - climada.entity.exposures.base - INFO - region_id not set.\n"
     ]
    }
   ],
   "source": [
    "#import filtered exposures\n",
    "hydro = pd.read_excel('exposures/hydro_exposures.xlsx', index_col=0)\n",
    "hydro = Exposures(hydro)\n",
    "hydro.set_geometry_points()\n",
    "hydro.check()\n",
    "\n",
    "nuclear = pd.read_excel('exposures/nuclear_exposures.xlsx', index_col=0)\n",
    "nuclear = Exposures(nuclear)\n",
    "nuclear.set_geometry_points()\n",
    "\n",
    "nuclear.check()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to get impact functions\n",
    "def init_IFS_linear(s):\n",
    "    \"\"\"define impact function imp_fun and return impact function set (IFS)\"\"\"\n",
    "    x0 = np.linspace(1,600,100)\n",
    "    imp_fun = ImpactFunc()\n",
    "    imp_fun.haz_type = 'LF'\n",
    "    imp_fun.id = 1\n",
    "    imp_fun.name = 'Low flow business interruption'\n",
    "    imp_fun.intensity_unit = 'days'\n",
    "    imp_fun.intensity = x0\n",
    "    imp_fun.mdd = s*x0\n",
    "    imp_fun.mdd[imp_fun.mdd>1]=1\n",
    "    if s<=1/365:\n",
    "        imp_fun.mdd[imp_fun.intensity>365] = s*x0[imp_fun.intensity>365]    \n",
    "    else:\n",
    "        imp_fun.mdd[imp_fun.intensity>365] = 1/365*x0[imp_fun.intensity>365]  \n",
    "\n",
    " # loss to annual operation\n",
    "    imp_fun.paa = imp_fun.intensity * 0 + 1\n",
    "   \n",
    "    #imp_fun.paa[imp_fun.intensity<mdm] = 0\n",
    "    imp_fun.check()\n",
    "    IFS = ImpactFuncSet()\n",
    "    IFS.append(imp_fun)\n",
    "    return IFS\n",
    "\n",
    "historical_data = pd.read_excel('historical_impacts_isimip2a.xlsx')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## function load hazards with only the parameters that we want to vary here\n",
    "\n",
    "def make_hazard(percentile,yearrange,gh_model,cl_model,bbox):\n",
    "    input_dir = '../data/isimip_data/'\n",
    "\n",
    "    min_days_per_month = 5\n",
    "\n",
    "    fn_str_var= 'co2_dis_global_daily'\n",
    "    scenario = 'hist'\n",
    "    soc = 'pressoc'\n",
    "    yearrange_ref=[1981,2005]\n",
    "    min_intensity=5\n",
    "    min_number_cells=16\n",
    "\n",
    "    hazard = LowFlow()\n",
    "    hazard.set_from_nc(percentile=percentile, input_dir=input_dir, yearrange=yearrange,yearrange_ref=yearrange_ref,bbox = bbox, gh_model=gh_model,\\\n",
    "                            cl_model=cl_model,  bias_correction = 'ewembi',scenario=scenario, scenario_ref=scenario, soc=soc, soc_ref=soc, keep_dis_data = True, min_days_per_month=min_days_per_month,\\\n",
    "                                min_intensity=min_intensity, min_number_cells=min_number_cells)\n",
    "    hazard.check()\n",
    "    return hazard"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# code to make the hazards, those are saved and should not have to be computed again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#input_dir = '../data/isimip_data/'\n",
    "#min_days_per_month = 5\n",
    "#percentile = 2.5\n",
    "#fn_str_var= 'co2_dis_global_daily'\n",
    "#scenario = 'hist'\n",
    "#soc = 'pressoc'\n",
    "#yearrange = [1990,2010]\n",
    "#yearrange_ref=[1981,2005]\n",
    "#bbox = [-15,30,50,72]\n",
    "#min_intensity=5\n",
    "#min_number_cells=16\n",
    "\n",
    "#for percentile in [2.5,10]:\n",
    "#    for gh_model in ['lpjml']: #matsiro does not have any data for soc='pressoc'\n",
    "#        for cl_model in ['gswp3', 'wfdei']:\n",
    "\n",
    "#            hazard = LowFlow()\n",
    "#            hazard.set_from_nc(percentile=percentile, input_dir=input_dir, yearrange=yearrange,yearrange_ref=yearrange_ref,bbox = bbox, gh_model=gh_model,\\\n",
    "#                                    cl_model=cl_model,  bias_correction = 'nobc',scenario=scenario, scenario_ref=scenario, soc=soc, soc_ref=soc, keep_dis_data = True, min_days_per_month=min_days_per_month,\\\n",
    "#                                        min_intensity=min_intensity, min_number_cells=min_number_cells)\n",
    "#            hazard.check()\n",
    "#            hazard.write_hdf5('_'.join(['hazards/low_flow','europe',str(yearrange[0]),str(yearrange[1]),str(percentile),gh_model,cl_model,'.h5']))\n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#for percentile in [2.5,10]:\n",
    "#    for gh_model in ['h08']: #matsiro does not have any data for soc='pressoc'\n",
    "#        for cl_model in ['gswp3','princeton']:\n",
    "\n",
    "#            hazard = LowFlow()\n",
    "#            hazard.set_from_nc(percentile=percentile, input_dir=input_dir, yearrange=yearrange,yearrange_ref=yearrange_ref,bbox = bbox, gh_model=gh_model,\\\n",
    "#                                    cl_model=cl_model,  bias_correction = 'nobc',scenario=scenario, scenario_ref=scenario, soc=soc, soc_ref=soc, keep_dis_data = True, min_days_per_month=min_days_per_month,\\\n",
    "#                                        min_intensity=min_intensity, min_number_cells=min_number_cells)\n",
    "#            hazard.check()\n",
    "#            hazard.write_hdf5('_'.join(['hazards/low_flow','europe',str(yearrange[0]),str(yearrange[1]),str(percentile),gh_model,cl_model,'.h5']))\n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First testing some individual events with some arbitrary model combination to test the method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 13:38:01,728 - climada.hazard.base - INFO - Reading hazards/low_flow_europe_1990_2010_2.5_lpjml_gswp3.h5\n",
      "2020-07-02 13:38:01,738 - climada.hazard.base - INFO - Reading hazards/low_flow_europe_1990_2010_10_h08_gswp3.h5\n"
     ]
    }
   ],
   "source": [
    "# get data for one climate and hydro model\n",
    "hazard_2_5 = LowFlow()\n",
    "hazard_2_5.read_hdf5('hazards/low_flow_europe_1990_2010_2.5_lpjml_gswp3.h5')\n",
    "hazard_2_5.check()\n",
    "\n",
    "hazard_10 = LowFlow()\n",
    "hazard_10.read_hdf5('hazards/low_flow_europe_1990_2010_10_h08_gswp3.h5')\n",
    "hazard_10.check()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spain 2005 Drought\n",
    "\n",
    "In Spain, the 2005 drought led to a reduction of 36% in national hydroelectric power\n",
    "production (equivalent to 12 876 GWh), with respect to the past five year average. The\n",
    "decrease in hydraulic and nuclear production has been compensated by an increase in fuel-gas\n",
    "plant production and combined cycles that increased their production by respectively 28% and\n",
    "66%, compared to 2004. This extra production led to an additional cost of 713 Mio €. \n",
    "source: https://ec.europa.eu/environment/water/quantity/pdf/comm_droughts/2nd_int_report.pdf p.45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 13:39:36,844 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n",
      "2020-07-02 13:39:36,845 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n",
      "2020-07-02 13:39:36,845 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2020-07-02 13:39:36,846 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2020-07-02 13:39:36,846 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2020-07-02 13:39:36,846 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2020-07-02 13:39:36,847 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2020-07-02 13:39:36,851 - climada.entity.exposures.base - INFO - Matching 124 exposures with 10920 centroids.\n",
      "2020-07-02 13:39:36,888 - climada.util.interpolation - WARNING - Distance to closest centroid is greater than 100km for 1 coordinates.\n",
      "2020-07-02 13:39:36,890 - climada.engine.impact - INFO - Calculating damage for 123 assets (>0) and 15 events.\n",
      "34.15185226053404\n"
     ]
    }
   ],
   "source": [
    "date0 = '2005-01-01'\n",
    "daten = '2006-01-01' \n",
    "hazard_2_5.select(date=(date0,daten))\n",
    "hazard_10.select(date=(date0,daten))\n",
    "\n",
    "hydro_spain = hydro[hydro['country']=='ESP']\n",
    "hydro_spain = Exposures(hydro_spain)\n",
    "hydro_spain.check()\n",
    "\n",
    "date0 = '2005-01-01'\n",
    "daten = '2006-01-01' \n",
    "\n",
    "IFS_lf = init_IFS_linear(0.5)\n",
    "\n",
    "imp_spain_2005 = Impact()\n",
    "imp_spain_2005.calc(hydro_spain, IFS_lf, hazard_2_5.select(date=(date0,daten)))\n",
    "print(imp_spain_2005.at_event.sum()/(imp_spain_2005.tot_value)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 13:39:46,285 - climada.engine.impact - INFO - Exposures matching centroids found in centr_LF\n",
      "2020-07-02 13:39:46,286 - climada.engine.impact - INFO - Calculating damage for 123 assets (>0) and 12 events.\n",
      "36.1919851202609\n"
     ]
    }
   ],
   "source": [
    "\n",
    "IFS_lf = init_IFS_linear(0.014)\n",
    "\n",
    "date0 = '2005-01-01'\n",
    "daten = '2006-01-01' \n",
    "imp_spain_2005 = Impact()\n",
    "imp_spain_2005.calc(hydro_spain, IFS_lf, hazard_10.select(date=(date0,daten)))\n",
    "print(imp_spain_2005.at_event.sum()/(imp_spain_2005.tot_value)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8c8926400>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IFS_lf.plot() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appears to work with the 10th percentile but the steepness of the impact function doesn't seem very realistic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Germany 2003 ->11% loss of production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 13:40:20,016 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n",
      "2020-07-02 13:40:20,017 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n",
      "2020-07-02 13:40:20,018 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2020-07-02 13:40:20,019 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2020-07-02 13:40:20,020 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2020-07-02 13:40:20,020 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2020-07-02 13:40:20,021 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2020-07-02 13:40:20,024 - climada.entity.exposures.base - INFO - Matching 5 exposures with 10920 centroids.\n",
      "2020-07-02 13:40:20,034 - climada.engine.impact - INFO - Calculating damage for 5 assets (>0) and 13 events.\n",
      "11.686543035783343\n"
     ]
    }
   ],
   "source": [
    "nuclear_germany = nuclear[nuclear['country_long']=='Germany']\n",
    "nuclear_germany = Exposures(nuclear_germany)\n",
    "nuclear_germany.check()\n",
    "\n",
    "date0 = '2003-01-01'\n",
    "daten = '2004-01-01' \n",
    "\n",
    "IFS_lf = init_IFS_linear(0.003)\n",
    "\n",
    "imp_germany_2003 = Impact()\n",
    "imp_germany_2003.calc(nuclear_germany, IFS_lf, hazard_2_5.select(date=(date0,daten))) #with the 2.5th percentile hazards\n",
    "print(imp_germany_2003.at_event.sum()/(imp_germany_2003.tot_value)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## checking 'normal' yrs for nuclear in germany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 13:40:44,432 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n",
      "2020-07-02 13:40:44,433 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n",
      "2020-07-02 13:40:44,433 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2020-07-02 13:40:44,433 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2020-07-02 13:40:44,434 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2020-07-02 13:40:44,434 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2020-07-02 13:40:44,434 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2020-07-02 13:40:44,438 - climada.entity.exposures.base - INFO - Matching 5 exposures with 10920 centroids.\n",
      "2020-07-02 13:40:44,447 - climada.engine.impact - INFO - Calculating damage for 5 assets (>0) and 108 events.\n",
      "4.363540931807163\n"
     ]
    }
   ],
   "source": [
    "nuclear_germany = nuclear[nuclear['country_long']=='Germany']\n",
    "nuclear_germany = Exposures(nuclear_germany)\n",
    "nuclear_germany.check()\n",
    "\n",
    "date0 = '1990-01-01'\n",
    "daten = '2000-01-01' \n",
    "\n",
    "IFS_lf = init_IFS_linear(0.007)\n",
    "\n",
    "imp_germany_2004_2010 = Impact()\n",
    "imp_germany_2004_2010.calc(nuclear_germany, IFS_lf, hazard_2_5.select(date=(date0,daten)))\n",
    "print(imp_germany_2004_2010.at_event.sum()/(imp_germany_2004_2010.tot_value*10)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# France 2003 & 2006\n",
    "\n",
    "## Les pertes de production ont atteint 5,5 TWh en 2003 et 2,5 TWh en 2006\n",
    "https://www.sfen.org/rgn/adapter-centrales-nucleaires-changement-climatique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 13:41:04,127 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n",
      "2020-07-02 13:41:04,130 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n",
      "2020-07-02 13:41:04,131 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2020-07-02 13:41:04,132 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2020-07-02 13:41:04,134 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2020-07-02 13:41:04,134 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2020-07-02 13:41:04,135 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2020-07-02 13:41:04,143 - climada.entity.exposures.base - INFO - Matching 14 exposures with 10920 centroids.\n",
      "2020-07-02 13:41:04,159 - climada.engine.impact - INFO - Calculating damage for 14 assets (>0) and 13 events.\n",
      "5.981567464723585\n"
     ]
    }
   ],
   "source": [
    "nuclear_france = nuclear[nuclear['country_long']=='France']\n",
    "nuclear_france = Exposures(nuclear_france)\n",
    "nuclear_france.check()\n",
    "\n",
    "date0 = '2003-01-01'\n",
    "daten = '2004-01-01' \n",
    "\n",
    "IFS_lf = init_IFS_linear(0.0006)\n",
    "\n",
    "imp_france_2003 = Impact()\n",
    "imp_france_2003.calc(nuclear_france, IFS_lf, hazard_2_5.select(date=(date0,daten)))\n",
    "print(imp_france_2003.at_event.sum()/1000) #(/1000 because the data is in GWh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff8c82a1048>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IFS_lf.plot() #very flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 13:41:12,488 - climada.entity.exposures.base - INFO - crs set to default value: {'init': 'epsg:4326', 'no_defs': True}\n",
      "2020-07-02 13:41:12,489 - climada.entity.exposures.base - INFO - Hazard type not set in if_\n",
      "2020-07-02 13:41:12,489 - climada.entity.exposures.base - INFO - centr_ not set.\n",
      "2020-07-02 13:41:12,489 - climada.entity.exposures.base - INFO - deductible not set.\n",
      "2020-07-02 13:41:12,490 - climada.entity.exposures.base - INFO - cover not set.\n",
      "2020-07-02 13:41:12,490 - climada.entity.exposures.base - INFO - category_id not set.\n",
      "2020-07-02 13:41:12,490 - climada.entity.exposures.base - INFO - region_id not set.\n",
      "2020-07-02 13:41:12,494 - climada.entity.exposures.base - INFO - Matching 14 exposures with 10920 centroids.\n",
      "2020-07-02 13:41:12,506 - climada.engine.impact - INFO - Calculating damage for 14 assets (>0) and 10 events.\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "nuclear_france = nuclear[nuclear['country_long']=='France']\n",
    "nuclear_france = Exposures(nuclear_france)\n",
    "nuclear_france.check()\n",
    "\n",
    "date0 = '2006-01-01'\n",
    "daten = '2007-01-01' \n",
    "\n",
    "IFS_lf = init_IFS_linear(0.002)\n",
    "\n",
    "imp_france_2006_2007 = Impact()\n",
    "imp_france_2006_2007.calc(nuclear_france, IFS_lf, hazard_2_5.select(date=(date0,daten)))\n",
    "print(imp_france_2006_2007.at_event.sum()/1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Europe 2003 6.6% loss hydro, 4.7% nuclear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 13:42:12,838 - climada.engine.impact - INFO - Exposures matching centroids found in centr_LF\n",
      "2020-07-02 13:42:12,839 - climada.engine.impact - INFO - Calculating damage for 46 assets (>0) and 13 events.\n",
      "4.541847053880298\n"
     ]
    }
   ],
   "source": [
    "#nuclear\n",
    "date0 = '2003-01-01'\n",
    "daten = '2004-01-01' \n",
    "\n",
    "IFS_lf = init_IFS_linear(0.002)\n",
    "\n",
    "imp_eu_2003 = Impact()\n",
    "imp_eu_2003.calc(nuclear, IFS_lf, hazard_2_5.select(date=(date0,daten)))\n",
    "print(imp_eu_2003.at_event.sum()/(imp_eu_2003.tot_value)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-07-02 13:42:02,235 - climada.engine.impact - INFO - Exposures matching centroids found in centr_LF\n",
      "2020-07-02 13:42:02,238 - climada.engine.impact - INFO - Calculating damage for 2092 assets (>0) and 13 events.\n",
      "5.126178520958528\n"
     ]
    }
   ],
   "source": [
    "#hydro\n",
    "date0 = '2003-01-01'\n",
    "daten = '2004-01-01' \n",
    "\n",
    "IFS_lf = init_IFS_linear(0.004)\n",
    "\n",
    "imp_eu_2003 = Impact()\n",
    "imp_eu_2003.calc(hydro, IFS_lf, hazard_2_5.select(date=(date0,daten)))\n",
    "print(imp_eu_2003.at_event.sum()/(imp_eu_2003.tot_value)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# now making a function which looks for the right slope of the impact function of each event for given gh model/ cl model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def if_finder(historical_data, hazard, exposures, input_dir=None, centroids=None,\n",
    "                    bbox=None, percentile=2.5, min_intensity=5, min_number_cells=16,\n",
    "                    min_days_per_month=5, yearrange_ref=[1981,2010],\n",
    "                    gh_model='h08', cl_model='gswp3',\n",
    "                    scenario='hist', scenario_ref='hist', soc='pressoc', \\\n",
    "                    fn_str_var='co2_dis_global_daily', bias_correction = 'nobc'):\n",
    "    \n",
    "    \n",
    "    \n",
    "    energies = historical_data['Energy'].unique()\n",
    "   \n",
    "    slope_list = []\n",
    "    for index, row in historical_data.iterrows():\n",
    "        year0 = historical_data['date0'][index].year\n",
    "        yearn = historical_data['daten'][index].year\n",
    "        yearrange = [year0,yearn]\n",
    "        exposuresn = exposures[historical_data['Energy'][index]]\n",
    "        country = historical_data['Country'][index].split(',')\n",
    "        exposuresn = exposuresn[exposuresn.country_long.isin(country)]\n",
    "        exposuresn = Exposures(exposuresn)\n",
    "        exposuresn.check()\n",
    "        hazardn = hazard.select(date=(historical_data['date0'][index].toordinal(),historical_data['daten'][index].toordinal()))\n",
    "        slope=0.0005\n",
    "        impacts_sum = 0\n",
    "        while historical_data['loss'][index]>impacts_sum:\n",
    "            print(impacts_sum)\n",
    "            slope = slope + 0.0005\n",
    "            if slope > 0.01: # break in case the slope becomes too large to make sense\n",
    "                slope = 'NA'\n",
    "                break\n",
    "            impact_function = init_IFS_linear(slope)\n",
    "            impact = Impact()\n",
    "            impact.calc(exposuresn, impact_function, hazardn)\n",
    "            if historical_data['unit'][index]=='percentage':\n",
    "                impacts_sum = impact.at_event.sum()/(impact.tot_value*(yearn-year0))*100\n",
    "            else:\n",
    "                impacts_sum = impact.at_event.sum()\n",
    "        slope_list.append(slope)\n",
    "    historical_data[\"_\".join(['slope_if',gh_model,cl_model])] = slope_list\n",
    "    return(historical_data)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_data = pd.read_excel('historical_impacts_isimip2a.xlsx', index_col=0)\n",
    "\n",
    "input_dir = '../data/isimip_data/'\n",
    "year0 = np.min(historical_data['date0']).year\n",
    "yearn = np.max(historical_data['daten']).year\n",
    "yearrange=[year0,yearn]\n",
    "\n",
    "\n",
    "min_days_per_month = 5\n",
    "percentile = 2.5\n",
    "#gh_model = 'h08'\n",
    "cl_model = 'gswp3'\n",
    "fn_str_var= 'co2_dis_global_daily'\n",
    "scenario = 'hist'\n",
    "soc = 'pressoc'\n",
    "yearrange_ref=[1981,2005]\n",
    "bbox = [-15,30,50,72]\n",
    "min_intensity=5\n",
    "min_number_cells=16\n",
    "\n",
    "exposures = {'Hydro': hydro, 'Nuclear': nuclear}\n",
    "\n",
    "for gh_model in ['lpjml']: #matsiro does not have any data for soc='pressoc'\n",
    "    for cl_model in ['gswp3','wfdei']:\n",
    "        \n",
    "        print(gh_model)\n",
    "        hazard = LowFlow()\n",
    "        hazard.read_hdf5(\"\".join(['hazards/low_flow_europe_1990_2010_2.5_', gh_model, '_', cl_model, '.h5']))\n",
    "        hazard.check()\n",
    "\n",
    "        historical_data = if_finder(historical_data.loc[0:12], hazard, exposures, input_dir=input_dir, bbox=bbox, gh_model=gh_model,cl_model=cl_model)\n",
    "for gh_model in ['h08']: #matsiro does not have any data for soc='pressoc'\n",
    "    for cl_model in ['gswp3','princeton']:\n",
    "\n",
    "        print(gh_model)\n",
    "        hazard = LowFlow()\n",
    "        hazard.read_hdf5(\"\".join(['hazards/low_flow_europe_1990_2010_2.5_', gh_model, '_', cl_model, '.h5']))\n",
    "        hazard.check()\n",
    "\n",
    "        historical_data = if_finder(historical_data.loc[0:12], hazard, exposures, input_dir=input_dir, bbox=bbox, gh_model=gh_model,cl_model=cl_model)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Average all the data points to get the average impact function:\n",
    "historical_data['if_slope'] = historical_data.replace('NA',float('nan')).iloc[:,8:12].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Energy</th>\n",
       "      <th>Country</th>\n",
       "      <th>date0</th>\n",
       "      <th>daten</th>\n",
       "      <th>loss</th>\n",
       "      <th>unit</th>\n",
       "      <th>slope_if_lpjml_gswp3</th>\n",
       "      <th>slope_if_h08_gswp3</th>\n",
       "      <th>slope_if_h08_princeton</th>\n",
       "      <th>slope_if_lpjml_wfdei</th>\n",
       "      <th>if_slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>21.0</td>\n",
       "      <td>percentage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>37.5</td>\n",
       "      <td>percentage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>France</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Gwh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>33.0</td>\n",
       "      <td>percentage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>17.0</td>\n",
       "      <td>percentage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>Finland, Sweden, Norway</td>\n",
       "      <td>2002-01-01</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>7.8</td>\n",
       "      <td>percentage</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>Finland, Sweden, Norway</td>\n",
       "      <td>1996-01-01</td>\n",
       "      <td>1997-01-01</td>\n",
       "      <td>21.3</td>\n",
       "      <td>percentage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hydro</td>\n",
       "      <td>Spain</td>\n",
       "      <td>1992-01-01</td>\n",
       "      <td>1994-01-01</td>\n",
       "      <td>14.5</td>\n",
       "      <td>percentage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nuclear</td>\n",
       "      <td>France</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>Gwh</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Nuclear</td>\n",
       "      <td>France</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>Gwh</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Nuclear</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2003-01-01</td>\n",
       "      <td>2004-01-01</td>\n",
       "      <td>12.0</td>\n",
       "      <td>percentage</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Energy                  Country      date0      daten     loss  \\\n",
       "0     Hydro                 Portugal 2005-01-01 2006-01-01     21.0   \n",
       "1     Hydro                    Spain 2005-01-01 2006-01-01     37.5   \n",
       "2     Hydro                   France 2005-01-01 2006-01-01  10000.0   \n",
       "3     Hydro                  Albania 2003-01-01 2004-01-01     33.0   \n",
       "4     Hydro                    Spain 2004-01-01 2008-01-01     17.0   \n",
       "5     Hydro  Finland, Sweden, Norway 2002-01-01 2003-01-01      7.8   \n",
       "6     Hydro  Finland, Sweden, Norway 1996-01-01 1997-01-01     21.3   \n",
       "7     Hydro                    Spain 1992-01-01 1994-01-01     14.5   \n",
       "8   Nuclear                   France 2003-01-01 2004-01-01   5500.0   \n",
       "9   Nuclear                   France 2006-01-01 2007-01-01   2500.0   \n",
       "10  Nuclear                  Germany 2003-01-01 2004-01-01     12.0   \n",
       "\n",
       "          unit  slope_if_lpjml_gswp3  slope_if_h08_gswp3  \\\n",
       "0   percentage                   NaN                 NaN   \n",
       "1   percentage                   NaN                 NaN   \n",
       "2          Gwh                   NaN               0.003   \n",
       "3   percentage                   NaN                 NaN   \n",
       "4   percentage                   NaN                 NaN   \n",
       "5   percentage                 0.007               0.002   \n",
       "6   percentage                   NaN               0.006   \n",
       "7   percentage                   NaN                 NaN   \n",
       "8          Gwh                 0.002               0.002   \n",
       "9          Gwh                 0.002               0.002   \n",
       "10  percentage                 0.003               0.002   \n",
       "\n",
       "    slope_if_h08_princeton  slope_if_lpjml_wfdei  if_slope  \n",
       "0                      NaN                 0.003       NaN  \n",
       "1                      NaN                   NaN       NaN  \n",
       "2                    0.003                 0.003    0.0030  \n",
       "3                      NaN                   NaN       NaN  \n",
       "4                      NaN                   NaN       NaN  \n",
       "5                    0.002                   NaN    0.0045  \n",
       "6                    0.004                   NaN    0.0060  \n",
       "7                      NaN                   NaN       NaN  \n",
       "8                    0.002                   NaN    0.0020  \n",
       "9                    0.002                 0.002    0.0020  \n",
       "10                   0.002                   NaN    0.0025  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7274f53828>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RLL+wnBL5SjDvjXn0r9sfW+vsud2uSgqKolisBw+0Vchr10Lt2trmN56e5o5KE5scy7Qj05h1YhZSSkZ4jWBE0xEUtrfQzRgySCUFRVEsjpSwcqVW0TQ+XtsvedgwsLWAD996g56fz/7M2ANjefDkAR/U+oDJrSdToUgFc4eWJVRSUBTFoty+re11sGMHNG6sFbCrUcPcUWmDyNuDtzPUfyhXHl6heYXmbOu1Dc8yFtJ0ySIqKSiKYhEMBli4UGsRGAwwdy4MHGgZBezOR5zHd7cv+27uo2rxqmx8byNdqnXJdoPIGaGSgqIoZnftmrZP8uHD4O2tJQdL2FU3LDaMb/Z9g1+AH8XyFuOHjj/wab1Ps+0gckaopKAoitno9TBzJowdq80m+uUX8PExf4mKuOQ4vj/6PTOPzyRNpjG0yVBGNhtJEfsi5g3MBFRSUBTFLAIC4MMP4exZ6NYN5s0DR+NtPZwheoOepeeWMmb/GO4/uU9Pt55MaTMF5yLO5g3MhFRSUBTFpJKTYeJEmDYNiheH336D7t3NG5OUkp3XdzLUfyiXIi/hVd6LTe9vomG5huYNzAxUUlAUxWSOHdMK2AUFQd++WgG7YsXMG1NARABD/Iew58YeKherzPoe6+lWvVuOHETOCJUUFEUxuvh4GD0afvgBypeHnTuhfXvzxnQ39i7f7v+WZeeXUTRvUea0n8Nn9T/DztrOvIGZmdGSghCiPOAHOAAGYJGUcq4QohiwFnAGQoEeUspooaXlucAbQALQT0p51ljxKYpiGv7+8MkncOuWNsV0yhQoWNB88cSnxDP96HRmHJ+B3qDn68ZfM7rZaIrmLWq+oCyIMVsKesBXSnlWCFEQOCOE8Af6AXullFOFECOAEcBwoCNQJf2rIbAg/b+KomRD0dHg66vNKKpWDQ4dgqZNzRdPmiGNX87/wrf7vyUiPoJ3Xd9latupVCxa0XxBWSCjJQUpZTgQnv59nBDiClAW6Aq0TL9MBxxASwpdAT8ppQROCCGKCCEc0++jKEo2smGD1iqIjISRI2HMGLC3N188u67vYoj/EAIfBNK4XGM29NhA4/KNzReQBTPJmIIQwhmoA/wBlH76Ri+lDBdClEq/rCxw57mXhaUf+1NSEEJ8AnwC4OTkZNS4FUXJnIgIGDQI1q8HDw/Yvh3q1DFfPBfvX2So/1B2heyiYtGKrHt3Hd1rdM+1g8gZYfSkIIQoAKwHvpJSxv7LH8aLTsi/HZByEbAIwNPT82/nFUUxPSnBzw8GD4aEBG3cYMgQ8xWwC48LZ8z+MSw9v5RCeQoxs91MBtYfSB6bPOYJKBsxalIQQtiiJYSVUsoN6YfvP+0WEkI4Ag/Sj4cBz+9VVw64Z8z4FEV5fbduwaefwq5d4OWlbYJTvbp5YnmS8oQZx2Yw/dh0UtJS+KLBF3zb4luK5TXzvNdsxMpYN06fTbQEuCKlnPXcqc2AT/r3PsCm5473FZpGwGM1nqAolstggB9/hJo14cgRbbrpoUPmSQhphjSWnltKlR+qMO7gODpU7sDlgZeZ3WG2SgiZZMyWghfQB7gohDiffmwUMBX4VQjxEXAbeDf93Ha06ajX0aak/p8RY1MU5TVcvaotQjt6VFtvsHAhVDDTdgL+If4M8R/ChfsXaFi2IeveXYeXk5d5gskBjDn76AgvHicAaPOC6yUw0FjxKIry+lJTYcYMGD8e8uUDnQ769DFPAbtLDy4x1H8oO67vwLmIM2u6r6FHzR5qEPk1qRXNiqJkyLlzWgG78+fhnXe07iIHB9PHcT/+PmP2j+Hncz9T0K4g072nM6jBIOxtzDjnNQdRSUFRlH+VlKS1DKZPh5Iltemmb79t+jgSUhOYdXwW045OI0mfxMD6AxnTYgwl8pUwfTA5mEoKiqL8oyNHtLGDa9fg//5P2/ugqImrQRikgeUByxm9bzR34+7SrXo3pradStXiVU0bSC6hkoKiKH8TF6etRJ43D5ydYfdubUc0U9t3cx++u305H3EezzKerOq+iuYVmps+kFxEJQVFUf5k505t3cGdO/DllzBpEhQoYNoYrkReYdieYWy9thWnwk6sfHsl77u9j5Uw2ix6JZ1KCoqiAPDoEXz9tbYyuUYNbbppYxOXB3rw5AHjDoxj0ZlF5LfLz9Q2U/my0ZdqENmEVFJQlFxOSm3weOBAiIqCb77RvvKYsCJEYmoic07M4bsj35GQmsAAzwGMbTGWkvlLmi4IBVBJQVFytfBwLRn8/jvUq6eNHbi7m+75Bmlg1cVVjNo7ijuxd+hSrQvft/2eaiWqmS4I5U9UUlCUXEhKWLZM6y5KSoLvv9eK2dmY8B3hYOhBfHf7cib8DHUd6+LXzY+Wzi1NF4DyQiopKEouc/OmthPanj3QvDksXgxVTTi78+rDqwzbM4zNVzdTrlA5lndbTq9avdQgsoVQSUFRcom0NK2A3ahRYGUF8+drs4ysTPReHPkkkvEHx/PT6Z/Ia5uXya0nM7jRYPLa5jVNAEqGqKSgKLnA5cvw8cdw/Dh07KgVsCtf/uWvywpJ+iTmnpjLlCNTeJLyhP51+zOu5ThKFyhtmgCUTFFJQVFysNRUmDYNJk6EggVhxQro1cs0BewM0sCawDWM3DuS249v06lqJ6a1nYZrSVfjP1x5ZSopKEoOdfq0VqLiwgV47z3473+hVKmXvy4rHL51GN/dvpy6dwoPBw+WdllKm4p/K46sWCCVFBQlh0lMhLFjtTpFDg6wcSN07WqaZwc/Cmb4nuH8HvQ7ZQuWZVnXZfRx76MGkbMRlRQUJQc5eFAbO7h+Hfr316aaFili/Oc+SnjEhIMTmH96Pnms8zCx1US+bvw1+WzzGf/hSpZSSUFRcoDYWBg+HH76CSpWhL17oXVr4z83WZ/MDyd/YNKhScSlxPFxnY8Z32o8DgXMsNGCkiVUUlCUbG7bNhgwAO7d0xajTZgA+fMb95lSSn699Csj9o4gNCaUjpU78r3397iVcjPugxWjU0lBUbKphw/hq69g5UpwdYXffoOGDY3/3KO3jzLEfwgnwk5Qu3RtdvfejXclM9TVVoxCJQVFyWakhLVr4T//gZgYbVB55EjjF7ALiQph+J7hrL+yHscCjizpsgQfdx+srayN+2DFpFRSUJRs5O5d+Pxz2LwZPD1h6VKoVcu4z4xKjGLiwYnMOzUPW2tbxrUYx5AmQ8hvZ+Q+KuVfSSkRRlhwkqGkIISwBzoBzYAyQCIQCGyTUl7K8qgURfkTKeHnn2HIEG1B2owZ2gY4xixgl6xPZv6p+Uw8NJGYpBg+rPMhE1tNxLGgo/Eeqvyr+JR4NlzZgC5ARw/XHnzq+WmWP+Olf6WEEOOAzsAB4A/gAWAPVAWmpicMXynlhSyPTlEUQkK06aX790PLlloBu8qVjfc8KSXrr6xn+J7h3Ii+QbtK7ZjuPZ3apWsb76HKPzJIAwdCD6AL0LH+8nqepD6hYtGK5LExTn9hRj5nnJJSjvuHc7OEEKUAp6wLSVEU0ArYzZ2rbXhja6vVK/r4Y+MWsDsRdgLf3b4cu3MMt1Ju7PxgJ+0rtzfeA5V/FPwoGL8AP/wu+HH78W0K5SlET7ee+Hj44FXeyyhdR5CBpCCl3PbXY+mtAzspZayU8gFa60FRlCwSGKiVqDh5Ejp1ggULoFw54z3vZvRNRuwdwa+XfsWhgAOLOy+mn0c/bKzUsKMpxSTF8OulX9EF6Dh25xhWwgrvit5MbTOVrtW7mmQxYKb/xIUQHwN9ACshxGEp5aisD0tRcqeUFPjuO5g8GQoXhlWr4P33jVfALjoxmsmHJ/PDyR+wFtaMaT6GoV5DKWBXwDgPVP5Gb9DjH+KPLkDHxqCNJKcl41rSlWltp/FBrQ8oW6isSePJyJhCZynllucOtZVStkg/FwC8MCkIIZaiDU4/kFK6pR8bB/QHItMvGyWl3J5+biTwEZAGfCGl3PVKv5GiZFMnT2qtg8BArZLpnDlQ0khbFKekpbDg1AImHJpAdGI0/Tz6MbHVRJO/AeVmgQ8C0Z3XseLiCiLiIyiWtxj96/bHx8OHeo71jNY99DIZaSm4p7cOxkgpA4ALQoiVgAT+bebRMuBHwO8vx2dLKWc8f0AI4Qq8D9REm920RwhRVUqZlrFfQ1Gyr4QEGDMGZs8GR0dtumnnzsZ5lpSSjUEbGbZnGNejrtPGpQ0z2s3Aw8HDOA9U/iTySSSrA1ejC9BxNvwsNlY2vFnlTXzcfXiz6pvYWduZO8QMjSlMEkI4ABPSM9cYoACQ799mHEkpDwkhnDMYR1dgjZQyGbgphLgONACOZ/D1ipIt7d+vDR7fuKHtgjZtmtZtZAwn755kyO4hHL59GNeSrmzrtY2OlTua7RNpbpGSlsK2a9vQBejYFrwNvUFPHYc6zGk/h161elEyv5Gag68oo2MKT4CvgCrAIuAUMP0VnzlICNEXOI02lTUaKAuceO6asPRjfyOE+AT4BMDJSU16UrKnx49h2DBYtAgqVYJ9+6BVK+M8KzQmlFF7R7E6cDWl8pfipzd/4qO6H6lBZCOSUnIm/Ay68zpWB67mUeIjHAo48FXDr+jr3pdapY284vA1ZGRMYRLQHLAF1kopuwghugDbhBDLpJTLM/G8BcBEtK6nicBM4EPgRR9V5ItuIKVchJaY8PT0fOE1imLJtmyBzz6D8HBtMdr48ZDPCJNKYpJi+O7wd8z9Yy5CCEY3G81wr+EUzFMw6x+mAHAv7h4rLqxAF6DjcuRl8ljn4a3qb+Hj7oN3Je9skYgzEmEnKaWH0NqYZ4A5UsrNQojtwMDMPExKef/p90KIxcDW9B/DgOd3jC0H3MvMvRXF0kVGaquQV68GNzfYsAEaNMj656SmpbLwzELGHRhHVGIUfdz7MLn1ZMoVMuKc1lwsMTWRjUEb0QXo8L/hj0EaaFyuMQs7LaRHzR4UsTfBhhZZKCNJIVAIsRzICxx8elBKqQfmZuZhQghHKWV4+o/d0EplAGwGVgkhZqENNFcBTmbm3opiqaTUEsEXX2j7HowfDyNGgF0WjylKKdl8dTPD9gzj2qNrtHJuxYx2M6jrWDdrH6QgpeTonaPozuv49fKvxCbH4lTYiZFNR9LXvS9Vi1c1d4ivLCMDzb2FELWAVCllUEZvLIRYDbQESgghwoCxQEshhAda11Ao8Gn6My4JIX4FLgN6YKCaeaTkBGFhWlfR1q1aWeslS6Bmzax/zul7pxmyewgHbx2keonqbOm5hTervKkGkbNYaEyotso4wI+Q6BDy2+anu2t3fNx9aOncMkdsOyqk/PdueSFEUynlkX85XwhwklIG/tM1xuLp6SlPnz5t6scqyksZDFqNoqFDQa/XFqN98QVYZ3GV6duPbzN632hWXFhByXwlGd9yPP3r9c8WfdfZRVxyHL9d/g1dgI6Dt7TOklbOrfBx96G7a/dsudBPCHFGSun5onMZ+ZvTXQjxPbATbUwhEq0gXmWgFVAB8M2iWBUl2wsO1grYHTyobYm5eLG2RWZWik2OZeqRqcw+MRuAkU1HMqLpCArlKZS1D8qlDNLA/pv7WRawjA1XNpCQmkDlYpWZ2GoifWr3oUKRCuYO0Wgy0n00WAhRFHgHeBdwRCudfQVY+G+tCEXJTfR6bRXyt99qG978/DN8+GHWlqjQG/QsPrOYsQfGEpkQSe/avZncejJOhdX07Kxw9eFV/AL8WH5hOXdi71A4T2F61+qNj4cPjcs1zhXdcRlqY6avJVic/qUoyl9cuKCVqDh9Grp2hfnzoUyZrLu/lJJtwdsY6j+UoIdBtKjQgpntZlKvTL2se0guFZ0YzZrANfhd8ONE2AmshBXtK7Vnuvd0ulTrQl7bvOYO0aQy3PEohPgS+AWIQ0sOdYERUsrdRopNUSxecjJMmaJ9FS2qbZP57rtZ2zo4F36OIf5D2HdzH1WLV2XT+5voXLVzrvjUaix6g56d13eiC9Cx+epmUtJScCvlxnTv6XxQ64NcvZFQZkajPpRSzhVCtAdKAf+HliRUUlBypRMntNbB5cvQu7fWdVS8eNbdPyw2jG/2fYNfgB/F8xXnx44/8km9T7C1ts26h+QyAREB+AX4sfLiSu4/uU+JfFQHswUAACAASURBVCUYUG8APh4+1HGooxItmUsKT/9vvQH8IqUMEOr/oJILPXmibXwzd662x8G2bfDGG1l3/7jkOKYdncas47MwSAPDvIYxsulICtsbqShSDvfgyQNWXVzFsvPLCLgfgK2VLZ2qdsLH3YeOVTpaRBE6S5KZpHBGCLEbcAFGCiEKAgbjhKUolmnvXm1m0c2b8Pnn2t4HhbJowo/eoGfpuaWM2T+G+0/u06tWLya3noxzEeeseUAukqxPZuu1regCdOy4vgO9QY9nGU9+6PgD77u9T4l8JcwdosXKTFL4CPAAbkgpE4QQxdC6kBQlx4uJ0eoULVkCVapo002bN8+ae0sp2Xl9J0P9h3Ip8hJNnZqyuedmGpQ1Qg2MHExKyal7p54VoYtOiqZMwTJ83ehrfDx8cC3pau4Qs4XMJIXGwHkp5RMhRG+0geZMlblQlOxo0yZtVfKDBzB8OIwdC3mzaEJKQEQAQ/yHsOfGHioXq8yGHht4q/pbqm87E8Jiw54VoQt6GIS9jT3dqnfDx92HthXbYm2VxSsGc7jMJIUFaBvuuAPDgCVoG+i0MEZgimJu9+9rq5B//RXc3bXqpvWyaAbovbh7fLPvG5adX0bRvEWZ22EuAzwHqP7tDEpITWDDlQ34Bfix58YeJJKmTk1Z3Hkx77q+q8ZfXkNmkoJeSimFEF2BuVLKJUIIH2MFpijmIiWsXKlVNI2Ph0mTtL0PbLNg0k98Sjwzjs1g+rHp6A16fBv7Mrr56GxXSdMcDNLAkdtH0J3Xse7yOuJS4nAu4sy3zb+lr3tfKhWrZO4Qc4TMJIW49H2U+wDNhBDWaHssKEqOcfs2DBgAO3ZA48baGEKNGq9/3zRDGr+c/4Vv939LRHwEPWr2YGqbqbgUdXn9m+dwN6JvPCtCdzPmJgXsCvCu67v4uPvQrEKzHFGEzpJkJim8B/RCW68QIYRw4tV3X1MUi2IwwE8/aWMGBoO25mDQoKwpYLfr+i6G+A8h8EEgTco34ff3fqdRuUavf+McLDY5lnWX1qEL0HH49mEEgjYV2zCh1QS6Ve9Gfrv85g4xx8pwUkhPBCuB+kKITsBJKaWf8UJTFNO4dk3bJ/nwYfD21rbIdHZ+/ftevH+Rof5D2RWyi4pFK7Lu3XV0r9FdDSL/gzRDGntv7kUXoOP3K7+TqE+kWvFqTGk9hd61e1O+cPmX30R5bZkpc9EDrWVwAG0h2w9CiKFSyt+MFJuiGJVeDzNn/m820S+/gI/P65eoCI8LZ8z+MSw9v5TCeQozq90sPq//OXls8mRN4DnMlcgr6AJ0rLiwgrtxdylqX5R+Hv3wcfehQdkGKok+JyY+iYGzdrJ1bwz169qxZ3avLH9GZrqPRgP1pZQPAIQQJYE9gEoKSrYTEKBVMD17Ft5+G+bNAweH17vnk5QnzDw+k++Pfk9KWgpfNvySb5p/Q7G8xbIm6BzkUcIj1gSuQReg49S9U1gLazpW6cicDnPoXLWzSqDPWbLtHFN+OUfopVIYQr0g6S0QaZxiuVGel5mkYPU0IaR7BKgRHiVbSUrSZhNNm6bVKfrtN+je/fXumWZIwy/Aj2/2f8O9uHu84/oOU9tMVbNh/iI1LZUd13egC9Cx5eoWUg2puJd2Z1a7WfSq1YvSBUqbO0SLcP1uFJ/N2MXR42kkhtSFh3WAOlAwDNtqe3Crk8CM/zSndd1+Rnl+ZpLCTiHELmB1+s/vAduzPiRFMY5jx7QCdkFBWjfRrFlQ7DU/xO+5sYchu4cQcD+ARuUase7ddTQp3yRrAs4BpJSciziH7ryOVYGreJjwkFL5SzGowSB83H1wd3A3d4hmp9cbGKs7wNL1N7l/1QV5uzHoe4J1EsLpOCU9j9PvrQpM/qg1NjbvGj2ezAw0DxVCdAe80MYUFkkpfzdaZIqSReLjYdQo+PFHcHKCnTuhffvXu+elB5cY6j+UHdd34FzEmTXd19CjZg/V/50uPC6clRdXogvQEfggEDtrO7pU64KPuw/tK7XP9ZVeDwXcYvDcA1w8m4/Um40gtrV2ongQ9nU30LiRYIFvO6o5tULb4NJ0MrWRq5RyPbDeSLEoSpbbvRs++URbfzBokLbvQYHX2FL3fvx9xh4Yy+KziyloV5AZ3jMY1GCQ6gMHkvRJbArahC5Ax66QXRikgYZlGzL/jfm85/Zerh5biU9IYdDcnWza/YiY665w1xOkD+SJwcr5KBXa+jOsb20GdPUEqps11pcmBSFEHCBfdAqQUkq1KaxicaKj4euvYdkyqFZNm27q5fXq90tITWDW8VlMOzqNJH0Sg+oPYkyLMRTPl4UbKGRDUkqOhx1Hd17H2ktreZz8mHKFyjHcazh93ftSvYR53+DMyW93AJOWnOHGxZKkhTaBxC6AAcqcoVCz5bzRujALfDtSpMCb5g71TzKyR3NBUwSiKFllwwYYOBAiI7Vuo2+/BXv7V7uXQRpYcWEFo/eNJiw2jLdrvM3UNlOpUrxK1gadzdyKucXyC8vxC/AjOCqYfLb5eLvG2/i4+9DKuVWuLEIXGhHD5zN3cfBICgkhdSDSHXCHAvewqXIAV484pn3elA4N6wP1zR3uP8pU95GiWLKICK2LaP16qFNHK1Xh4fHq99t/cz++u305F3GO+mXqs+rtVTSr0CzrAs5m4lPiWX95PboAHftD9wPQokILRjYdyTuu71AwT+76/KjXG5i84jALf7tORFAF5K0moH8PrJMR5Y9Tos4S+nR1YtonbbCxec0pbiakkoKS7UkJfn4weDAkJGgb3/j6vnoBuyuRVxi2Zxhbr22lQuEKrHp7Fe+5vZcra+wYpIEDoQfQBehYf3k9T1KfUKloJca3HE+f2n1yXe2m45fu8OWc/QScyUPKjYbwuAXQAopdw77ORho2kswb7E1Nl5ZAS/MG+4pUUlCytdBQ+PRTbUC5aVP4+WdtDOFVPHjygHEHxrHozCLy2+VnWttpfNHwC+xtXrHvKRsLfhSMLkDH8gvLuf34NoXyFKKnW098PHzwKu+Va2ZZJaXoGTRnJxt2RhJ9vRqENQDZF+xisXI+SvnWexnapzYDu9UHqpo73CyhkoKSLRkM2irkkSO1shQ//qhthGP1Ch/mE1MTmXNiDt8d+Y6E1AQ+8/yMMS3GUDJ/yawP3ILFJMWwNnAtugAdx8OOYyWs8K7ozdQ2U3mr+lvktc2inYUs3Jp9gYxddJKQwOKk3fCCxE7aCcezFPBaSYfWBVgwuCMlinQ0b6BGYrSkIIRYCnQCHkgp3dKPFQPWAs5AKNBDShkttI8dc4E3gASgn5TyrLFiU7K3oCCtgN3Ro9p6g4ULoUKFzN/HIA2suriKUXtHcSf2Dl2rdWVa22lUK/GKTY1sSG/QsztkN7oAHZuCNpGcloxrSVemtZ1G79q9KVOwjLlDNLqwB7F8Nmsn+w8l8yTEHR7UBtwgfwQ2lQ9TvXYM3w3yolOjumgbTuZsxmwpLAN+RNud7akRwF4p5VQhxIj0n4cDHYEq6V8N0XZ5a2jE2JRsKDUVpk+H8eMhf37Q6aBPn1crYHcw9CC+u305E36Geo718OvmR0vnllkes6W6eP8iugAdKy+uJCI+guJ5i9O/bn98PHyo51gvR3cP6fUGZqw9zo9rgrgX5KQNEKf2AKsURPkTFPdeSq9OZZk2oA32dt3MHa7JGS0pSCkPCSGc/3K4K/8bfdGhVVwdnn7cT0opgRNCiCJCCEcpZbix4lOyl7NntRIV58/Du+/CDz9A6VcolXP14VWG7xnOpqubKF+oPMu7LadXrV65YhA58kkkqy6uQheg41zEOWysbHizypv4uPvwZtU3c/RWoGeDwxk0cw9nTtmScqMBxHgBXlD0OnncN1O/fhrzfL2pXak50Nzc4ZqVqccUSj99o5dShgshSqUfLwvcee66sPRjf0sKQohPgE8AnJycjButYnaJiTBhgtZCKFlSW4PQ7RU+vEU+iWTCwQn8dOYn8trkZXLryQxuNDjH95OnpKWw9dpWdAE6tgdvR2/QU9exLnM7zKWnW88cO26SlKLn63m7Wbstgqjg9AFiQx+wjcfK+Qhlm+/nyw9c8e3RGKhs7nAtiqUMNL+orfqiVdRIKRcBiwA8PT1feI2SMxw5orUOrl3TylzPmAFFi2buHkn6JP77x3+ZfHgyT1Ke8Em9TxjXchyl8pd6+YuzKSklp++dRhegY3XgaqISo3Ao4MBXDb/Cx8MHt1Ju5g7RKDYdCWLUguNcDShK2s0mkPCGdqJ0AAUar6Zti3ws8O2AQ7EO5g3Uwpk6Kdx/2i0khHAEnpbiDgOe31apHHDPxLEpFiIuTptVNG+etgOavz+0bZu5exikgbWBaxm5dyS3Ht+iU9VOfN/2e2qUzIINly3U3di7rLiwAl2AjisPr5DHOg9vVX8LH3cfvCt5Y2NlKZ8Bs0ZEVDyfz9qJ/4EE4kNqQ4QHUB3yRWLtcpSqtaOZ8GlD3mmRvrJYyRBT/y3ZDPgAU9P/u+m544OEEGvQBpgfq/GE3GnHDm3dQVgYfPUVTJyY+QJ2h28dxne3L6funaKOQx2Wdl1Ka5fWxgnYzBJSE9gYtBFdgI49N/ZgkAaalG/Cwk4L6VGzB0Xsi5g7xCw1Z90JZq+8RNjlshhueUHKO2CVCuVOUqzNUnp0dGD2f9phb/eWuUPNtow5JXU12qByCSFEGDAWLRn8KoT4CLgNPC0Ovh1tOup1tCmp/2esuBTL9OiRtiJ5+XKoUUObbtq4cebuEfwomOF7hvN70O+ULVgW3Vs6etfuneMGkaWUHLl9BF2AjnWX1xGbHItTYSdGNR1FX/e+Oaou04WQ+wyc6c+pU9Yk3/CEqEZAIyhyEzu3bdT1TOW/g1tTv3r6wLHy2oQ24Sd78vT0lKdPnzZ3GMprkFLb/WzQIIiK0rqNRo+GPJmoRP0o4RETDk5g/un52NvYM8JrBIMbDyafbT7jBW4GN6Nv4hfgh98FP25E3yC/bX7ecX0HH3cfWji3yBHJT6834Dvfn1Vb7/LoWmXknUZgsAPbJ4gKx3CsfpvPe1RleE8vbGyy/+9rLkKIM1JKzxedy1mdjEq2cu+eVs1040aoV08rVeGeia7fZH0yP578kUmHJxGbHEv/uv0Z13IcDgVec7NlCxKXHMe6y+vQBeg4dOsQAkErl1aMbTGWt2u8TQG719gcwkJsPXGNkT8eJehCYfQ3G0N8+g5IpS6Qv+FaWjSzY6FvR8qV8jZvoLmESgqKyUkJS5dqReuSk+H777WuI5sM/m2UUrLu8jpG7BnBzZibdKzckene06lZqqZxAzeRNEMa+27uQxegY8OVDSTqE6lSrAqTWk2ij3sfnApn76nYD2MS+Gz2DnbtiycuxA3C6wBVIe9DrF2OUsntEWM/rk8v79pAbXOHm+uopKCY1I0b2k5oe/dC8+ZaAbsqmegCP3bnGL67fTkRdoLapWuzu/duvCvljE+QQQ+D0J3XseLiCsJiwyicpzB93fvi4+5Do3KNsvUq43m/n2L68gvcuVQGw60mkNwdhB7KnqJIKx3dO5RkzqB2FMjX1dyh5noqKSgmkZamrUIePRqsrWH+fG2WUUYL2IVEhTBi7wh+u/wbjgUcWdJlCT7uPtl+M5eoxCjWBK5BF6Dj5N2TWAtr2lduz8x2M+lSrUu2rdB66eYDBs72548TgqQQT4hK31im8C1sa+zCvV4Ss79sSdNajYFMzihQjEolBcXoLl/WFqGdOAFvvAE//QTly7/8daC9aU46NIkfT/6InbUd41uOx7exL/nt8hs3aCNKTUtlV8gulp1fxpZrW0hJS6FWqVrM8J7BB7U/yJZjInq9gZGL96LbdJuH1yohbzeGtA/AJgHhdByHxof59J3KjO7dDBubV6heqJiMSgqK0aSkwLRp2lqDQoVgxQro1StjBeyS9cnMPzWfiYcm8jj5MR96fMiEVhNwLOho/MCN5HzEeXTndawKXMWDJw8oma8kn3l+ho+7Dx4OHtmue8j/VAhD5x3m8rkCpN5sDHHp3XglL5HXcx3Nm9rw05AOODu0MW+gSqaopKAYxalTWuvg4kV4/32YOxdKZaCyhJSS9VfWM3zPcG5E36B9pfZM955OrdK1jB+0EdyPv8/KiyvRBei4cP8Ctla2dK7WGR93HzpW7oit9StuD2cGMfFJDJy1k617Y4gNcYW7nkAlsI/C2vkoLrV2MfrDuvTr4AHkjEH/3EglBSVLJSTAuHEwcyY4OMCmTdClS8Zee/zOcXx3+3I87Di1StVi5wc7aV+5vVHjNYZkfTJbrm1BF6BjR/AO0mQa9cvU58eOP/K+2/sUz1fc3CFm2KKtZ5j6SwC3LpfGcNMLkt8CkQZlzlC4hR9dvIsyf3BHCuTrbO5Qc43A+4FsuroJ15KudKuR9aW9VVJQsszBg9rmN9evQ//+2lTTIhmosnAj+gYj947k10u/4lDAgcWdF/N/Hv+XrQaRpZT8cfcPdOd1rL20luikaMoULMOQJkPo694X15Ku5g4xQ67fjWLAjJ0cOyZJvFEXHtYD6kHBMGyr+1OrbgLTBzWndd0GQANzh5ujJemT2B2yG/8Qf07ePUlIdAjRSdEYpAGAug51VVJQLFNsLAwfrg0gV6yoTTdtnYFSQ9GJ0Uw+PJkfTv6AtbBmTPMxDPUamq0WZN15fIflF5bjF+DH1UdXyWuTl241uuHj7kMblzYWn9j0egNjlh5g6e83eXDNRRsg1vcC6yREhWOU8jzGh91cmPBhS2xs3n3p/ZRXExYbxuarm9kfup+L9y9yJ/YOCakJf7omr01eXIq4UKtULZpVaEa36sbZAEglBeW1bNsGAwZoq5O//lobVM73kuoSKWkpLDi1gAmHJhCdGE0/j35MbDWRsoXKmibo1/Qk5Qm/B/2OLkDH3ht7kUiaOTVjaJOhvFvzXQrlKWTuEP/VoYBbDJ57gItn85F6sxHEpmfwElfIW3cDTZoI5g1uRzWnnFlE0JwMBgPHw46zLXgbJ8JOEPQwiMiESPQG/bNrBIIi9kWoUaIGdR3r0q5SOzpU7mCyD0sqKSiv5OFDrYrpypVQs6ZWv6jhSzZQlVKy4coGhu8ZTkh0CG0rtmWG9wzcHSy/rLFBGjh86/CzInTxKfG4FHFhTIsx9HXvS8WiFc0d4j+KT0jh89k72LInipjr6QPE0gfyxGDlcpQKbf0Z8X/ufNKpHpBzS4ubWmxSLFuCt7D3xl7Ohp8lNCaU2ORY5HNbxdha2eJQwIHqxavjVd6LN6u+ST3HelhldAGPEaikoGSKlLB2LfznP/D4MYwdC6NGgd1LdnI8efckvrt9OXL7CK4lXdnWaxsdK3e0+GmYIVEhz4rQhcaEUtCuID1ce+Dj4UNTp6YWW4TOb3cAE38+w83AkqTd9IKkroABypyhULPlvNG6MAt8O1KkwJvmDjVHuBJ5hS3XtnDo1iEuR17mXtw9ktOS/3RNAbsCVC9RHQ8HD1o6t6RLtS4WuSZFJQUlw+7ehc8/h82boX59WLIEar1kpmhoTCgj945kTeAaSuUvxcJOC/mwzocWveHL46THz4rQHbl9BIGgbcW2TGo1iW41ullk9dXQiBgGzNjJ4aOpJITUgcj0jWUK3MOm6n5cPeKYMagZ3vXTVxYrryRFn8Lem3vZeX0np+6d4nrUdR4lPno2+AtgJawonrc4VYpVoUHZBnSo3IFWzq2ws8kee2Bb7r9MxWJIqSUAX19ITdW2xfzqK61cxT+JSYphyuEpzP1jLtbCmm+afcMwr2EUzFPQdIFnQpohjT039qAL0PF70O8k6ZOoXqI637X5jt61e1OuUDlzh/gner2BySsOs/C360QEVUDeagL698E6GVH+OCXqLMHnLSe+698GG5vu5g43W4qIj2BT0CYOhB7g/P3z3Hl8hyepT/50jb21Pc6FnXEt6UrzCs3pUq0L1UpUM1PEWUMlBeVfhYRo00v374eWLWHxYqj8L/ucp6al8tPpnxh/cDxRiVH0ce/D5NaTLe5N9alLDy6hC9Cx4sIKwuPDKWpflA89PsTHw4f6ZepbVPfW8Ut3+HLOfgLO5CHlRkN43AJoAcWuYV9nIw0bSeYN9qamS0u0/a2UjDAYDJy6d4ptwds4ducYQQ+DePDkAamG1GfXCASF7QvjUdyDeo71aOPShjervEkhe8ueVPAqVFJQXigtTVuF/M03YGsLCxdqaxD+afxLSsmmq5sY5j+M4KhgWjm3Yka7GdR1rGvawDPgUcIjVgeuRheg4/S901gLa96o8gY+7j50qtqJPDaZ2OHHiJJS9Ayas5MNOyOJvl4NwhqA7At2sVg5H6V8670M7VObgd3qA1XNHW62EJ8Sz/bg7fiH+HM2/Cw3Ym7wOOnxnwZ/baxsKJW/FNWLV6dR+UZ0qtKJhmUbmnXw15RUUlD+JjBQK1Fx8iR06gQLFkC5f/mgf/reaXx3+3Lo1iGql6jOlp5beLPKmxb1KTslLYUdwTvQBejYem0rqYZUPBw8mN1+Nr1q9aJU/gzU4DCBNfsCGbvoJCGBxUm74QWJnbQTjmcp4LWSDq0LsGBwR0oU6WjeQLOB4EfBbL66mcO3D3PxwUXuxd0jSZ/0p2vy2+anWvFq1Cpdi1bOrehcrbPFtmpNRSUF5ZmUFPjuO5g8GQoXhtWr4b33/rmA3a2YW4zeN5qVF1dSMl9J5r8xn/71+lvMILKUknMR554VoXuY8JDS+Uvznwb/oa97X4uYChv2IJbPZu1k/6FknoS4w4PagBvkj8Cm8mGq147hu0FedGpUF7C8Vpcl0Bv07L+5n50hO/kj7A+Co4J5lPCINJn27BorYUWxvMVwL+1Og7INaFexHW0rtc22pcmNyTL+9Spmd/Kk1joIDNQqmc6dCyVKvPja2ORYvjv8HbNPzEYIwcimIxnRdITFLNoKjwt/VoQu8EEgdtZ2dK3WFR93H9pXbm/WpKXXG5ix9jg/rgniXpCTNkCc2gOsUhDl/6C491J6dSrLtAFtsLczzorV7OxhwkM2BW1if+h+zkec5/bj28SlxP3pmjzWeShXqBw1S9akqVNTOlftjFtpNzNFnP2opJDLJSTAmDEwezY4OsKWLVqX0YukpqWy+Oxixh0YR2RCJL1r92Zy68kWsT1kkj6JTUGb0AXo2BWyC4M00KhcIxa8uYD3ar5H0bxFzRbb2eBwBs3cw5lTtqTcaAAxXoAXFL1OHvfN1K+fxtzBbahbpRnQzGxxWhKDwcD5++fZem0rR28f5crDK9yPv0+KIeVP1xXKU4japWtTx6EObV3a0qlaJ4rYZ6DglvKPVFLIxfbv1waPn26R+f33WrfRX0kp2XptK8P2DCPoYRAtKrRgZruZ1CtTz/RB/yWu42HHnxWhe5z8mHKFyjHcazg+7j5mmxqYlKLn63m7Wbstgqjg9AFiQx+wjUc4H6Vc8/18+YErvj0aA/8ylSuXSEhJYFfILnaH7Ob0vdPciLlBdGL03wZ/S+QrQbXi1WhYtiEdq3SkuVPzXDP4a0oqKeRCjx/DsGGwaBFUqvS/6aYvcjb8LEN2D2F/6H6qFq/Kxvc20qVaF7MOIt+KufWsCF1wVDD5bPPRvUZ3+rr3pZVzK7MUodtw6Arf/HSCaxeKknazCSS8oZ0oHUD+RqvxbpmPBb4dcCiW/UqBZ6VbMbfYGLSRQ7cOcfHBRcJiw0jUJ/7pmrw2ealcrDK1StV6tvK3QhG1W5upqKSQy2zZohWwi4iAoUO1vQ9eVMDuzuM7jN43muUXllM8b3F+6PgDn9b71GybwsSnxLP+8np0ATr2h+4HoKVzS0Y1G0X3Gt1NviguIiqez2buZM/BBOJDakOEB1AD8kVi7XKUqrWjmfBpQ95pkb6yOJcxGAwcun2IHcE7OBF2gmtR14h8EvmnwV+BoGjeotQsWZP6ZevjXdGb9pXak8/O8laM5yYqKeQSkZHwxRewZo1WmmLTJvD0/Pt1cclxTD0ylVknZiGlZFiTYYxqNorC9i/oVzIygzRwIPQAugAd6y+v50nqEyoVrcSElhPoXbs3LkVdTBrPzF+PM3flZe5eKYvhlhekvANWqVD2JMXaLKVHRwdm/6cd9nZvmTQuc4tKiGJr8Fb23tjLuYhz3Hp8i9jk2D9dY2dtR5mCZXAt6UqT8k3oVLUTHqU9VPePBVJJIYeTUpta+sUX2r4HEyZoex/8tYCd3qDn57M/M/bAWB48eUBPt55MaTMF5yLOJo85+FEwugAdyy8s5/bj2xTKU4iebj3p59GPJuWbmKzr6kLIfQbO9OfUKWuSb3hCVGOgMRS5iV3N7dStn8J/B7emfvX0geNc4OmuX0fvHOVy5GUi4iP+VvitoF1B3Eq64e7gThuXNnSu1pkS+f5hKpticcySFIQQoUAckAbopZSeQohiwFrAGQgFekgpo80RX05x5w589pm250HDhlr9opp/2TpXSsn24O0M9R/KlYdXaOrUlC09t9CgrGl31YpJimFt4Fp0ATqOhx3HSljhXdGbqW2m8lb1t8hrm9foMej1Bnzn+7Nq610eXauMvNMIDL3B9gmiwjEcmxzi8x5VGd7TCxsb07ZSTO1lu34BWAtrSuQrQZViVWhUrhEdKneghXMLi1mnorwac/7ptZJSPnzu5xHAXinlVCHEiPSfh5sntOzNYNAGkYcN08pVzJ6tlbr+awG78xHnGbJ7CHtv7qVyscps6LGBt6q/ZbJP4nqDnt0hu9EF6NgUtInktGRcS7oyre00etfuTZmCZYwew9YT1xj541GCLhRGf7MxxKcPBJe6QP6Ga2nRzI6Fvv/f3p2HVVWujR//PiKoqInijKI4gKICKk6YI85alJn5lkp1fqmZDaaeU9bxHE/Wq6V57M3sdMrammapWWZOGJpjzm7FGcQRBZVQERn38/tjLYkMN90JpQAAGYNJREFUSJQ9KPfnurjYe+29Wfd+GG7W86x1332pU72n3WNxljvp+hUREEHDKg2dFLGwJ1dK6RH8VsXLAmxAkkKRHT9uFLD7+WcIDzeSQ4Nb+r+cu3qON9e/iWWfhcrlKjOrzyxGhY7Cw80xpX0PJB7AYrWw4MACLqRewLucN8+1eo7IkEha12pt16R0KSWN52euYk10KtfimsP5loA/lLuEm98WGja/zD/+Xxue7BkEBNktDme4F7p+CedzVlLQwFqllAb+o7X+BKihtT4PoLU+r5RyjWI094jsbOOIYNIkKFPGmCp65pnfl6hIzUzl3S3vMn3rdHJ0DuM6jGNip4kOubDr4vWLLDywEIvVwt4LeyldqjT9G/cnMjiS/v797ZqQZi/byXvz93PmYG1sp8Ig4zFQ2eCzE69uFh7tVZUPXupNBc8Iu8XgaPdq1y/hfM5KCh211gnmH/4opdSR232hUmoEMALA19f5V9K6AqvVKFGxezdERMBHH0HtPDMvObYc5u6dy9/X/53E64k80ewJ3gl/x+4tJDNzMllxbAUWq4WVx1eSbcumVa1WzOozi/9p/j9UK1/NLvs9GJ/ECzOj2P6LIj0uFJLNxjKVTuHedA3BrdOZ+XJXHmxhLhzf4+6nrl/C+ZySFLTWCebnJKXUMqAtkKiUqmUeJdQCkgp47SfAJwChoaE6v+eUFBkZMGUKTJ0KlSvDN9/AoEG/PzpYHbuaCVETiEmKIaxuGN8N+Y72ddrbLSatNbsSdmGxWvgq5iuSbyRTs0JNXmn3CpEhkTSvXvw1aLKzbbz+35+wfHeaS8caos90gJynoHQayncbNTtsYuSgRrwxtBOlS9+7F0GVhK5fwvkcnhSUUuWBUlrra+btXsC/gOVAJDDV/Py9o2O7l2zbZhwdHD4Mw4YZU0fe3r89vj9xPxOiJrA2bi0NKjdg8eOLeazpY3abrz939Rxf7v+SefvncejiIcq4leGRJo8QGRxJz4Y9i/2MlKidcYz/cBOH9lUkO749XDMXgqvF4NnmGzp1dOfj8X2oXzO8WPfrKCW165dwPmccKdQAlpl/nEoDC7XWq5VSO4FvlFJ/AU4DjzshNpd3/brR+GbWLKPHwcqV0DdPaf2EawlMWj+JuXvn4lXWi/d7vc/oNqPt0jgmLSuN7458h8VqYd2Jddi0jbC6YfxnwH8Y3GxwsRYmS0lN5/kZq1gZfYWrsc0goTXQEMom4+a3Bb/ma3jj2VY83ScEuHcqYkrXL+FqlNb37gxMaGio3rVrl7PDcJh164wzi06ehBdeMHofVDSrO1zPvM70rdN5d+u7ZOVkMabtGN7s/CZVylUp1hi01mw5swXLPgvfHPqGqxlX8a3ky/Cg4QwPHk5j78bFtq9PVuxm6udWTh2qgS2+I2R4gcoBn114NTrEQz2q8NHYvlTwvDemRqTrl3AVSqndWut8ahq41impogC//grjx8PcudC4MWzcCJ3MCss5thwsVgtvRr/J+dTzDAocxNTwqcV+DvnJlJPMs85jnnUecb/GUd69PIMCBxEZHEmX+l0ope7+j1bsuWRGTV/N1q2aGydawaXWQGt44AzuTaJo0SqNmS93pXNwO6DdXe/PnqTrl7hXSVJwccuWwejRRu2i114zTjktZ17cGxUXxfio8exP3E/7Ou1ZMngJYXXDim3f1zKuseTQEixWCz+f+hmFoptfNyZ1mcTApgPv+tz17Gwbk+ZuYO6yeJKO+aFPd4DsJ8EtHVVvK9VDt/KXQX5MjuxK6dKuOZsoXb/E/UaSgotKTDSuQl68GIKDYcUKaG22L4hJimFC1ARWx66mvld9Fj22iMHNBhfLInKOLYfo+Gjm7Z/Ht4e/JS0rjcZVGjOl2xSGBQ+764Y6G62nGDtrAwf2eJIV3x6udjceqHqYcq2+JSxMMXtsLwJ8u9/1eyluN7t+RcdHY020StcvcV+SpOBitIb58+GVV4xF5bffNkpcu7sbZ6RMWj+Jz/Z+RkWPirzX8z1ebPtisSwiH7l0BMs+C18e+JKzV89SqUwlhgUNIzI4kvZ12t9xwklNy2T0zFX8sC6ZlNhAOBcKOhLKpFDKbwv1ekTx2jPBjBjQGmh61++jONzs+vXD0R/YemardP0SJYokBRdy+jSMHAmrV0NYmHFVcpMmxlk+U3+ewbQt08jIyeDFti/y985/x9vT+8+/aCGSbyTnFqHbfm47bsqN3o16M6PXDB4OePiOpzfmrbXy1qe7iT9QjZyTHSE9ArBB7d080Gk+/bpXYs64vnhV6H9X8ReHtMw0VsWuYt2JdexM2El8Sny+Xb+qeVbD39ufDnU60LdRXx70fVAWf8V9SZKCC7DZYM4cY81Aa/jgA+PsIpSNL/bN443oN0i4lsDApgOZGj71rs7wycrJYk3cGixWC8uPLiczJ5MW1Vswved0ngp66o6ucj15IYVR01ezcXM2N060hItmY5kKCZT2X09gyDWmj+lEzzbmlcVOUpSuX0E1guhSr4t0/RIljiQFJzt61OiTvHkz9OxpFLCrXx+i46MZt3Yc+y7so03tNix6bBGd6t15U3frBWtuEbqk60lU9azK86HPExkcSUjNkCJND2Vn23j7y018vDiWxCP1zQXiIeCWgaq7jaqtPiMywpf/fS6c0qUfu+OY75R0/RLizklScJKsLJgxw2iHWa4cfP45REbCkUuHGbBwAj8e/5F6leqxcOBCnmj+xB2d8pmYmphbhM6aaMW9lDsPBTxEZHAkfRv1LVJrzW0Hz/Dyv9ezb1dZsuLbwZUuQBeocoyyLb+jXXvN7LE9aebXld+K3dpfcloyPxz7gej46EK7fvk84EPTqk150PdBBjQeQFCNIJn+ESIfkhScYO9eo0TF3r0wcCDMng2qQiKjf/wn/93zX8p7lGdaj2m81O6lIs/rZ2Rn8MOxH7BYLaw6voocnUOb2m34sO+HDGk+5LbXIdIzsxnz79UsXX2RlONN4Fwb0MPB4yql6m+mbvd1TBgWxAuPtgH872AUiq4oXb9a1mxJN79u0vVLiCKSpOBA6enw1lswbRpUrQpLlkC/h28w85eZTN08lbSsNJ4PfZ5JXSYVqYKo1pod53ZgsVpYFLOIX9N/pXbF2owPG8/w4OEEVgu8ra+zKDqGf3yyg7gD3uTEd4QbA4wHau2mYscF9O5egTlj+1LVq9+dvP3bJl2/hHAe+Q1ykC1bjKODo0fh6afhvek2Vp1dgP+HEzl79SwRARFM6zGtSAXNzl49y3zrfCxWC0cvH6Vs6bIMbDqQyOBIwv3CcSvlVvjrk67y/PurWb8xg+txwZAUBDSH8hco3WgjTYKu8L9jOjKgvXllsR1I1y8hXIskBTtLTYWJE+HDD8HXF9asAQ//DfReOo495/fQulZr5j86n671u97W17ueeZ1lR5ZhsVr46cRPaDSdfDsxIWwCjzd7nAfKFFwkLTvbxvSvt/HhoiMkHPFFnwqDrMFQKhNV9xe8e87lyQE+TBsVTlmPgcUzACbp+iXEvUGSgh2tWQMjRsCZMzBmDESOO8K/tv2N5Zbl1H2gLvMfnc+TLZ7800Vkm7ax6dQmLFYLiw8tJjUzlfpe9ZnUZRLDgoYV+l/znuPnGTNjHXt2upNxoi2kdAQ6QuVYygQvp02bHGaP60lQw85A52J539L1S4h7lyQFO0hOhldfBYsFAgJg+U8XWX1jMu0sH+Pp7sk73d/hlfavUM69XKFfJy45zihCt38eJ1NOUsGjAoMDBxMZEmlcPJVPMknPzObV2Wv5+scLJB8PgLNtwTYM3FMpVX8zPp3X8/JTgYwb3AFodNfvVbp+CXF/kaRQzJYuNS48u3QJ/joxnYo9Z/HUL+9wPfM6I1uP5B9d/0H18gW3n76SfoXFhxZjsVrYfHozCkV4g3De6vYWjzZ5lPIe5f/wmu83H2HinG0ctVYm52QHuG4uBNfcR4UOX9GjiydzxvWhZpU+d/y+pOuXECWDJIVicv68MUX07bcQ0tLGy58u4uPY1zn982ke8n+IaT2m0bRa/rV9cmw5rDuxDovVwrIjy0jPTifAO4B3ur/D0KCh1K1U93fPv5Ccyuj3VxO1IY3UuCC4EAI0Ac8k3Py24h/0K1NGtWdg5xAgpMjvRbp+CVFySVK4S1ob00Rjx8KNGzDi7U3sqTqOibt30rJmSz6P+JzufvlX/Dx08VBuEbqEawlULluZZ0KeITI4krY+bX93lfGMb7Yxa8Ehzh32wXaqI2QOglJZUGcHVcLnMrhvTWa+2IuyHo/cduzS9UsIcStJCnfh5EljITkqClr3OkaVwa/xydll+KT6YHnEwtCgoX+Y97+cdpmvYr7CYrWwK2EXbsqNfo378UGfDxjgPyC34un+uERemBHFzp1uZJwIheQOQAfwOoFH8x9pFZrFB2O706aJuXD8J252/Vp3Yh27E3YTnxJPSnqKdP0SQvyOJIU7kJNjXIU8cSLgeYmu095ic8ZHlE0qy5RuUxjbYSye7r/V0MnKyWLl8ZVYrBZWHFtBli2L4BrBvN/rfZ5s8SQ1KtQgO9vGuI+iWLjiHJePNUKfaQ+2oeB+HVVvK7XCNvLikCaMf6IDpUs3KDS+uOQ4vj/6PRtPbSQmKYZz1879oeuXp7sn/t7+BNUIyl38la5fQghJCkV0+LBRwG7r9gwChv8fCY2msDH9Gs+1eo7JXSdTo0INwLjKeO+FvVj2WVgYs5BLaZeoXr46Y9qOITI4kuCawaz45Rg9Rq3kyP5KZJ8Ig+u9jZ1U30/5dl/TrXMZ5rzahzrVe+YbS7Ytm59P/szK2JXsOLuDY8nHpOuXEOKuSFK4TVlZ8O67MPlfGo+W31Bt8usczY6nX/1+vNfzvdxSEuevnWfBgQVYrBZikmLwcPPg4YCHiQyOpLV3J176YB2dpuzjWlwOnA8B/KHcJdz8ttCwxWUmj2jLkO5BQNDv9n+z69f6k+vZd2FfgV2/6laqS2DVQDrV68RD/g/RrHozB42QEOJ+IEnhNuzZA88+C9bkLVR5ZRzJnttp5B3Mwl5R9GjQg/Ts9NxmNWvi1mDTNtr5tOOjfh9x5UQj5iw8zbdvumE7BWQ8BiobfHZSuZuFgX2q8e8xvajgGQEYi797zu9hxbEVbDm95U+7frWq1Yrw+uHS9UsIUSwkKRTixg2YPBne+ywO975/g4ZLKVuxNp93/5yhLYayI2EHI38YydcHv+ZKxhXqPFCHZ5uNYd+mOlh/9GF0XCgkmxVEK53EI3A1wa0zeP+lrjzYogNpmcGsiVvDhOiX2ZWwixMpJwrs+hXgHUD7Ou3p79+fsDphsvgrhLALSQoF2LQJnhmdTJzPW5R6YTalPTx4o+O/GBQ4iKWHlxL4USDHk4/j6e5JvVId4GAwZ3f149PTD0JOGSidhqq3lZodNjFyUCOGDvBlRewFNp7ayLMbpnB2+R+7fnm6e9LYuzEtqrfIXfz1reTrpBEQQpRESmv9589yUaGhoXrXrl3F+jWvXoW/vp7Bf/bOplTXt6DMVYYHDyekZgjfHzXm9AHK3wjgxqFwbFtegmTzoq1qMZRtuJvmnWJpHXaNw7/uLbTrV8PKDWnj04ZeDXrRu1FvWfwVQjiEUmq31jo038ckKfxm5UrN8KlLuNzyNahygtY12lC7Uk2i46O5nnUdlVYdfegR2PxXSGkIXrGo9rMpH7Adr9oXuWpLyrfrV80KNQmsGkhH3448HPAwQTWCCohACCHsr7CkINNHwOXLMPS1bazW4yB8G17u1cjRldmduJPdZzzh4OMQ3x1d6SQ0Xo0a2Q5V7go2stFAKpB6w+j61aJ6C0JqhBDeIJz+/v2l65cQ4p7icklBKdUHmAW4AZ9qrafaa19aw4cLTjB+7TgyG34HNmM4UjIvwZW6kFMFPC9DyDxoacl9XSmz65e/t39u16/O9TpL1y8hxD3Ppf6KKaXcgNlAT+AssFMptVxrfai497X3aCIdZzzNjVprocHNSp85oAGlwes0YHT98nmgIS2qt6Bzvc5EBETgV9mvuMMRQgiX4FJJAWgLxGqtTwAopRYBEUCxJgXP0d25UX0D+GiwuXHzDFA3ytG0mj8dfdvRs2FP+jbqi6eHZ6FfSwgh7ieulhR8gDN57p8F2uV9glJqBDACwNf3zk7X9PPy5VC6F6Vu1KBPYEcmPzSKVjVbybn/QogSz9WSgspn2+9Oj9JafwJ8AsbZR3eyk4PvfHEnLxNCiPueq/1rfBbI21GmDpDgpFiEEKLEcbWksBNorJTyU0p5AEOA5U6OSQghSgyXmj7SWmcrpcYAazBOSZ2rtT7o5LCEEKLEcKmkAKC1XgmsdHYcQghRErna9JEQQggnkqQghBAilyQFIYQQuSQpCCGEyHVPl85WSl0ETt3hy6sCl4oxnOLiqnGB68YmcRWNxFU092Nc9bTW1fJ74J5OCndDKbWroHrizuSqcYHrxiZxFY3EVTQlLS6ZPhJCCJFLkoIQQohcJTkpfOLsAArgqnGB68YmcRWNxFU0JSquErumIIQQ4o9K8pGCEEKIW0hSEEIIkatEJgWlVB+l1FGlVKxS6jUnx3JSKXVAKbVPKbXL3FZFKRWllDpufq7sgDjmKqWSlFIxebblG4cyfGCO336lVCsHx/VPpdQ5c8z2KaX65XnsdTOuo0qp3naMq65Sar1S6rBS6qBS6mVzu1PHrJC4nDpmSqmySqkdSimrGddkc7ufUmq7OV5fmyXzUUqVMe/Hmo/Xd3BcXyil4vOMV4i53WE/++b+3JRSe5VSK8z79h8vrXWJ+sAoyR0HNAA8ACsQ6MR4TgJVb9n2LvCaefs1YJoD4ugMtAJi/iwOoB+wCqNTXntgu4Pj+icwPp/nBprfzzKAn/l9drNTXLWAVubtisAxc/9OHbNC4nLqmJnvu4J52x3Ybo7DN8AQc/vHwPPm7dHAx+btIcDXdhqvguL6AhiUz/Md9rNv7u9VYCGwwrxv9/EqiUcKbYFYrfUJrXUmsAiIcHJMt4oALOZtC/CIvXeotd4IJN9mHBHAPG34BfBSStVyYFwFiQAWaa0ztNbxQCzG99secZ3XWu8xb18DDmP0GHfqmBUSV0EcMmbm+04177qbHxroDiwxt986XjfHcQkQrpTKr12vveIqiMN+9pVSdYD+wKfmfYUDxqskJgUf4Eye+2cp/JfG3jSwVim1Wyk1wtxWQ2t9HoxfcqC6k2IrKA5XGMMx5uH73DzTa06JyzxUb4nxX6bLjNktcYGTx8ycCtkHJAFRGEclKVrr7Hz2nRuX+fgVwNsRcWmtb47X2+Z4zVRKlbk1rnxiLm7/Bv4K2Mz73jhgvEpiUsgvezrzvNyOWutWQF/gBaVUZyfGcrucPYZzgIZACHAemGFud3hcSqkKwFLgFa311cKems82u8WWT1xOHzOtdY7WOgSj93pboGkh+3ZaXEqp5sDrQBOgDVAF+Jsj41JKDQCStNa7824uZN/FFldJTApngbp57tcBEpwUC1rrBPNzErAM45cl8eYhqfk5yUnhFRSHU8dQa51o/iLbgP/y23SHQ+NSSrlj/OFdoLX+1tzs9DHLLy5XGTMzlhRgA8acvJdS6mYHyLz7zo3LfLwStz+NeLdx9TGn4bTWOgP4HMePV0fgYaXUSYwp7u4YRw52H6+SmBR2Ao3NVXwPjEWZ5c4IRClVXilV8eZtoBcQY8YTaT4tEvjeGfEVEsdyYLh5JkZ74MrNKRNHuGUO91GMMbsZ1xDzTAw/oDGww04xKOAz4LDW+v08Dzl1zAqKy9ljppSqppTyMm+XA3pgrHesBwaZT7t1vG6O4yAgWpurqA6I60iexK4w5u3zjpfdv49a69e11nW01vUx/kZFa62fwhHjZY8Vc1f/wDiD4BjGnOYbToyjAcaZH1bg4M1YMOYCfwKOm5+rOCCWrzCmFbIw/uv4S0FxYByqzjbH7wAQ6uC45pv73W/+MtTK8/w3zLiOAn3tGNeDGIfn+4F95kc/Z49ZIXE5dcyAIGCvuf8YYFKe34EdGAvci4Ey5vay5v1Y8/EGDo4r2hyvGOBLfjtDyWE/+3li7MpvZx/ZfbykzIUQQohcJXH6SAghRAEkKQghhMglSUEIIUQuSQpCCCFySVIQQgiRS5KCEHdIGZVHxzs7DiGKkyQFIYQQuSQpCFEESqk3lNF3YB0QYG57Tim106zJv1Qp5amUqmjW43c3n/OAMnpnuCulXlJKHTKLrS1y6hsS4haSFIS4TUqp1hglB1oCAzGKpQF8q7Vuo7UOxijd8BdtlK3egFH6GPN1S7XWWRh9FlpqrYOAUQ58C0L8KUkKQty+TsAyrXWaNiqP3qyZ1VwptUkpdQB4Cmhmbv8UeMa8/QxGYTUwSiosUEoNBW6WQRbCJUhSEKJo8qsL8wUwRmvdApiMUYcGrfUWoL5SqgtGN7ObRdX6Y9TPaQ3szlP1Ugink6QgxO3bCDyqlCpnVrd9yNxeEThvrh88dctr5mEU9fscQClVCqirtV6P0UDFC6jgiOCFuB1SEE+IIlBKvQEMB05hVG09BFzH+AN/CqNyZkWt9dPm82sC8RhVSVPMxLEeo969Ar7UWk919PsQoiCSFISwI6XUICBCaz3M2bEIcTtkLlMIO1FK/R9Gm9V+zo5FiNslRwpCCCFyyUKzEEKIXJIUhBBC5JKkIIQQIpckBSGEELkkKQghhMj1/wEGogLA8GeobAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0 = np.linspace(0,400,20)\n",
    "fig,ax = plt.subplots()\n",
    "c=1\n",
    "\n",
    "\n",
    "\n",
    "for index, row in historical_data[historical_data['Energy']=='Hydro'].iterrows():\n",
    "                \n",
    "    for gh_model in ['h08']: #matsiro does not have any data for soc='pressoc'\n",
    "        for cl_model in ['gswp3','princeton']:    \n",
    "            if historical_data['_'.join(['slope_if',gh_model,cl_model])][index] !='NA':\n",
    "                ax.plot(x0,x0*historical_data['_'.join(['slope_if',gh_model,cl_model])][index]*100,color='Green')             \n",
    "\n",
    "    \n",
    "    \n",
    "    for gh_model in ['lpjml']: #matsiro does not have any data for soc='pressoc'\n",
    "        for cl_model in ['gswp3','wfdei']:    \n",
    "            if historical_data['_'.join(['slope_if',gh_model,cl_model])][index] !='NA':\n",
    "                ax.plot(x0,x0*historical_data['_'.join(['slope_if',gh_model,cl_model])][index]*100,color='Blue')             \n",
    "\n",
    "                \n",
    "ax.plot(0,0,label='h08',color='Blue') \n",
    "ax.plot(0,0,label='lpjml',color='Green')                \n",
    "\n",
    "\n",
    "                \n",
    "ax.set_xlabel('days')\n",
    "ax.set_ylabel('loss(%)')\n",
    "ax.set_title('Hydro')\n",
    "ax.legend()\n",
    " #blue is lpjml, green h08, no noticable pattern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7274d69dd8>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0 = np.linspace(0,400,20)\n",
    "fig,ax = plt.subplots()\n",
    "for index, row in historical_data[historical_data['Energy']=='Nuclear'].iterrows():\n",
    "    for gh_model in ['lpjml']: #matsiro does not have any data for soc='pressoc'\n",
    "        for cl_model in ['gswp3','wfdei']:    \n",
    "            if historical_data['_'.join(['slope_if',gh_model,cl_model])][index] !='NA':\n",
    "                ax.plot(x0,x0*historical_data['_'.join(['slope_if',gh_model,cl_model])][index]*100, color ='Blue')\n",
    "                \n",
    "    for gh_model in ['h08']: #matsiro does not have any data for soc='pressoc'\n",
    "        for cl_model in ['gswp3','princeton']:    \n",
    "            if historical_data['_'.join(['slope_if',gh_model,cl_model])][index] !='NA':\n",
    "                ax.plot(x0,x0*historical_data['_'.join(['slope_if',gh_model,cl_model])][index]*100, color='Green')             \n",
    "\n",
    "\n",
    "                \n",
    "ax.plot(0,0,label='h08',color='Blue') \n",
    "ax.plot(0,0,label='lpjml',color='Green')                 \n",
    "                \n",
    "ax.set_xlabel('days')\n",
    "ax.set_ylabel('loss(%)')\n",
    "ax.set_title('Nuclear')\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7276e3d4e0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "IFS_hydro = init_IFS_linear(historical_data['if_slope'][historical_data['Energy']=='Hydro'].mean())\n",
    "IFS_thermo = init_IFS_linear(historical_data['if_slope'][historical_data['Energy']=='Nuclear'].mean())\n",
    "IFS_hydro.plot()\n",
    "IFS_thermo.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The uncertainty is very large in the impact functions, as only a few events could be used and the data doesn't appear to always show those events. But it seems to be the best way to do it for now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compare this method and the method with the isimip2b data: isimip2b provides historical data that is not forced with reanalysis data, meaning that the actual events are not seen and require a different method to estimate the impact functions (see if_calibration_isimip2b.ipynb)\n",
    "\n",
    "# This can probably be ignored, but we started with the isimip2b as we did not know how this isimip2a data was computed to start with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_data = pd.read_excel('historical_impacts_isimip2a.xlsx', index_col=0)\n",
    "\n",
    "isimip2a_isimip2b_slopes = pd.read_excel('slopes_isimip2a_isimip2b.xlsx', index_col=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gh_model</th>\n",
       "      <th>thermo_isimip2a</th>\n",
       "      <th>hydro_isimip2a</th>\n",
       "      <th>thermo_isimip2b</th>\n",
       "      <th>hydro_isimip2b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>matsiro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.002569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>h08</td>\n",
       "      <td>0.002333</td>\n",
       "      <td>0.004333</td>\n",
       "      <td>0.000560</td>\n",
       "      <td>0.002612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lpjml</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.000673</td>\n",
       "      <td>0.002995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gh_model  thermo_isimip2a  hydro_isimip2a  thermo_isimip2b  hydro_isimip2b\n",
       "0  matsiro              NaN             NaN         0.000284        0.002569\n",
       "1      h08         0.002333        0.004333         0.000560        0.002612\n",
       "2    lpjml         0.002000        0.003333         0.000673        0.002995"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isimip2a_isimip2b_slopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "isimip2a_isimip2b_slopes.loc[1,'hydro_isimip2a'] = historical_data[['slope_if_lpjml_gswp3', 'slope_if_lpjml_wfdei']][historical_data['Energy']=='Hydro'].mean(axis=1).mean()\n",
    "\n",
    "isimip2a_isimip2b_slopes.loc[1,'thermo_isimip2a'] = historical_data[['slope_if_lpjml_gswp3', 'slope_if_lpjml_wfdei']][historical_data['Energy']=='Nuclear'].mean(axis=1).mean()\n",
    "\n",
    "isimip2a_isimip2b_slopes.loc[2,'hydro_isimip2a'] = historical_data[['slope_if_h08_gswp3', 'slope_if_h08_princeton']][historical_data['Energy']=='Hydro'].mean(axis=1).mean()\n",
    "\n",
    "isimip2a_isimip2b_slopes.loc[2,'thermo_isimip2a'] = historical_data[['slope_if_h08_gswp3', 'slope_if_h08_princeton']][historical_data['Energy']=='Nuclear'].mean(axis=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "isimip2a_isimip2b_slopes.to_excel('slopes_isimip2a_isimip2b.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f72752ee710>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D8NVXX/H1119z1llnHfN9N23aROPGjenduzfx8fFZJoL8yGoNgWXLlnH77bczbdo0TjnllAz7L1myhC1btnD48GEmTZpE69atCyQOkXDZvx/++U84/3zYvh3efttPD/2//ws6svBRIigEN9xwA2bGFVdcAfjkMHLkSNq3b0/r1q05/fTTs/3ZO+64g9TUVBo3bkzXrl0ZO3Zshk/c2RkyZAiNGjXinHPOoUyZMhn67Y9H2hoCQ4cOZfDgwQA8/PDD/PLLL1x33XU0bdqUTp06Hdm/VatW9OnTh0aNGlG7dm2uvfbaAolDJBw+/dRPDnnqKd8FtG4d/PGPQUcVflqPoBAMHDiQPXv28PjjjwcdynGpVasWCQkJVKlSpVDfN9L/fSX6/fyzbwUMGwanneaXkAx9bisyclqPIKyDxWZ2JTAUKA6Mcs49nen104BXgQqhffo452aEM6bCdu2117Jp0ybmzp0bdCgikoXZs32RuG3b/F3C//kPlCsXdFSFK2yJwMyKA8OAy4FkYKmZTXPOrU2326PAm865EWbWAJgB1ApXTEGYMmVKgR9z9uzZ9O7dO8O22rVrh+W90ktKSgrr8UUK048/wv33+zuEzz4bFi70U0NjUThbBM2Bjc65zQBm9gbQGUifCBxwUuhxeWB7ft/MORczM1LatWtHu3btgg6jUERb16VEh8mTfYnoXbugb1+/dkDp0kFHFZxwDhZXB7ale54c2pZef6C7mSXjWwN3Z3UgM7vNzBLMLGHnzp1HvV66dGl27dqli0YR45xj165dlI7lv1ApUN9+6wd/r7sOqleHhAR44onYTgIQ3hZBVh/PM1+p/wyMdc49b2atgHFm1sg5dzjDDzk3EhgJfrA480Fr1KhBcnIyWSUJiW6lS5fO8mY4kbxwzt8M9sADsG8fPP00PPgg5KGUV5EWztOQDNRM97wGR3f93ApcCeCcW2RmpYEqwI68vFGJEiWoXbv2cYQqIkXVli1+MHjOHL9GwKhRUK9e0FFFlnB2DS0F6ppZbTMrCXQDpmXa52vgUgAzqw+UBvSxXkSOW2qqXzO4USP4/HM/NXTePCWBrIStReCcSzGzu4DZ+Kmho51za8xsAJDgnJsGPAi8bGb347uN/ubU0S8ix2ndOr9s5KJFftH4l17y9wdI1sLaQxa6J2BGpm390j1eC8TohC0RKWiHDsGzz8KAAf5egNdeg+7di2Z9oIKkoRIRKRISE+GWW2DlSrj+et8tdOqpQUcVHVRrSESi2r59frGYFi1g506/kPykSUoCeaEWgYhErQUL4O9/hw0b/JjAwIFQoULQUUUftQhEJOrs3evvDL74YkhJ8VNDR41SEsgvJQIRiSozZvgpoSNG+FpBq1bBpZcGHVV0U9eQiESFH37wF/7x46FBA/jsM2jZMuioiga1CEQkojkHb77pL/5vvAH9+sEXXygJFCS1CEQkYm3fDnfcAVOnQny8Hwto0iToqIoetQhEJOI4B6+84lsBs2fDc8/5u4SVBMJDLQIRiSibN0OPHjB3rp8VNGoUnHlm0FEVbWoRiEhESE2FwYP9jKClS319oLlzlQQKg1oEIhK4NWv8DWGLF0P79vC//4GWoSg8ahGISGAOHvQF4s49FzZtggkT4L33lAQKm1oEIhKIpUt9kbjVq+Evf4EhQ+Dkk4OOKjapRSAiheq33+Chh/x9AD/9BNOmweuvKwkESS0CESk08+b5InGbNsHtt8Mzz0D58kFHJWoRiEjY7dnjL/xt2/rnc+f6AWElgcigRCAiYfX++9Cwob8f4KGH/MIxaQlBIoMSgYiExc6dfhC4Y0eoVMkvIP/cc1C2bNCRSWZKBCJSoJyDiRN9eYjJk+Hf/4aEBDj//KAjk+xosFhECkxyMvTs6buDWrTw9YIaNgw6KjkWtQhE5LgdPuxLQjRoAB99BIMGwaefKglEC7UIROS4bNzoi8TNmweXXAIvvwx16gQdleSFWgQiki8pKX6x+MaN/UIxL7/s1wtQEog+ahGISJ6tWuWLxC1dCp06wfDhUL160FFJfqlFICK5duAAPPYYnHceJCX5pSPffVdJINqpRSAiubJ4sW8FrFkD3bv7tQOqVAk6KikIahGISI5+/RUefBAuuMCXipg+HcaNUxIoStQiEJFszZ3rZwRt3gz/+IcvEnfSSUFHJQVNLQIROcru3T4BXHopFC8O8+fDiBFKAkWVEoGIZDB1qr8xbPRo6NULVqyAiy4KOioJJyUCEQFgxw7o1g2uucYvErN4se8KKlMm6Mgk3JQIRGKcczB+PNSvD1OmwBNP+CJx8fFBRyaFJayJwMyuNLMvzWyjmfXJZp/rzWytma0xswnhjEdEMvr6a2jfHm68Ec46C5Yvh759oUSJoCOTwhS2WUNmVhwYBlwOJANLzWyac25tun3qAo8AFzrnfjKzU8IVj4j8Lq1IXK9e/vHQoXDnnX5gWGJPOKePNgc2Ouc2A5jZG0BnYG26fXoAw5xzPwE453aEMR4RAb76yq8bvHAhXHYZjBwJtWsHHZUEKZxdQ9WBbemeJ4e2pVcPqGdmn5rZ52Z2ZVYHMrPbzCzBzBJ27twZpnBFiraUFHj2WTjnHF8raPRo+OADJQEJb4vAstjmsnj/ukAboAaw0MwaOed2Z/gh50YCIwHi4+MzH0NEjmHFCl8eIjERrr0Whg2DqlWDjkoiRThbBMlAzXTPawDbs9hnqnPukHNuC/AlPjGISAHYvx8efdTPANq2Dd56C955R0lAMgpnIlgK1DWz2mZWEugGTMu0z7tAWwAzq4LvKtocxphEYsZnn8G558J//gM33ADr1kGXLkFHJZEobInAOZcC3AXMBtYBbzrn1pjZADPrFNptNrDLzNYCHwMPO+d2hSsmkVjwyy9w773QujX89hvMmgVjx0KlSkFHJpHKnIuuLvf4+HiXkJAQdBgiEemDD+C222DrVrjrLnjySYiLCzoqiQRmluicy/I2Qd1ZLFIE/PQT3HwztGsHpUv7qaEvvqgkILmjRCAS5d55xxeJGzcOHnnE3x3cunXQUUk00XoEIlHqu+9898/bb0PTpjBjhh8cFskrtQhEooxz8OqrvhXw/vt+HGDJEiUByT+1CESiSFIS3H67HxS+8EIYNQrOPjvoqCTaqUUgEgUOH4b//hcaNYJPP/UDwQsWKAlIwVCLQCTCrV/vi8R9+qmfFfTSS3D66UFHJUWJWgQiEerQId//f845sHatvyls5kwlASl4ahGIRKBly+CWW/xU0C5dfLfQqacGHZUUVWoRiESQ/fv9vQDnn++nh779ti8UpyQg4aQWgUiE+OQTXyr6q6/8XcLPPw8VKwYdlcQCtQhEAvbzz/7GsD/8AQ4e9FNDR49WEpDCo0QgEqBZs/yU0OHD4Z57/Mphl18edFQSa5QIRAKwaxfcdBNcdRWULeu7hYYOhXLlgo5MYpESgUghcg4mT/blISZMgL59/QyhCy4IOjKJZRosFikk334Ld94JU6bAeefB7Nm+WJxI0HLVIjCze83sJPNeMbMvzOyKcAcnUhQ4B2PG+FbAjBnw9NOweLGSgESO3HYN3eKc2wtcAZwM3Aw8HbaoRIqILVvgiiv8zWGNG8PKldC7N5ygtrhEkNwmAgt9vxoY45xbkW6biGSSmgovvOBnBH3+uZ8VNG8e1KsXdGQiR8vt55JEM/sAqA08YmZxwOHwhSUSvdat80XiPvvMzwr63//gtNOCjkoke7lNBLcCTYHNzrnfzKwSvntIREIOHYJnn4UBA/w00HHj4IYbwNR2lgiX20TQCljunPvVzLoD5wFDwxeWSHRJTPTjACtXwvXX+/UCTjkl6KhEcie3YwQjgN/M7BygF7AVeC1sUYlEiX37oE8faNECdu70U0MnTVISkOiS20SQ4pxzQGdgqHNuKBAXvrBEIt+CBX6tgGee8UXi1q6Fa64JOiqRvMttIvjZzB4BbgSmm1lxoET4whKJXHv3+hvDLr4YUlJgzhx4+WWoUCHoyETyJ7eJoCtwAH8/wXdAdeC5sEUlEqFmzvRTQkeMgPvv90XiLr006KhEjk+uEkHo4v86UN7MOgD7nXMaI5CY8cMPcOONcPXVEBfnp4YOGgQnnhh0ZCLHL7clJq4HlgDXAdcDi82sSzgDE4kEzsGbb/ryEG+8Af36wRdfQMuWQUcmUnByO320L3C+c24HgJmdDMwBJocrMJGgbd8Od9wBU6dCfLwfC2jSJOioRApebscIiqUlgZBdefhZkajiHLzyim8FzJ4NAwfCokVKAlJ05bZFMMvMZgMTQ8+7AjPCE5JIcDZvhh49YO5cPyto1Cg488ygoxIJr1wlAufcw2b2J+BCfLG5kc65KWGNTKQQpRWJe/RRKF4cXnrJ1wsqpnavxIBcF8N1zr0NvB3GWEQCsWYN3HqrXyOgfXtfJK5GjaCjEik8OX7eMbOfzWxvFl8/m9neYx3czK40sy/NbKOZ9clhvy5m5swsPj+/hEh+HDzoC8Sdey5s2uSXjnzvPSUBiT05tgicc/kuIxG6+3gYcDmQDCw1s2nOubWZ9osD7gEW5/e9RPJq6VLfCli1Cv7yFxgyBE4+OeioRIIRzh7Q5sBG59xm59xB4A18raLMHgeeBfaHMRYRAH77DR5+2N8H8OOPMG0avP66koDEtnAmgurAtnTPk0PbjjCzc4Gazrn3wxiHCOBXCDvnHD8dtEcPPzbQsWPQUYkEL5yJIKvlONyRF82KAYOBB495ILPbzCzBzBJ27txZgCFKLNizB/7xD2jb1t8jMHeuHxAuXz7oyEQiQzgTQTJQM93zGsD2dM/jgEbAPDNLAloC07IaMHbOjXTOxTvn4k9WG17y4P33oWFDXx30oYf8wjFt2wYdlUhkCWciWArUNbPaZlYS6AZMS3vRObfHOVfFOVfLOVcL+Bzo5JxLCGNMEiN27vSDwB07QqVKfgH5556DsmWDjkwk8oQtETjnUoC7gNnAOuBN59waMxtgZp3C9b4S25zz00Dr14fJk+Hf/4aEBDj//KAjE4lcub6hLD+cczPIVIrCOdcvm33bhDMWKfqSk6FnT98d1Ly5rxfUqFHQUYlEPt1AL1Hv8GFfEqJBA/joI79OwGefKQmI5FZYWwQi4bZhg58KOn8+XHKJHxSuUyfoqESii1oEEpVSUvz9AE2awPLlvkronDlKAiL5oRaBRJ2VK315iIQE6NwZhg+HatWCjkokeqlFIFHjwAF47DFo1gy2bvVLR06ZoiQgcrzUIpCo8PnnvhWwdi107w6DB0OVKkFHJVI0qEUgEe3XX+GBB+CCC2DvXpg+HcaNUxIQKUhqEUjE+ugjPyNoyxZ/f8DTT8NJJwUdlUjRoxaBRJzdu/0ykZddBiec4KeGDh+uJCASLkoEElGmTvU3ho0ZA716wYoVcNFFQUclUrQpEUhE+P576NoVrrnGLxKzeDE88wyUKRN0ZCJFnxKBBMo5GD/etwLefRcef9wvIxmv1atFCo0GiyUwX3/tF4yZORNatfJF4urXDzoqkdijFoEUusOHYcQIv2DM/PkwdCgsXKgkIBIUtQikUH31lZ8RtHAhXH65rxpau3bQUYnENrUIpFCkpMCzz/rF41et8rOCZs9WEhCJBGoRSNitWAG33AJffAHXXgvDhkHVqkFHJSJp1CKQsNm/Hx591M8A+uYbv3TkO+8oCYhEGrUIJCw++8wXiVu/Hm66ya8aVqlS0FGJSFbUIpAC9csvcO+90Lo1/PYbzJoFY8cqCYhEMrUIpMB88AHcdpu/P+DOO+HJJyEuLuioRORY1CKQ4/bTT3DzzdCuHZQu7aeGvviikoBItFAikOPyzju+PMS4cfDPf/r1gy+8MOioRCQv1DUk+fLdd3DXXfD223Duub5MRNOmQUclIvmhFoHkiXPw6qu+FfD++/DUU75SqJKASPRSi0ByLSkJbr/dDwq3bg2jRsFZZwUdlYgcL7UI5JgOH/aDv40a+fsD/vtfXyxOSUCkaFCLQHK0fr0vEvfpp35W0EsvwemnBx2ViBQktQgkS4cO+fsAzjkH1q714wIzZyoJiBRFahHIUZYt80Xili+HLl18V9CppwYdlYiEi1oEcsS+ffDIIwdvjBMAABBJSURBVHD++X566Ntvw1tvKQmIFHVqEQgAn3zii8R99ZVvDQwcCBUrBh2ViBQGtQhi3M8/+xvD/vAHOHgQPvzQrx2sJCASO5QIYtisWX5K6PDhvmLoqlVw2WVBRyUihS2sicDMrjSzL81so5n1yeL1B8xsrZmtNLOPzExzUgrBrl1+jYCrroITT/RTQ4cMgXLlgo5MRIIQtkRgZsWBYcBVQAPgz2bWINNuy4B451wTYDLwbLjiEV8e4q23fHmICRP86mHLlkGrVkFHJiJBCmeLoDmw0Tm32Tl3EHgD6Jx+B+fcx86530JPPwdqhDGemPbtt/DHP8L110PNmpCQAI8/DqVKBR2ZiAQtnImgOrAt3fPk0Lbs3ArMzOoFM7vNzBLMLGHnzp0FGGLR5xyMHg316/sxgWefhc8/9zeKiYhAeBOBZbHNZbmjWXcgHnguq9edcyOdc/HOufiTTz65AEMs2rZsgSuu8NNCzzkHVqyAhx+GEzRpWETSCWciSAZqpnteA9ieeSczuwzoC3Ryzh0IYzwxIzUVhg71M4IWL4YRI+Djj6FevaAjE5FIFM7PhkuBumZWG/gG6Ab8Jf0OZnYu8BJwpXNuRxhjiRlr1/oWwOef+1lBL73kxwRERLITthaBcy4FuAuYDawD3nTOrTGzAWbWKbTbc0A54C0zW25m08IVT1F38KAf/D33XNiwAcaPh+nTlQRE5NjC2lvsnJsBzMi0rV+6x7p9qQAkJPhWwMqV0K2b7xY65ZSgoxKRaKE7i6PYb79Br17QogX88ANMnQoTJyoJiEjeaP5IlJo/3y8Ys3Ej9Ojhp4VWqBB0VCISjdQiiDJ790LPntCmjV9C8qOPYORIJQERyT8lgigyfTo0bOgv/A884McELrkk6KhEJNopEUSBH36A7t2hQwcoX94vIP/8875gnIjI8VIiiGDOwRtv+PIQb74Jjz0GX3zhB4dFRAqKBosj1Dff+LGA997zS0e+8go0bhx0VCJSFKlFEGGcg5df9qWi58zxS0YuWqQkICLhoxZBBNm0yU8F/fhjPyvo5ZfhzDODjkpEijq1CCJAaioMGuQ/9Scm+llBc+cqCYhI4VCLIGCrV/vyEEuWQMeOvlJo9ZxWbRARKWBqEQTk4EHo3x/OOw82b/alIaZOVRIQkcKnFkEAliyBW26BNWvgL3/xReKqVAk6KhGJVUoEhei33+Bf/4IhQ6BqVT81tEOHoKMSkcAdPAg//ui/du3K+D394x49/LKDBUyJoJB8/LEvErd5M/zjH/D00/4uYREpQg4ehJ9+yv5Cnt2F/pdfsj/mCSdApUpQubI/dhgoEYTZnj1+neCXX4Yzzvh9aqiIRLCUlNxfxNM//vnn7I9ZvPjvF/RKlaBGDb+YeKVKGbenfU97XK4cWFZLwBccJYIweu89/+n/u+98MujfH8qWDToqkRiSkgK7d2d9Ec/pAr93b/bHLFYs4wW7WjU/9zvzBT3z45NOCvsFPb+UCMJgxw64915fJ6hxYz8bKD4+6KhEolhqqr+gH6u7JfPj3buzP2axYlCx4u8X6lNP9YW9srqQp/8eF+d/tghRIihAzsGECT4J7N0LAwZA795QsmTQkYlEiNRU31+alwv6rl05X9DN/IIcaRfrKlXgrLNy/oReubIfpCtiF/T8UiIoINu2+SJx06f76qCvvOLXDhApkg4f9hf03PSdZ76gO5f9cdNf0CtVgrp1f79wV6zov2e+sJcv7/vfJd+UCI7T4cO+JESvXv7DzuDBcPfd+n8pUcI533zNywyXXbv87JXDh7M/bvnyGS/YZ5yRc/955co+CegPJxBKBMdhwwY/rXf+fLj0Up8Q6tQJOiqJSc75GSt5meGSdkFPTc3+uCed9PvFulIlOP307PvO0/apWNFPeZSooX+tfEhJ8Z/8+/WDUqVg1Ch/p3CETgiQaJJ2Qc/LgGja45wu6HFxGbtWatbMuf887YJeokTh/e4SGCWCPFqxwheJS0yEzp1h+HA/e0wkA+fg119z96k88+OUlOyPW65cxgt32rTFY13QNWNBcqBEkEsHDsATT/g7gitV8ktHdumiVkCR55yvDZLXQdEff4RDh7I/7oknZrxwN2yY801FaRf0UqUK73eXmKFEkAuLFvlWwLp1cOONvluocuWgo5I8y+qCnpvB0QMHsj9mmTIZL9b16x97ULRiRShduvB+b5FjUCLIwa+/Qt++8MIL/m7wGTPgqquCjkrYty9/t//v35/9MUuVyjg1sV69rC/gmacvlilTeL+3SJgoEWRjzhw/IygpCe64A556yk+gkAK0f3/eZrikPd63L/tjliyZ8WJ95pk5fzpPe6zaHxLDlAgy2b0bHnwQRo/297LMnw8XXRR0VBHuwIHfL9K5reny44++qyY7JUpkvKDXqePrdOTUh165sv+EroEbkTxRIkjn3Xf9p/8dO3xpiMcei7GWf1oJ3bzeXPTrr9kfs0SJjF0rtWpBs2bHrrhYtqwu6CKFRIkA+P57fzfwW2/5qrDvveevVVHr0KHcXdAzX9xzqomeuYRuzZq/l9DN6eaiQiihKyLHJ6YTgXMwbhzcd5//UPvEE75URMTcQ5OSkr9P6DnVRM9cQrd69ZznoqevuKgLukiRFLOJ4Ouv4fbbYdYsaNXKF4mrXz9Mb5a5hG5u5qD/+KMv6pWdtAt62lfVqkfPRc/8OK0muiouikg6YU0EZnYlMBQoDoxyzj2d6fVSwGtAM2AX0NU5lxTOmA4fhhEjoE8f3yJ44QU/LpCrWldpF/S83v5/rBK6aTXRK1eGU075fS565u6W9NMXdUEXkQIStkRgZsWBYcDlQDKw1MymOefWptvtVuAn59yZZtYNeAboGq6YvvzSrxv8ySdweZtDjHzsG2qV3QEf5nL64rFK6KZf5KJy5YwldLMbHK1QQRd0EQlUOFsEzYGNzrnNAGb2BtAZSJ8IOgP9Q48nA/81M3Mup6tt/oy+eQF3vNqCMuxjDPdz07yx2Lxsdq5QIeOFO62Ebk4DoyqhKyJRKpyJoDqwLd3zZKBFdvs451LMbA9QGfgh/U5mdhtwG8Bpp52Wr2DqnVWMDtWX8+Kl71L1tJpQaXD2NdFVQldEYkg4r3hZTTHJ/Ek/N/vgnBsJjASIj4/PV2uhdZ/WtO4DR+ciEZHYFs7O6WSgZrrnNYDt2e1jZicA5YEfwxiTiIhkEs5EsBSoa2a1zawk0A2YlmmfacBNocddgLnhGB8QEZHsha1rKNTnfxcwGz99dLRzbo2ZDQASnHPTgFeAcWa2Ed8S6BaueEREJGthHRV1zs0AZmTa1i/d4/3AdeGMQUREcqYJ7CIiMU6JQEQkxikRiIjEOCUCEZEYZ9E2W9PMdgJb8/njVch013KEUpwFJxpiBMVZkKIhRij8OE93zp2c1QtRlwiOh5klOOfig47jWBRnwYmGGEFxFqRoiBEiK051DYmIxDglAhGRGBdriWBk0AHkkuIsONEQIyjOghQNMUIExRlTYwQiInK0WGsRiIhIJkoEIiIxLmYSgZldaWZfmtlGM+sTdDxpzCzJzFaZ2XIzSwhtq2RmH5rZhtD3igHENdrMdpjZ6nTbsozLvBdC53almZ0XcJz9zeyb0DldbmZXp3vtkVCcX5pZu0KKsaaZfWxm68xsjZndG9oeUeczhzgj7XyWNrMlZrYiFOe/Q9trm9ni0PmcFCp/j5mVCj3fGHq9VoAxjjWzLenOZdPQ9sD+hgBwzhX5L3wZ7E1AHaAksAJoEHRcodiSgCqZtj0L9Ak97gM8E0BcFwHnAauPFRdwNTATv+JcS2BxwHH2Bx7KYt8GoX/7UkDt0P+J4oUQY1XgvNDjOOCrUCwRdT5ziDPSzqcB5UKPSwCLQ+fpTaBbaPv/gJ6hx3cA/ws97gZMCjDGsUCXLPYP7G/IORczLYLmwEbn3Gbn3EHgDaBzwDHlpDPwaujxq8A1hR2Ac24BR68Wl11cnYHXnPc5UMHMqgYYZ3Y6A2845w4457YAG/H/N8LKOfetc+6L0OOfgXX49boj6nzmEGd2gjqfzjn3S+hpidCXAy4BJoe2Zz6faed5MnCpmWW1TG5hxJidwP6GIHa6hqoD29I9Tybn/+CFyQEfmFmimd0W2naqc+5b8H+cwCmBRZdRdnFF4vm9K9TEHp2uay3wOEPdEufiPyFG7PnMFCdE2Pk0s+JmthzYAXyIb43sds6lZBHLkThDr+8BKhd2jM65tHP5n9C5HGxmpTLHmEX8YRcriSCr7B8p82YvdM6dB1wF3GlmFwUdUD5E2vkdAZwBNAW+BZ4PbQ80TjMrB7wN3Oec25vTrllsCzLOiDufzrlU51xT/FrozYH6OcQSSJyZYzSzRsAjwNnA+UAloHeQMaaJlUSQDNRM97wGsD2gWDJwzm0Pfd8BTMH/p/4+rVkY+r4juAgzyC6uiDq/zrnvQ3+Eh4GX+b27IrA4zawE/uL6unPundDmiDufWcUZieczjXNuNzAP369ewczSVl1MH8uROEOvlyf33YkFGeOVoe4355w7AIwhQs5lrCSCpUDd0KyCkvgBo2kBx4SZnWhmcWmPgSuA1fjYbgrtdhMwNZgIj5JdXNOAv4ZmPrQE9qR1eQQhU9/qtfhzCj7ObqFZJLWBusCSQojH8Otzr3PODUr3UkSdz+zijMDzebKZVQg9LgNchh/P+BjoEtot8/lMO89dgLkuNEJbyDGuT5f4DT+Gkf5cBvc3VJgj00F+4Uflv8L3JfYNOp5QTHXwsy5WAGvS4sL3X34EbAh9rxRAbBPx3QCH8J9Wbs0uLnyzdljo3K4C4gOOc1wojpX4P7Cq6fbvG4rzS+CqQoqxNb6ZvxJYHvq6OtLOZw5xRtr5bAIsC8WzGugX2l4Hn4g2Am8BpULbS4eebwy9XifAGOeGzuVqYDy/zywK7G/IOacSEyIisS5WuoZERCQbSgQiIjFOiUBEJMYpEYiIxDglAhGRGKdEIJIPZlbL0lU8FYlmSgQiIjFOiUAkk9Cn/XVm9nKolvwHZlbGzJqF6ssvAu7MtP9CM/si9HVBaPs4M+ucbr/XzayTmTUM1apfHio+VjeAX1PkCCUCkazVBYY55xoCu4E/4WvD3OOca5Vp3x3A5c4XD+wKvBDaPgq4GcDMygMXADOAfwBDnS9IFo+/I1okMEoEIlnb4pxbHnqciK++WcE5Nz+0bVy6fUsAL5vZKnwpgwYAoX3PNLNTgD8DbztfBnkR8E8z6w2c7pzbF/5fRyR7SgQiWTuQ7nEqUJHsywLfD3wPnIP/hF8y3WvjgBvwLYMxAM65CUAnYB8w28wuKdDIRfJIiUAkd3YDe8ysdej5DeleKw9863yZ5hvxS6OmGQvcB+CcWwNgZnWAzc65F/BF3JqEN3SRnCkRiOTezcCw0GBx+u6c4cBNZvY5UA/4Ne0F59z3+BLJY9Lt3xVYHVq96mzgtXAHLpITVR8VCSMzK4svK3yec25P0PGIZEUtApEwMbPLgPXAi0oCEsnUIhARiXFqEYiIxDglAhGRGKdEICIS45QIRERinBKBiEiM+39QlkVcO+jMUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x0 = np.linspace(0,365,20)\n",
    "\n",
    "color = ['red','blue']\n",
    "for b in isimip2a_isimip2b_slopes.index[1:3]:\n",
    "    c=0\n",
    "    fig,ax = plt.subplots()\n",
    "    for a in isimip2a_isimip2b_slopes.columns[1:]:\n",
    "        ax.plot(x0,x0*isimip2a_isimip2b_slopes[a][b],label=a, color= color[c])\n",
    "        c = c+1\n",
    "        if c==2:\n",
    "            c=0\n",
    "        \n",
    "    ax.set_title(isimip2a_isimip2b_slopes['gh_model'][b])\n",
    "    ax.set_xlabel('ndays')\n",
    "    ax.set_ylabel('loss')\n",
    "    ax.legend()\n",
    "    \n",
    "x0 = np.linspace(0,365,20)\n",
    "color = ['red','blue']\n",
    "b=0\n",
    "c=0\n",
    "fig,ax = plt.subplots()\n",
    "for a in isimip2a_isimip2b_slopes.columns[3:5]:\n",
    "    ax.plot(x0,x0*isimip2a_isimip2b_slopes[a][b],label=a, color= color[c])\n",
    "    c = c+1\n",
    "    if c==2:\n",
    "        c=0\n",
    "\n",
    "ax.set_title(isimip2a_isimip2b_slopes['gh_model'][b])\n",
    "ax.set_xlabel('ndays')\n",
    "ax.set_ylabel('loss')\n",
    "ax.legend()   "
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}