{
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ultimate Cost of Carbon computation\n",
    "\n",
    "## The model defined in function calls \n",
    "\n",
    "Running this document (costs.ipynb) interactively will require python 3 with libraries matplotlib, math, numpy, and IPython.  The pdf version of the file is a non-interactive printout of the original.  \n",
    "\n",
    "The model is divided into two components, the code for which are in the following module.  One function (climateModel) simulates climate dynamics and the other (econModel) estimates costs.  \n",
    "\n",
    "The arguments to the physical model (function climateModel) are\n",
    "\n",
    "| Parameter Name | Description | Value note |\n",
    "|----------------|----- | ------------ |\n",
    "|  CReleaseGton | Gigatons of anthropogenic carbon |  the buffer chemistry is good for the range 1000 - 5000 |\n",
    "| CFeedbackFactor | Fraction by which the natural carbon cycle amplifies the human source | < 1.5 |\n",
    "| oceanAcidTime | Time scale for pH recovery of the ocean, years | models find 1-10kyr |\n",
    "|thermostatTime | Time scale for atmospheric CO2 recovery | models find 200kyr, must be longer than 100 kyr |\n",
    "|warmingTime | Time scale for temperature equilibration | governed by ocean overturning |\n",
    "|iceMeltingTime | Time scale for collapsing ice sheets, years | models predict a few thousand years |\n",
    "| dt2X | The equilibrium climate sensitivity, degrees C per doubling of CO2 | IPCC range 2.5 - 4.5 |\n",
    "\n",
    "The parameters other than the magnitude of the carbon release are grouped into an array for convenience in uncertainty analysis.  The model spans one million years of time, in time steps which become larger through time as things change more slowly.  The model returns the following lists (arrays) of values:\n",
    "\n",
    "| Results Name | Description |\n",
    "| ---- | ---- |\n",
    "| times | A list of time points in years |\n",
    "| deltaTs | Value of each time step in years |\n",
    "| CAtmFactors | Atmospheric CO2 concentrations relative to preanthropogenic |\n",
    "| temperatures | Equilibrium radiative temperature anomalies |\n",
    "| seaLevels | Sea Level in meters |\n",
    "\n",
    "The climate module imposes a two-stage drawdown on the atmospheric pCO2 perturbation.  The timing of the first stage of drawdown is determined by the pH recovery of the oceans (oceanAcidTime).  The timing of the second (final) recovery is set by silicate weathering in the CO2 thermostat (thermostatTime).  The airborne fractions of the CO2 at each stage were taken from model results from the tailMIP model intercomparison project, Archer et al., (2009), from model values using 1,000 and 5,000 Gton C releases, at time points of 1,000 and 10,000 years respectively. Airborne fractions for other release magnitudes in the model are interpolated between the 1,000 and 5,000 Gton C release values.  \n",
    "\n",
    "The model neglects time-dependence in the global temperature anomaly, and derives a list of temperatures using the equilibrium climate sensitivity and the ratio of atmospheric CO2 to natural.  \n",
    "\n",
    "Sea level changes relative to temperature are based on the correlation between sea level and global mean temperature in the paleo reconstructions, from Archer and Brovkin ().  The time scale for ice sheet melting is a parameter in the argument list (iceMeltingTime); the time scale for ice sheet recovery is taken to be the thermostat time scale (thermostatTime).  \n",
    "\n",
    "The economic model takes results from the climate model and adds three new parameters:\n",
    "\n",
    "| Parameter Name | Description |\n",
    "| --- | --- |\n",
    "| climCostFactor | Economic penalty due to climate change, in percent GDP per degree C |\n",
    "|SLCostFactor | Economic penalty due to sea level rise, in percent GDP for complete melting (70 m) |\n",
    "| econRecoveryTime | Economic recovery time for both types of cost.  This could be a soil time of 10kyr or a CO2 time of 200 kyr |\n",
    "\n",
    "The economic model returns the following results:\n",
    "\n",
    "| Result Name | Description |\n",
    "| --- | --- |\n",
    "|  climCosts | A list of economic penalties due to climate change, in % GDP |\n",
    "| seaLevelCosts | A list of economic penalties due to sea level rise, in % GDP |\n",
    "| cumClimCost | Cumulative cost due to climate, in dollars / ton C |\n",
    "| cumSeaLevelCost | Cumulative cost due to sea level, in dollars / ton C |\n",
    "\n",
    "Also included in the following code section is a wrapper for both models, with all parameters packaged in a single list, for convenience in Monte Carlo simulation.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import numpy as np\n",
    "from IPython.display import Markdown, display\n",
    "\n",
    "def climateModelWrapper( parmList ):\n",
    "    CReleaseGton = parmList[0]\n",
    "    CFeedbackFactor = parmList[1]\n",
    "    oceanAcidTime = parmList[2]\n",
    "    thermostatTime = parmList[3]\n",
    "    warmingTime = parmList[4]\n",
    "    iceMeltingTime = parmList[5]\n",
    "    dT2x = parmList[6]\n",
    "    times, deltaTs, CAtmFactors, temperatures, seaLevels = \\\n",
    "       climateModel( CReleaseGton, CFeedbackFactor, oceanAcidTime, thermostatTime, \\\n",
    "                    warmingTime, iceMeltingTime, dT2x )\n",
    "    return times, deltaTs, CAtmFactors, temperatures, seaLevels\n",
    "\n",
    "def climateModel( CReleaseGton, CFeedbackFactor, oceanAcidTime, thermostatTime, warmingTime, iceMeltingTime, dT2x ):\n",
    "\n",
    "    SLMax = 70.\n",
    "    seaLevelMperC = 17\n",
    "    CAtmNatGton = 500   # Gton C in a natural atmosphere, used for radiative forcing calculation\n",
    "    CReleaseTotal = CReleaseGton * ( 1. + CFeedbackFactor )\n",
    "    CAirborneOcnEquil = 0.1 + (CReleaseTotal - 1000)/4000 * 0.05\n",
    "    CAirborneOcnNeut  = 0.1 + (CReleaseTotal - 1000)/4000 * 0.15\n",
    "\n",
    "    CAtmFactors = []\n",
    "    temperatures = []\n",
    "    seaLevels = []\n",
    "    times = []\n",
    "    deltaTs = []\n",
    "    \n",
    "    tInit = 100     # these parameters produce a series of time points that plot nicely in logs to 1 million years\n",
    "    tFactor = 1.08\n",
    "    nTSteps = 120\n",
    "    \n",
    "    tEvolving = 0\n",
    "    \n",
    "    timeNow = tInit\n",
    "    for iTime in range(0,nTSteps):\n",
    "        dt = timeNow * ( tFactor - 1. )\n",
    "        timeNow *= tFactor\n",
    "        CShort = CAirborneOcnEquil * CReleaseGton / CAtmNatGton \\\n",
    "            * math.exp( -timeNow / oceanAcidTime )\n",
    "        CLong = CAirborneOcnNeut * CReleaseGton / CAtmNatGton \\\n",
    "            * math.exp( -timeNow / thermostatTime )    \n",
    "        CAtmFactor = 1 + CShort + CLong\n",
    "        tEquil = math.log( CAtmFactor ) / math.log(2) * dT2x\n",
    "        if iTime == 0:\n",
    "            tEvolving = tEquil * 0.8\n",
    "        elif dt < warmingTime:\n",
    "            tEvolving += ( tEquil - tEvolving ) * dt / warmingTime\n",
    "        else:\n",
    "            tEvolving = tEquil\n",
    "\n",
    "        seaLevel = tEvolving * seaLevelMperC \\\n",
    "            * ( 1 - math.exp( - timeNow / iceMeltingTime ) ) \\\n",
    "            * ( math.exp( - timeNow / thermostatTime ))  \n",
    "        if seaLevel > SLMax:\n",
    "            seaLevel = SLMax\n",
    "        \n",
    "        times.append( timeNow )\n",
    "        deltaTs.append( dt )\n",
    "        CAtmFactors.append( CAtmFactor )\n",
    "        temperatures.append( tEvolving )\n",
    "        seaLevels.append( seaLevel )\n",
    "    return times, deltaTs, CAtmFactors, temperatures, seaLevels\n",
    "\n",
    "def econModel( times, deltaTs, temperatures, seaLevels, CReleaseGton, \\\n",
    "              climCostFactor, SLCostFactor, econRecoveryTime ):\n",
    "    SLMax = 70\n",
    "    climCosts = []\n",
    "    seaLevelCosts = []\n",
    "    cumClimCost = 0\n",
    "    cumSeaLevelCost = 0\n",
    "    for index, time in enumerate(times):\n",
    "        climCost = temperatures[index] * climCostFactor \\\n",
    "            * math.exp( - time / econRecoveryTime ) \n",
    "        seaLevelCost = seaLevels[index] * SLCostFactor / SLMax \\\n",
    "            * math.exp( - time / econRecoveryTime ) \n",
    "        climCosts.append( climCost )\n",
    "        seaLevelCosts.append( seaLevelCost )\n",
    "        cumClimCost += climCost * deltaTs[index] / CReleaseGton * 1E3 # %/yr * yr / Gton / 100% * $100E12 / 1E9Gton/ton\n",
    "        cumSeaLevelCost += seaLevelCost * deltaTs[index] / CReleaseGton * 1E3\n",
    "    return climCosts, seaLevelCosts, cumClimCost, cumSeaLevelCost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulations for 1000, 5000 Gton C releases, with economic recovery on geomorphic or carbon cycle time scales\n",
    "\n",
    "The next module runs the model for an array of values of the amount of carbon released and the time scale for recovery of the economic damages. Values can be added or deleted to the arrays at the top of the module to expand or contract the number of runs that will be plotted in modules below this.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run for all combinations of the following parameter settings\n",
    "CReleaseGtons     = [ 1000, 5000 ]\n",
    "\n",
    "# used in Monte Carlo, below, defined here to find log means as defaults\n",
    "parmNames = [ \"CReleaseGton\", \\\n",
    "              \"CFeedbackFactor\", \"oceanAcidTime\", \"thermostatTime\", \"warmingTime\", \"iceMeltingTime\", \"dT2x\", \\\n",
    "              \"climCostFactor\", \"SLCostFactor\", \"econRecoveryTime\", \\\n",
    "              \"AllGeo\", \"All\"]\n",
    "parmRanges = [  [1000,5000],      # 0, Gton C released \\\n",
    "                [0.1,0.5],        # 1, magnitude of possible carbon cycle feedback \\\n",
    "                [2000.,8000.],    # 2, oceanAcidTime \\\n",
    "                [1.e5,4.e5],      # 3, thermostatTime, with factor of 2 uncertainty on either side \\\n",
    "                [100.,1000.],     # 4, warmingTIme, in reality a range from different parts of the ocean \\\n",
    "                [300.,3000.],     # 5, iceMeltingTime, from Heinrich events (fast) and models (slow) \\\n",
    "                [1.5,4.5],        # 6, dT2x, range from IPCC \n",
    "                [1,4],            # 7, climCostFactor\n",
    "                [1,15],           # 8, SLCostFactor\n",
    "                [10000,200000]]   # 9, econRecoveryTime\n",
    "numParms = 10\n",
    "numParmsGeo = 7\n",
    "\n",
    "# geometric means to use in base scenario\n",
    "climBaseParmList = []\n",
    "parmLogRanges = []\n",
    "for parmRange in parmRanges:\n",
    "    parmLogRange = [ math.log(parmRange[0]), math.log(parmRange[1]) ]\n",
    "    parmLogRanges.append( parmLogRange )\n",
    "    climBaseParmList.append( math.exp( (parmLogRange[0]+parmLogRange[1])/2.)) \n",
    "\n",
    "#for i in range(1,6):\n",
    "#    print(parmNames[i],climBaseParmList[i])\n",
    "\n",
    "CFeedbackFactor = climBaseParmList[1]\n",
    "oceanAcidTime = climBaseParmList[2]\n",
    "thermostatTime = climBaseParmList[3]\n",
    "warmingTime = climBaseParmList[4]\n",
    "iceMeltingTime = climBaseParmList[5]\n",
    "dT2x = climBaseParmList[6]\n",
    "    \n",
    "CAtmFactorLists = []\n",
    "temperatureLists = []\n",
    "seaLevelLists = []\n",
    "for CReleaseGton in CReleaseGtons:\n",
    "    times, deltaTs, CAtmFactors, temperatures, seaLevels = \\\n",
    "        climateModel( CReleaseGton, CFeedbackFactor, oceanAcidTime, thermostatTime, warmingTime, \\\n",
    "                     iceMeltingTime, dT2x )\n",
    "    CAtmFactorLists.append( CAtmFactors )\n",
    "    temperatureLists.append( temperatures )\n",
    "    seaLevelLists.append( seaLevels )    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Figures from the simulations\n",
    "Time evolution of model parameters will be plotted on a linear time scale on the left, and a log scale on the right.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Atmospheric CO$_2$"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(\"## Atmospheric CO$_2$\"))\n",
    "\n",
    "fig,axarr = plt.subplots(nrows=1,ncols=2,sharex=False,sharey=True,squeeze=False)\n",
    "\n",
    "for index, CReleaseGton in enumerate(CReleaseGtons):\n",
    "    ax = axarr[0][0]\n",
    "    myString = str(CReleaseGton) + \" Gton\"\n",
    "    ax.plot(times,CAtmFactorLists[index],label=myString)\n",
    "    ax.set_xlabel(\"time, years\")\n",
    "    ax.set_ylabel(\"times preanthropogenic\")\n",
    "    ax.legend()\n",
    "    ax.set_xticks([0,5e5,1e6])\n",
    "    ax = axarr[0][1]\n",
    "    ax.semilogx(times,CAtmFactorLists[index])\n",
    "    ax.set_xlabel(\"time, years\")\n",
    "plt.savefig(\"figure_01.pdf\")\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Global Temperature Anomaly"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(\"## Global Temperature Anomaly\"))\n",
    "\n",
    "fig,axarr = plt.subplots(nrows=1,ncols=2,sharex=False,sharey=True,squeeze=False)\n",
    "\n",
    "for index, CReleaseGton in enumerate(CReleaseGtons):\n",
    "    ax = axarr[0][0]\n",
    "    myString = str(CReleaseGton) + \" Gton\"\n",
    "    ax.plot(times,temperatureLists[index],label=myString)\n",
    "    ax.set_xlabel(\"time, years\")\n",
    "    ax.set_ylabel(\"$^{\\circ}$C\")\n",
    "    ax.legend()\n",
    "    ax.set_xticks([0,5e5,1e6])\n",
    "    ax = axarr[0][1]\n",
    "    ax.semilogx(times,temperatureLists[index])\n",
    "    ax.set_xlabel(\"time, years\")\n",
    "plt.savefig(\"figure_02.pdf\")\n",
    "plt.show()        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Sea Level"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(\"## Sea Level\"))\n",
    "fig,axarr = plt.subplots(nrows=1,ncols=2,sharex=False,sharey=True,squeeze=False)\n",
    "for index, CReleaseGton in enumerate(CReleaseGtons):\n",
    "    ax = axarr[0][0]\n",
    "    myString = str(CReleaseGton) + \" Gton\"\n",
    "    ax.plot(times,seaLevelLists[index],label=myString)\n",
    "    ax.set_xlabel(\"time, years\")\n",
    "    ax.set_ylabel(\"meters above present\")\n",
    "    ax.set_xticks([0,5e5,1e6])\n",
    "    ax.legend()\n",
    "    ax = axarr[0][1]\n",
    "    ax.semilogx(times,seaLevelLists[index])\n",
    "    ax.set_xlabel(\"time, years\")\n",
    "plt.savefig(\"figure_03.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Costs"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "<table border 1px><tr><td colspan=1>Direct Costs</td></tr><tr><td>Carbon Release</td><td>Recovery Time Scale</td><td>Climate Costs \\$/tonC</td><td></td><td>Total Costs \\$/tonC</td></tr><tr><td>1,000 Gt.</td><td>10,000 yr.</td><td>\\$8.3k</td><td></td><td>\\$8.3k</td></tr><tr><td></td><td>200,000 yr.</td><td>\\$75.2k</td><td></td><td>\\$75.2k</td></tr><tr><td>5,000 Gt.</td><td>10,000 yr.</td><td>\\$10.2k</td><td></td><td>\\$10.2k</td></tr><tr><td></td><td>200,000 yr.</td><td>\\$121.2k</td><td></td><td>\\$121.2k</td></tr><tr><td colspan=1>Costs Assuming Population Feedback</td></tr><tr><td>Carbon Release</td><td>Recovery Time Scale</td><td>Climate Costs \\$/tonC</td><td>Sea Level Costs \\$/tonC</td><td>Total Costs \\$/tonC</td></tr><tr><td>1,000 Gt.</td><td>10,000 yr.</td><td>\\$33.1k</td><td>\\$25.8k</td><td>\\$59.0k</td></tr><tr><td></td><td>200,000 yr.</td><td>\\$301.0k</td><td>\\$180.3k</td><td>\\$481.3k</td></tr><tr><td>5,000 Gt.</td><td>10,000 yr.</td><td>\\$40.8k</td><td>\\$27.5k</td><td>\\$68.3k</td></tr><tr><td></td><td>200,000 yr.</td><td>\\$484.9k</td><td>\\$265.1k</td><td>\\$750.1k</td></tr>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(\"## Costs\"))\n",
    "fig,axarr = plt.subplots(nrows=3,ncols=2,sharex=\"col\",sharey=\"row\",squeeze=False,figsize=(10,10))\n",
    "\n",
    "myString  = \"<table border 1px>\"\n",
    "\n",
    "myString += \"<tr><td colspan=1>Direct Costs</td></tr>\"\n",
    "\n",
    "myString += \"<tr><td>Carbon Release</td><td>Recovery Time Scale</td>\"\n",
    "myString += \"<td>Climate Costs \\$/tonC</td><td></td>\"\n",
    "myString += \"<td>Total Costs \\$/tonC</td></tr>\"\n",
    "\n",
    "CFeedbackFactor = climBaseParmList[1]\n",
    "oceanAcidTime = climBaseParmList[2]\n",
    "thermostatTime = climBaseParmList[3]\n",
    "warmingTime = climBaseParmList[4]\n",
    "iceMeltingTime = climBaseParmList[5]\n",
    "dT2x = climBaseParmList[6]\n",
    "\n",
    "econRecoveryTimes = [ 10000, 200000 ]\n",
    "\n",
    "climCostFactor = 1  # direct climate costs\n",
    "SLCostFactor = 0\n",
    "\n",
    "for CReleaseGton in CReleaseGtons:\n",
    "\n",
    "    for rIndex, econRecoveryTime in enumerate(econRecoveryTimes):\n",
    "        \n",
    "        times, deltaTs, CAtmFactors, temperatures, seaLevels = \\\n",
    "            climateModel( CReleaseGton, CFeedbackFactor, oceanAcidTime, thermostatTime, warmingTime, \\\n",
    "                         iceMeltingTime, dT2x  )\n",
    "        climCosts, seaLevelCosts, cumClimCost, cumSeaLevelCost = \\\n",
    "                econModel( times, deltaTs, temperatures, seaLevels, \\\n",
    "                      CReleaseGton, climCostFactor, SLCostFactor, econRecoveryTime )\n",
    "\n",
    "        labelString = str(int(CReleaseGton/1000)) + \"k Gton / \" + str(int(econRecoveryTime/1000)) + \"k years\"\n",
    "        ax = axarr[0][0]\n",
    "        ax.plot(times,climCosts,label=labelString)\n",
    "        ax.legend()\n",
    "        ax.set_title(\"Direct Climate\")\n",
    "        ax.set_ylabel(\"Percent GDP\")\n",
    "        ax = axarr[0][1]\n",
    "        ax.semilogx(times,climCosts)\n",
    "        \n",
    "        if(rIndex == 0):\n",
    "            myString += \"<tr><td>\" + \"{:,}\".format(CReleaseGton) + \" Gt.</td><td>\" \n",
    "        else:\n",
    "            myString += \"<tr><td></td><td>\" \n",
    "        myString += \"{:,}\".format(econRecoveryTime) + \" yr.</td><td>\\$\" \n",
    "        myString += \"{:,.1f}\".format( cumClimCost/1000. )\n",
    "        myString += \"k</td><td></td>\"\n",
    "        myString += \"<td>\\$\" + \"{:,.1f}\".format( cumClimCost/1000. ) \n",
    "        myString += \"k</td></tr>\"\n",
    "\n",
    "myString += \"<tr><td colspan=1>Costs Assuming Population Feedback</td></tr>\"\n",
    "myString += \"<tr><td>Carbon Release</td><td>Recovery Time Scale</td>\"\n",
    "myString += \"<td>Climate Costs \\$/tonC</td><td>Sea Level Costs \\$/tonC</td>\"\n",
    "myString += \"<td>Total Costs \\$/tonC</td></tr>\"\n",
    "\n",
    "climCostFactor = 4  # 3 + 1 for feedback + direct\n",
    "SLCostFactor = 15\n",
    "for CReleaseGton in CReleaseGtons:\n",
    "    for rIndex, econRecoveryTime in enumerate(econRecoveryTimes):\n",
    "        \n",
    "        times, deltaTs, CAtmFactors, temperatures, seaLevels = \\\n",
    "            climateModel( CReleaseGton, CFeedbackFactor, oceanAcidTime, thermostatTime, \\\n",
    "                         warmingTime, iceMeltingTime, dT2x  )\n",
    "        climCosts, seaLevelCosts, cumClimCost, cumSeaLevelCost = \\\n",
    "            econModel( times, deltaTs, temperatures, seaLevels, \\\n",
    "                      CReleaseGton, climCostFactor, SLCostFactor, econRecoveryTime )\n",
    "        \n",
    "        labelString = str(int(CReleaseGton/1000)) + \"k Gton / \" + str(int(econRecoveryTime/1000)) + \"k years\"\n",
    "\n",
    "        ax = axarr[1][0]\n",
    "        ax.plot(times,climCosts,label=labelString)\n",
    "        ax.legend()\n",
    "        ax.set_title(\"Climate with Population Feedback\")\n",
    "        ax.set_ylabel(\"Percent GDP\")\n",
    "        ax = axarr[1][1]\n",
    "        ax.semilogx(times,climCosts)\n",
    "        ax = axarr[2][0]\n",
    "        ax.plot(times,seaLevelCosts,label=labelString)\n",
    "        ax.set_ylabel(\"Percent GDP\")\n",
    "        ax.set_xlabel(\"time, years\")\n",
    "        ax.set_title(\"Sea Level\")\n",
    "        ax.set_xticks([0,5e5,1e6])\n",
    "        ax.legend()\n",
    "        ax = axarr[2][1]\n",
    "        ax.semilogx(times,seaLevelCosts)\n",
    "        ax.set_xlabel(\"time, years\")\n",
    "\n",
    "        if(rIndex == 0):\n",
    "            myString += \"<tr><td>\" + \"{:,}\".format(CReleaseGton) + \" Gt.</td><td>\" \n",
    "        else:\n",
    "            myString += \"<tr><td></td><td>\" \n",
    "        myString += \"{:,}\".format(econRecoveryTime) + \" yr.</td><td>\\$\" \n",
    "        myString += \"{:,.1f}\".format( cumClimCost/1000. )\n",
    "        myString += \"k</td><td>\\$\" + \"{:,.1f}\".format( cumSeaLevelCost/1000. ) + \"k</td>\"\n",
    "        myString += \"<td>\\$\" + \"{:,.1f}\".format( ( cumClimCost + cumSeaLevelCost )/1000. ) \n",
    "        myString += \"k</td></tr>\"        \n",
    "        \n",
    "plt.tight_layout()\n",
    "plt.savefig(\"figure_06.pdf\")\n",
    "plt.show()\n",
    "\n",
    "display(Markdown(myString))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Sensitivity to the Economic Recovery Rate"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Markdown(\"## Sensitivity to the Economic Recovery Rate\"))\n",
    "fig,axarr = plt.subplots(nrows=1,ncols=2,sharex=True,sharey=True,squeeze=False)\n",
    "\n",
    "CFeedbackFactor = climBaseParmList[1]\n",
    "oceanAcidTime = climBaseParmList[2]\n",
    "thermostatTime = climBaseParmList[3]\n",
    "warmingTime = climBaseParmList[4]\n",
    "iceMeltingTime = climBaseParmList[5]\n",
    "dT2x = climBaseParmList[6]\n",
    "\n",
    "econRecoveryRange = []\n",
    "econRecoveryPlot = []\n",
    "for i in range(0,10):\n",
    "    econRecoveryRange.append( float((i+1)*10000) )\n",
    "    econRecoveryPlot.append( float((i+1)*10 ) )\n",
    "\n",
    "for CReleaseGton in CReleaseGtons:\n",
    "    cumClimCostList = []\n",
    "    cumSeaLevelCostList = []\n",
    "\n",
    "    for rIndex, econRecoveryTime in enumerate(econRecoveryRange):\n",
    "    #    print(EconRecoveryTime)\n",
    "        times, deltaTs, CAtmFactors, temperatures, seaLevels = \\\n",
    "            climateModel( CReleaseGton, CFeedbackFactor, oceanAcidTime, thermostatTime, \\\n",
    "                         warmingTime, iceMeltingTime, dT2x  )\n",
    "        climCosts, seaLevelCosts, cumClimCost, cumSeaLevelCost = \\\n",
    "            econModel( times, deltaTs, temperatures, seaLevels, \\\n",
    "                      CReleaseGton, climCostFactor, SLCostFactor, econRecoveryTime )\n",
    "        cumClimCostList.append( cumClimCost/1000. )\n",
    "        cumSeaLevelCostList.append( cumSeaLevelCost/1000. )\n",
    "\n",
    "    labelString = str(int(CReleaseGton/1000)) + \"k Gton\"\n",
    "\n",
    "    ax = axarr[0][0]\n",
    "    ax.plot(econRecoveryPlot,cumClimCostList,label=labelString)\n",
    "    ax.legend()\n",
    "    ax.set_title(\"Climate Costs\")\n",
    "    ax.set_ylabel(\"\\$k/ton C\")\n",
    "    ax.set_xlabel(\"Econ. Time, kyr\")\n",
    "\n",
    "    ax = axarr[0][1]\n",
    "    ax.plot(econRecoveryPlot,cumSeaLevelCostList,label=labelString)\n",
    "    ax.legend()\n",
    "    ax.set_title(\"Sea Level Costs\")\n",
    "    ax.set_xlabel(\"Econ. Time, kyr\")\n",
    "\n",
    "plt.savefig(\"figure_s02.pdf\")\n",
    "plt.show()           "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monte Carlo Uncertainty Propagation\n",
    "\n",
    "In which uncertainty is applied to the input parameters, to derive the sensitivity of model output.  \n",
    "\n",
    "The code in the following module begins by running the base case. Then for as many iterations as specified in the variables numIters, the code applies variation to each model parameter in turn, drawing from a log-uniform distribution between the low and high values specified in list parmRanges, keeping the other model parameters at their base values.  Finally the code varies all parameters simultaneously and independently.  The results are tabulated and plotted.   \n",
    "\n",
    "The results show that of the geophysical parameters, the climate sensitivity has the strongest impact on the uncertainty in the cost values.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "<table border 1px><tr><td>n=10000</td><td colspan=3>Parameter Range</td><td colspan=2>Climate Cost, k\\$/tonC</td><td></td><td colspan=2>Sea Level Cost, k\\$/tonC</td><td></td><td colspan=2>Total Cost, k\\$/tonC</td><td></td></tr><tr><td></td><td>low</td><td>high</td><td>base</td><td>median</td><td>sigma</td><td>%</td><td>median</td><td>sigma</td><td>%</td><td>median</td><td>sigma</td><td>%</td></tr><tr><td>Base</td><td></td><td></td><td></td><td>70.9</td><td></td><td></td><td>27.3</td><td></td><td></td><td>98.2</td><td></td><td></td></tr><tr><td>CReleaseGton</td><td>1,000.0</td><td>5,000.0</td><td>2,236.1</td><td>71.0</td><td>7.4</td><td>11</td><td>27.4</td><td>2.6</td><td>10</td><td>98.4</td><td>10.0</td><td>10</td></tr><tr><td>CFeedbackFactor</td><td>0.1</td><td>0.5</td><td>0.2</td><td>71.0</td><td>3.1</td><td>4</td><td>27.4</td><td>1.2</td><td>4</td><td>98.3</td><td>4.2</td><td>4</td></tr><tr><td>oceanAcidTime</td><td>2,000.0</td><td>8,000.0</td><td>4,000.0</td><td>70.8</td><td>1.3</td><td>2</td><td>27.3</td><td>0.6</td><td>2</td><td>98.1</td><td>1.9</td><td>2</td></tr><tr><td>thermostatTime</td><td>100,000.0</td><td>400,000.0</td><td>200,000.0</td><td>70.9</td><td>4.3</td><td>6</td><td>27.3</td><td>3.1</td><td>11</td><td>98.2</td><td>7.3</td><td>8</td></tr><tr><td>warmingTime</td><td>100.0</td><td>1,000.0</td><td>316.2</td><td>70.9</td><td>0.0</td><td>0</td><td>27.3</td><td>0.0</td><td>0</td><td>98.2</td><td>0.1</td><td>0</td></tr><tr><td>iceMeltingTime</td><td>300.0</td><td>3,000.0</td><td>948.7</td><td>70.9</td><td>0.0</td><td>0</td><td>27.3</td><td>0.7</td><td>3</td><td>98.2</td><td>0.7</td><td>1</td></tr><tr><td>dT2x</td><td>1.5</td><td>4.5</td><td>2.6</td><td>71.4</td><td>23.6</td><td>32</td><td>27.5</td><td>9.1</td><td>32</td><td>98.9</td><td>32.6</td><td>32</td></tr><tr><td>climCostFactor</td><td>1.0</td><td>4.0</td><td>2.0</td><td>70.6</td><td>30.1</td><td>39</td><td>27.3</td><td>0.0</td><td>0</td><td>97.9</td><td>30.1</td><td>29</td></tr><tr><td>SLCostFactor</td><td>1.0</td><td>15.0</td><td>3.9</td><td>70.9</td><td>0.0</td><td>0</td><td>27.1</td><td>26.8</td><td>74</td><td>97.9</td><td>26.8</td><td>25</td></tr><tr><td>econRecoveryTime</td><td>10,000.0</td><td>200,000.0</td><td>44,721.4</td><td>70.9</td><td>52.8</td><td>63</td><td>27.3</td><td>16.0</td><td>53</td><td>98.3</td><td>68.7</td><td>60</td></tr><tr><td>AllGeo</td><td></td><td></td><td></td><td>70.3</td><td>25.0</td><td>34</td><td>26.5</td><td>8.9</td><td>32</td><td>96.8</td><td>33.5</td><td>33</td></tr><tr><td>All</td><td></td><td></td><td></td><td>67.6</td><td>87.5</td><td>91</td><td>24.0</td><td>46.7</td><td>114</td><td>99.8</td><td>118.5</td><td>87</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "numIters = 10000\n",
    "\n",
    "def modelWrapper( parmList ):\n",
    "\n",
    "    CReleaseGton = parmList[0]\n",
    "    CFeedbackFactor = parmList[1]\n",
    "    oceanAcidTime = parmList[2]\n",
    "    thermostatTime = parmList[3]\n",
    "    warmingTime = parmList[4]\n",
    "    iceMeltingTime = parmList[5]\n",
    "    dT2x = parmList[6]\n",
    "    climCostFactor = parmList[7]\n",
    "    SLCostFactor = parmList[8]\n",
    "    econRecoveryTime = parmList[9]\n",
    "    \n",
    "    times, deltaTs, CAtmFactors, temperatures, seaLevels = \\\n",
    "        climateModel( CReleaseGton, CFeedbackFactor, oceanAcidTime, thermostatTime, \\\n",
    "                    warmingTime, iceMeltingTime, dT2x )\n",
    "\n",
    "    climCosts, seaLevelCosts, cumClimCost, cumSeaLevelCost = \\\n",
    "        econModel( times, deltaTs, temperatures, seaLevels, CReleaseGton, \\\n",
    "                  climCostFactor, SLCostFactor, econRecoveryTime )\n",
    "\n",
    "    return cumClimCost, cumSeaLevelCost\n",
    "\n",
    "cumClimCost, cumSeaLevelCost = modelWrapper( climBaseParmList )\n",
    "baseCosts = [cumClimCost, cumSeaLevelCost]\n",
    "\n",
    "costValues = []  # structure cost[iCost][iParm][iter] where iCost is climate or sea level\n",
    "for costType in range(0,3):\n",
    "    costValues.append([])\n",
    "    for index in range(0,numParms+2):  # 2 additional for the var of all geophys params, all\n",
    "        costValues[costType].append([])  \n",
    "    \n",
    "for iter in range(0,numIters):\n",
    "    \n",
    "    # first tweak each parameter individually, keeping others constant\n",
    "    for iParm in range(0,numParms):  # loop over parameters that need to be tweaked\n",
    "        parmList = climBaseParmList.copy()\n",
    "        s = parmLogRanges[iParm][0] + random.random() * ( parmLogRanges[iParm][1] - parmLogRanges[iParm][0] )\n",
    "        parmList[iParm] = math.exp(s)   # resetting only one\n",
    "        cumClimCost, cumSeaLevelCost = modelWrapper( parmList )\n",
    "        costValues[0][iParm].append( cumClimCost ) \n",
    "        costValues[1][iParm].append( cumSeaLevelCost )\n",
    "        costValues[2][iParm].append( cumClimCost + cumSeaLevelCost )\n",
    "\n",
    "    # tweak just the geophysical parameters\n",
    "    parmList = climBaseParmList.copy()\n",
    "    for iParm in range(0,numParmsGeo):\n",
    "        s = parmLogRanges[iParm][0] + random.random() * ( parmLogRanges[iParm][1] - parmLogRanges[iParm][0] )\n",
    "        parmList[iParm] = math.exp(s)\n",
    "    cumClimCost, cumSeaLevelCost = modelWrapper( parmList )\n",
    "    costValues[0][numParms].append( cumClimCost )\n",
    "    costValues[1][numParms].append( cumSeaLevelCost )\n",
    "    costValues[2][numParms].append( cumClimCost + cumSeaLevelCost )\n",
    "        \n",
    "    # now tweak them all together\n",
    "    parmList = climBaseParmList.copy()\n",
    "    for iParm in range(0,numParms):\n",
    "        s = parmLogRanges[iParm][0] + random.random() * ( parmLogRanges[iParm][1] - parmLogRanges[iParm][0] )\n",
    "        parmList[iParm] = math.exp(s)\n",
    "    cumClimCost, cumSeaLevelCost = modelWrapper( parmList )\n",
    "    costValues[0][numParms+1].append( cumClimCost )\n",
    "    costValues[1][numParms+1].append( cumSeaLevelCost )\n",
    "    costValues[2][numParms+1].append( cumClimCost + cumSeaLevelCost )\n",
    "\n",
    "means = [[],[],[]]\n",
    "stds = [[],[],[]]\n",
    "relStds = [[],[],[]]\n",
    "maxCost = 0.\n",
    "for iParm in range(0,numParms+2):\n",
    "    for iCost in range(0,3):  # climate, sea level, total\n",
    "        mean = np.mean( costValues[iCost][iParm] )\n",
    "        median = np.median( costValues[iCost][iParm] )\n",
    "        std = np.std( costValues[iCost][iParm], ddof=1 )\n",
    "        means[iCost].append( median )\n",
    "        stds[iCost].append( std )\n",
    "        relStds[iCost].append( std / mean )\n",
    "        max = np.amax( costValues[iCost][iParm] )\n",
    "        if max > maxCost:\n",
    "            maxCost = max\n",
    "\n",
    "#csv output\n",
    "import csv\n",
    "with open('monte_carlo.csv', mode='w') as monte_file:\n",
    "    monte_writer = csv.writer(monte_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n",
    "\n",
    "    monte_writer.writerow(['','Low','High','Base','Median Climate Cost \\$k/ton','sigma','pct', \\\n",
    "                           'Median Sea Level Cost $k/ton','sigma','pct','Median Total Cost','sigma','pct'])\n",
    "    monte_writer.writerow(['Base','','','',baseCosts[0],'','',baseCosts[1],'','',baseCosts[0]+baseCosts[1],'',''])\n",
    "    \n",
    "# individual parameter variations\n",
    "    for iParm in range(0,numParms+2):\n",
    "        rowList = []\n",
    "        rowList.append(parmNames[iParm])\n",
    "        if(iParm < numParms):\n",
    "             rowList.append( parmRanges[iParm][0] )\n",
    "             rowList.append( parmRanges[iParm][1] )\n",
    "             rowList.append( climBaseParmList[iParm] )\n",
    "        else:\n",
    "             rowList.append(\"\")\n",
    "             rowList.append(\"\")\n",
    "             rowList.append(\"\")\n",
    "        for iCost in range(0,3):            \n",
    "            rowList.append( means[iCost][iParm] )\n",
    "            rowList.append( stds[iCost][iParm] )\n",
    "            rowList.append( relStds[iCost][iParm] )\n",
    "        monte_writer.writerow(rowList)    \n",
    "            \n",
    "        \n",
    "# table header        \n",
    "myString  = \"<table border 1px><tr><td>n=\" + str(numIters) \n",
    "myString += \"</td><td colspan=3>Parameter Range</td><td colspan=2>Climate Cost, k\\$/tonC</td><td></td>\"\n",
    "myString += \"<td colspan=2>Sea Level Cost, k\\$/tonC</td><td></td>\"\n",
    "myString += \"<td colspan=2>Total Cost, k\\$/tonC</td><td></td></tr>\"\n",
    "\n",
    "# subheader \n",
    "myString += \"<tr><td></td><td>low</td><td>high</td><td>base</td>\"\n",
    "myString += \"<td>median</td><td>sigma</td><td>%</td>\"\n",
    "myString += \"<td>median</td><td>sigma</td><td>%</td>\"\n",
    "myString += \"<td>median</td><td>sigma</td><td>%</td></tr>\"\n",
    "\n",
    "# base results\n",
    "myString += \"<tr><td>Base</td><td></td><td></td><td></td>\"\n",
    "myString += \"<td>\" + \"{:,.1f}\".format(baseCosts[0]/1000.) + \"</td><td></td><td></td>\"\n",
    "myString += \"<td>\" + \"{:,.1f}\".format(baseCosts[1]/1000.) + \"</td><td></td><td></td>\"\n",
    "myString += \"<td>\" + \"{:,.1f}\".format((baseCosts[0]+baseCosts[1])/1000.) + \"</td><td></td><td></td></tr>\"\n",
    "\n",
    "# individual parameter variations\n",
    "for iParm in range(0,numParms+2):\n",
    "    myString += \"<tr><td>\" + parmNames[iParm] + \"</td>\"\n",
    "    if(iParm < numParms):\n",
    "        myString += \"<td>\" + \"{:,.1f}\".format(parmRanges[iParm][0]) + \"</td>\"\n",
    "        myString += \"<td>\" + \"{:,.1f}\".format(parmRanges[iParm][1]) + \"</td>\"\n",
    "        myString += \"<td>\" + \"{:,.1f}\".format(climBaseParmList[iParm]) + \"</td>\"\n",
    "    else:\n",
    "        myString += \"<td></td><td></td><td></td>\"\n",
    "    for iCost in range(0,2):\n",
    "        myString += \"<td>\" + \"{:,.1f}\".format(means[iCost][iParm]/1000.) + \"</td>\"\n",
    "        myString += \"<td>\" + \"{:,.1f}\".format(stds[iCost][iParm]/1000.) + \"</td>\"\n",
    "        myString += \"<td>\" + \"{:,.0f}\".format(relStds[iCost][iParm]*100.) + \"</td>\"\n",
    "    myString += \"<td>\" + \"{:,.1f}\".format(means[2][iParm]/1000.) + \"</td>\"\n",
    "    myString += \"<td>\" + \"{:,.1f}\".format(stds[2][iParm]/1000.) + \"</td>\"\n",
    "    myString += \"<td>\" + \"{:,.0f}\".format(relStds[2][iParm]*100.) + \"</td>\"\n",
    "    myString += \"</tr>\"\n",
    "    \n",
    "myString += \"</table>\"\n",
    "display(Markdown(myString))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b855f60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axarr = plt.subplots(nrows=numParms+2,ncols=3,sharex=True,sharey=False,squeeze=False,figsize=(5,20))\n",
    "\n",
    "for iParm in range(0,numParms+2):\n",
    "    for iCost in range(0,2):  # climate, sea level\n",
    "        ax = axarr[iParm][iCost]\n",
    "#        ax.set_xticks([0,1,2,3,4])\n",
    "        costs = []\n",
    "        for i in range(0,len(costValues[iCost][iParm])):\n",
    "            costs.append(costValues[iCost][iParm][i]/1e6)\n",
    "        ax.hist(costs,range=(0,maxCost/1e6),bins=20)\n",
    "    ax = axarr[iParm][2]     # total\n",
    "    costs = []\n",
    "    for i in range(0,len(costValues[iCost][iParm])):\n",
    "        costs.append((costValues[0][iParm][i]+costValues[1][iParm][i])/1e6)\n",
    "    ax.hist(costs,range=(0,maxCost/1e6),bins=20)\n",
    "            \n",
    "for iParm in range(0,numParms+2):\n",
    "    ax = axarr[iParm][0]\n",
    "    ax.set_ylabel(\"# sims\")\n",
    "    ax = axarr[iParm][1]\n",
    "    ax.set_title(parmNames[iParm])\n",
    "ax = axarr[numParms+1][0]\n",
    "ax.set_xlabel('Climate \\$10$^6$/ton')\n",
    "ax = axarr[numParms+1][1]\n",
    "ax.set_xlabel('Sea Level \\$10$^6$/ton')\n",
    "ax = axarr[numParms+1][2]\n",
    "ax.set_xlabel('Total \\$10$^6$/ton')\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"figure_s01.pdf\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Marginal Cost Calculation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "CReleaseStep = 100\n",
    "CReleaseSeries    = list(range(1000,5000,CReleaseStep))\n",
    "\n",
    "CFeedbackFactor = climBaseParmList[1]\n",
    "oceanAcidTime = climBaseParmList[2]\n",
    "thermostatTime = climBaseParmList[3]\n",
    "warmingTime = climBaseParmList[4]\n",
    "iceMeltingTime = climBaseParmList[5]\n",
    "dT2x = climBaseParmList[6]\n",
    "\n",
    "climCostFactor   = 4       # economy percent hit per degree C\n",
    "SLCostFactor     = 15      # percent hit for full sea level rise\n",
    "econRecoveryTime = 200000\n",
    "\n",
    "marginalCosts = []\n",
    "for i in range(0,3):\n",
    "    marginalCosts.append([])\n",
    "\n",
    "\n",
    "for index, CReleaseGton in enumerate(CReleaseSeries):\n",
    "    \n",
    "    times, deltaTs, CAtmFactors, temperatures, seaLevels = \\\n",
    "        climateModel( CReleaseGton, CFeedbackFactor, oceanAcidTime, thermostatTime, warmingTime, \\\n",
    "            iceMeltingTime, dT2x  )\n",
    "    climCosts, seaLevelCosts, cumClimCost, cumSeaLevelCost = \\\n",
    "        econModel( times, deltaTs, temperatures, seaLevels, \\\n",
    "            CReleaseGton, climCostFactor, SLCostFactor, econRecoveryTime )\n",
    "    \n",
    "    costs = [ cumClimCost, cumSeaLevelCost, cumClimCost + cumSeaLevelCost ]  # dollars / ton\n",
    "\n",
    "    for i in range(0,3):\n",
    "        costs[i] *= CReleaseGton * 1e9    # back to dollars\n",
    "\n",
    "    if index > 0:\n",
    "        for index, cost in enumerate(costs):\n",
    "            marginalCost = ( cost - oldCosts[index] ) / ( CReleaseStep * 1.e15 ) # million dollars per ton\n",
    "            marginalCosts[index].append( marginalCost ) \n",
    "\n",
    "    oldCosts = costs.copy()\n",
    "\n",
    "CReleaseSeries.remove(1000)\n",
    "labels = [\"Climate\",\"Sea Level\",\"Total\"]\n",
    "for i in range(0,3):\n",
    "    \n",
    "    plt.plot(CReleaseSeries,marginalCosts[i],label=labels[i])\n",
    "plt.legend()\n",
    "plt.xlabel(\"Gton C Released\")\n",
    "plt.ylabel(\"Marginal Costs, 10$^6$\\$/ton C\")\n",
    "plt.savefig(\"figure_07.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataSet = \"assets_by_sealevel.csv\"\n",
    "altitudes, slopes = [],[]\n",
    "totLists, cumTotLists = [ [], [], [], [] ],     [ [], [], [], [] ]\n",
    "cumTots = [ 0, 0, 0, 0 ]\n",
    "with open(dataSet,\"r\") as ins:\n",
    "    for lineIndex, line in enumerate(ins):\n",
    "        values = line.split(\",\")\n",
    "        if lineIndex == 0:\n",
    "            headers = values[1:6]\n",
    "        elif lineIndex < 71:\n",
    "            values = line.split(\",\")\n",
    "            altitudes.append( float(values[0]) )\n",
    "            slopes.append( float(values[5]))\n",
    "            for i in range(0,4):\n",
    "                value = float(values[i+1])\n",
    "                totLists[i].append( value )\n",
    "                cumTots[i] += value\n",
    "                cumTotLists[i].append( cumTots[i] )\n",
    "        else:\n",
    "            values = line.split(\",\")\n",
    "            for i in range(0,4):\n",
    "                value = float(values[i+1])\n",
    "                cumTots[i] += value\n",
    "    for iLine in range(0,70):\n",
    "        for iVal in range(0,4):\n",
    "            cumTotLists[iVal][iLine] /= cumTots[iVal]\n",
    "fig,axarr = plt.subplots(nrows=1,ncols=2,sharex=True,sharey=False,squeeze=False)\n",
    "ax = axarr[0][0]\n",
    "for i in range(0,4):\n",
    "    header = headers[i].replace(\" (km2)\",\"\")\n",
    "    ax.plot(altitudes,cumTotLists[i],label=header)\n",
    "ax.set_xlabel(\"Sea level rise, meters\")\n",
    "ax.set_ylabel(\"Fraction Lost\")\n",
    "ax.legend()\n",
    "ax = axarr[0][1]\n",
    "ax.plot(altitudes,slopes)\n",
    "ax.set_xlabel(\"Sea level rise, meters\")\n",
    "ax.set_ylabel(\"Mean Slope\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"figure_04.pdf\")                         \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F6XIrWSaz+Oxiphyegtc1L8bvH090Qs5WHSzWsycmVaoQ9PUCZHy8TuJTCo7mzZtz8+ZNAHr06EH9+vWpUaMGq1atArQf0p6entSsWZNatWqxcOFCtmzZwpkzZxg4cCB16tQhJiYGZ2dnHj16BMCZM2fw8PAAtDPJR44cSbt27Rg8eDBJSUl8+OGHNGjQADc3N1auXJluXJGRkRw9epTVq1c/k0DSKzm/YcMGGjZsmFqI8WliGTNmDO7u7tSoUYNp06bp5d9PlXNX8k3i48cEfPQRxpUr4fjBRJ0cMzYxls+OfsYffn/Qq0ov6jjUYfrx6YzZN4ZlbZZhYWSR+UHSEAYGOE7+kHtvj9SOyhoyRCdxKvDVqa+4Fqrb2zMudi5MaTglS/smJiaye/duOnToAMCaNWuws7MjJiaGBg0a0Lt3b/z8/Lh//37qvI+wsDBsbGxYunQp8+fPx90908na+Pj4cOTIEczMzFi1ahXFihXj9OnTxMXF0bRpU9q1a5daAfipbdu20aFDB6pWrYqdnR1nz56lXr16wLMl569evcovv/zC0aNHMTIyYuzYsfz0008MHjyYmTNnYmdnR1JSEq1bt+bixYu4ubll558zUyqBKPlCSsnD6V+QGBZGhZUr0Jia5vqYj2IeMeHABC4GX2Ri/Yl41vBECIGZoRlTD09l5N6RLG+zHGtj62wd16JZMyxee41Hy5ZTrEcPvRZ1VPQvJiaGOnW0hb2bN2/O8OHDAVi8eDFbt24F4N69e9y4cYNq1apx69Yt3nnnHTp37ky7du2yfb5u3bphZmYGaFcovHjxYmpF4PDwcG7cuPFCAvHy8uK9994DoF+/fnh5eaUmkLQl5//66y98fHxo0KBB6ntzdHQEYNOmTaxatYrExEQCAgK4cuWKSiBK0RDu7U3Enj04TJyIafXquT7ezcc3Gb9/PCExISz0WEib8v9NKepQoQNGGiMmHZrEiD0jWNV2FTamWe9rEULgOPlDbvfsxaOVqygx+cNcx6uQ5SsFXXvaB5LWwYMH2bdvH8ePH8fc3BwPDw9iY2OxtbXlwoUL7Nmzh2+//ZZNmzaxZs2aF45paGhIckoV59jYZ/vcni8Rv2TJEtq3b//S+EJCQti/fz+XL19GCEFSUhJCCObOnZvu8YYMGcLs2bOfOcbt27eZP38+p0+fxtbWFk9Pzxfi0gXVB6LkuXj/+wR++T/M3OtTfPiwXB/v2INjvLX7LeKS4vihww/PJI+nWpdvzTctv8E3zJfhfw4nJCYkW+cwdXGhWI8ePF6/nnj/+7mOWSlYwsPDsbW1xdzcnGvXrqUuP/vo0SOSk5Pp3bs3X375JWdThnRbWVkRERGR2t7Z2RkfHx8Afv3115eep3379ixfvpyEhAQA/v33X6Kiop7ZZ8uWLQwePJg7d+7g5+fHvXv3qFChQrqLVrVu3ZotW7YQFKSt/BQaGsqdO3d48uQJFhYWFCtWjMDAQHbv3v1CW11QCUTJUzIpiQdTtX95lp7zVa5nm2/+dzNj942llGUpNnbaSE37mi/dt4VTC5a2XsrdJ3cZtmcYwdHB2TqXw4R3wcCA4IVqcmFR06FDBxITE3Fzc+Ozzz6jcePGANy/fx8PDw/q1KmDp6dn6l/6np6ejB49OrUTfdq0aUyYMIHmzZtjkMHP9IgRI6hevTr16tWjZs2ajBo16oXRWV5eXvTs2fOZ13r37s3GjRtfOF716tX53//+R7t27XBzc6Nt27YEBARQu3Zt6tatS40aNRg2bFjq6ok6J6Usso/69etLpWAJXrVKXqnmIh//tjVXx0lMSpTzTs2TNdfWlKP3jpYRcRFZbns64LRsuKGh7PRrJxkQGZCt8wYuXCivVHOR0RcvZTdkRUp55cqV/A5BeU56/yfAGZmFz1h1BaLkmdgrVwhevASrdu0o1qN75g1eIjohmvcPvs+6K+vo79KfJa2WYGlsmeX27iXdWdl2JaGxoXj+4Yl/hH+W2xYfMQIDOzvtyoVqcqHyilMJRMkTyXFx3J88GUMbG0p+MT3Hs82DooPw/MOTv/3/ZmrDqXzc6GMMNdkfC1LHsQ7ft/ueiPgIhu4Zyp0nWVt4zMDSEvvx44g+fZrIAweyfV5FKUpUAlHyRPCCBcTf9KXUrFkY2trm6BjXQq/R//f+3HlyhyWtljDQdWCuYqphX4M17dcQlxjH0D+GcivsVpba2fbpg3GFCgTNm49M6QxVlFeRSiCK3kUdO0bouh+xHTgQy+bNcnSMv+/9zeDdgxEIfuz4Iy2cWugktmp21fihww9IJEP3DOV66PVM2wgjIxwnfUD87duEpYznV5RXkUogil4lhYXx4KOPMa5YEcdJH2S7vZSSDVc28O6Bd6lQrAIbO2+kml01ncZYyaYSP7T/AUONIcP/HM6VkMzXKrNs1Qpzd3eCl35LUmSkTuNRlMJCJRBFb6SUPJwxg8SQEErPm4smZTZuViUmJzLr5Cy+Ov0VHk4e/ND+BxzNHfUSq3MxZ9Z2WIuFoQUj9ozgQvCFDPcXQuA4ZTJJISGEfP+9XmJSlIJOJRBFb57s3MmTXbtxGD8esxo1stU2Mj6Sd/a/w8/Xf8azhicLWy7E3MhcT5FqlbUqy9oOa7ExtWHknyPxCfTJcH+zWrWw7tyZ0LXrSHj4UK+xKbqji3LuGVm7di3jx4/PcJ+DBw9y7Nix1OcrVqzgxx9/1GkceUElEEUvEh484OGMLzGrV4/ib4/IVtuAyAAG/zGY4w+O83mTz/nA/QM0Im9+VEtZlmJth7WUsCjBmH1jOBFwIsP9Hd5/H5KSCP5mcZ7Ep+SeLsq559bzCWT06NEMHjw4z+PILZVAFJ2Tyck8mPoRJCVRem72ZptffnSZ/r/352HkQ5a3WU6fqn30GGn6HM0dWdN+DWUsyzBu3zgO+x9+6b7GTmWwfestwrdtI7YAL/yjpC9tOfcFCxZQs2ZNatasyaJFiwDw8/PDxcWFIUOG4ObmxhtvvJF6xfKyEu5p7dixg0aNGlG3bl3atGlDYGAgfn5+rFixgoULF1KnTh0OHz7M9OnTmT9/PgDnz5+ncePGuLm50bNnTx4/fgyAh4cHU6ZMoWHDhlStWpXDh1/+c5lXVAJRdC70h7VEnzpFiU8+wdjJKcvt9t7Zy9A/hmJqaMr6TutpUrqJHqPMmL2ZPWvar6GSTSUmHJjA/rv7X77vqJForK0JmjsvDyNUcutpOfdatWrh4+PDDz/8wMmTJzlx4gTfffcd586dA+D69euMHDmSixcvYm1tzbJly7J8jmbNmnHixAnOnTtHv379mDt3Ls7OzowePZr333+f8+fP07x582faDB48mK+++oqLFy9Sq1Ytvvjii2diPnXqFIsWLXrm9fyiEoiiU7HXrxO8aBFWbdtQrFfPzBug7Wxfc3kNEw9OpKpdVX7q9BOVbCrpOdLM2Zra8l2773C1c+WDgx+wxy/9VQkNihXDYewYoo4dI/LwiwXvlILlaTl3d3d3ypUrx/Dhwzly5Ag9e/bEwsICS0tLevXqlfoXftmyZVNrSQ0aNCjdooYv4+/vT/v27alVqxbz5s3jn3/+yXD/8PBwwsLCeP311wEYMmQIhw4dSt3eq1cvAOrXr4+fn1923rZeqASi6ExyXBwPJn2IplgxSs6YkaXZ5glJCUw/Pp2FPgvp4NyB1e1WU9yseB5EmzXFTIqxsu1K3BzcmHxoMjt8d6S7n23//hiVLastcVIAlhpVXu5pH8j58+dZsmQJxsbGGZalef7n+OnzjEq4P/XOO+8wfvx4Ll26xMqVK3NdUt3ExATQDgTI7hK5+pDlGhBCCBOgN+Cctp2Ucobuw1IKo+CFi4i7cYOyK1dkabZ5eFw4Hxz8gJMPTzLKbRRj64zNs87y7LA0tmR5m+W8u/9dPjnyCYnJifSs8uzVlTA2xvGDidx/733Ct23Dpnfv9A8WHw33fSDUFzRGYPD0Yax9aAz/+z719Qz20RhADsvCKP9p0aIFnp6eTJ06FSklW7duZf369QDcvXuX48eP06RJk9R1yuG/Eu4dO3Z8aQn38PBwypQpA8C6detSX7eysuLJkycv7F+sWDFsbW05fPgwzZs3Z/369alXIwVRdooIeQPhgA8Qp59wlMIq6sQJQteuxaZ/Pyyz8AN/78k9xv41Fv9If2Y2m0m3St3yIMqcMzcyZ2nrpbx38D0+P/Y58Unx9HXp+8w+Vu3bY1a7NsGLvsG6Y0c05uYQEQj3TsDdlMfDi5Csy78cRfpJxsAoJUG9LBGlfNU8187WGRqNeuWSUr169fD09KRhw4aAtux63bp18fPzw9XVlXXr1jFq1CiqVKnCmDFjAJg2bRrDhw9n1qxZNGrUKN3jTp8+nT59+lCmTBkaN27M7du3AejatStvvPEG3t7eLFmy5Jk269atY/To0URHR1OxYkV++OEHPb7z3BFZrSgqhLgspXz5YgsFkLu7uzxz5kx+h1HkJYWHc6t7DzSmplTY+lumEwbPBZ1jwv4JJJPMIo9FuJfMfF3pgiI+KZ4PDn7AQf+DfOj+IYNrpBl6mZxM9IFt3Bn3CfZtnHGo8gAe+2m3GZpCGXco1wjKNgZHV0BCUgIkxac8EtI8T/N6cuJz+zy/b8rz5HTaJqXTNjmdtkkJkBADCVEw5hiUyN68nay6evUqrq6uejm2Pvj5+dGlS5fUNdGLovT+T4QQPlLKTH8xs3MFckwIUUtKeSm7AaYJqgPwDWAAfC+lnPPc9onACCARCAaGSSnvpGwbAnyasuv/pJTrUPJdzIULBEybTuKjRzh7bcw0efx+63c+O/oZpS1L823rbylvXT6PItUNYwNjFngsYMrhKcw7M4/4kBuMEDZw9yTcO4l5bBhWTraEHLyNbW03DBu8DeUaQ0k3MDTO7/AzFhEIX1eDqzv1lkCUoiU7CaQZ4CmEuI32FpYApJQyS6u0CyEMgG+BtoA/cFoIsV1Kmbbw0DnAXUoZLYQYA8wF+goh7IBpgDsgAZ+Uto+zEb+iQ0lhYQQtWEjY5s0YOjritPgbzGrVeun+UkpWXFjBsgvLcC/hzqKWiyhmUiwPI9aByGC4dxKju8eZe+8Cn8TH8M3tbcQ/DmeMYQlE9W5QtjGOPcoSMWgcwf7VKfV2xjOSCxSrElC2IVzbAR76X6/84axZxF3V7dwZE1cXSn78sc6O5+zsXKSvPnIrOwmkYy7P1RC4KaW8BSCE+BnoDqQmECll2gUWTgCDUr5vD+yVUoamtN0LdAC8chmTkk1SSsK3eRM0bx5J4eHYDRmC/fjxGFhavLRNfFI8nx/7nN9v/U63St2Y3mQ6RgZGeRh1DkgJj248238R6qvdZmCMYel6zKo8AOPEeyznDPE1BzKh3gSEEBgDtv368djLC7u3BmFSuXK+vpVscekCez/T3nqzdc7vaJQCLssJ5OmtpFwoA9xL89wfSL/nSWs48HQl+PTalsllPEo2xd24wcMvZhB95gxmdepQcvo0TF1cMmzzOPYx7x14j7NBZ3mn7ju8XevtHC8mpVeJcfDgnDZR3Dup/RoTqt1mZqe9DVVvMJRrAqXrgKEJBsAXMhnjEzNZfXk1cUlxTG4wGSEE9uPGEr5tG0Hzv6bsiuX5+tayxTUlgVz7HZqM0+updHmloOSPTBOIEOKIlLKZECIC7e2jtL/9UkppncVzpfepkW4PvhBiENrbVU+H82Sn7UhgJEC5cuWyGJqSkeToaB4tX07ID2sxsLCg5JczsOndG6HJeMjtrfBbjNs3jqDoIOa9Po8Ozh3yKOIsiArRJop7J7T9Fw/OQVLK4MLilaFaJ22Hd7km2ucvSXoaoeHTxp9ibGDMhqsbSEhO0K6SaGtL8VEjCf56AVEnTmLROKO/lQoQu4rgWEPbD6LnBKIUfpkmEClls5SvVrk8lz9QNs1zJ+DB8zsJIdoAnwCvSynj0rT1eK7twZfEuwpYBdpRWLmM+ZUXsX8/gf+bScKDBxTr1QvHSR9gaGeXabuTASd5/+D7GGmMWNNhDbUdaudBtC8hJYTeSrkVdVybOB79q92mMdJeUTQaqR0dVbYRWDpk6/BCCCY3mIyxgTFrLq8hLimO6U2mY/fWWzze6EWEdw74AAAgAElEQVTQ3Lk4b9mcacItMFy7wKF52j6fbP5bKK+W7EwkNAXGou1Ml8BhYIWUMqtTK08DVYQQFYD7QD9gwHPnqAusBDpIKYPSbNoDzBJCPJ2d1g74KKuxK9mXcP8+D2fOInL/fkyqVKb8hvWYu2dtuO3WG1uZcXwG5a3Ls7T1Upyssl4PS6fuHIfjS7UJIypY+5qpjTZJ1O6XcjuqLhhlb52S9AgheK/ee5gYmLD8wnLik+KZ2Wwmju+/x4PJU3jy++8U69o11+fJEy5d4O+v4N/d2tt2ivIS2elE/xGIAJ7OeukPrAeyVC5VSpkohBiPNhkYAGuklP8IIWYAZ6SU24F5gCWwOeU++V0pZTcpZagQ4ku0SQhgxtMOdUW3ZHw8IevW8WiZ9r6944eTsBs8GGGUead3skxm8dnFrL68mialmjDfYz7Wxlm9w6ljl3+FraO1/ReV22iTRrkmYF8V9HQlIIRgbJ2xGBsY883Zb0hITmBOx9mYrl1H0MKFWLVrhyalFEWBVrIW2JTT3sZSCUTJQHYSSDUpZdr7EAeEEBkv2/YcKeUuYNdzr32e5vs2GbRdA6zJzvmU7Ik6dYqHM2YQf9MXyzatKfnxxxiVLp2ltjGJMXxy5BP23tlLn6p9+KjRRxhp8mmk1fFlsOcj7S2p/l5gnvktN10aUWsExhpj5p2ZR0JSAl9+8B4Bw0fyeP16io/I3too+UIIcOkKp7+DuAgwye3da6Woys6fYueEEI2fPhFCNAKO6j4kJa8lhoTwYMpU7g4egoyJxWn5MsouXZpp8khITiAgMoCzgWcZvmc4++7sY5L7JD5r/Fn+JI/kZNjziTZ5uHSBwdvyPHk8NbjGYD5t9CkH/Q8yNfonzFo059GKlSQ+LiRTl1y7aGep39ib35EoBVhWRmFdQtvnYQQMFkLcTXlenjRzOJTCRyYnE7ZpM0ELF5IcHU3xkSOxHzMajZkZ8UnxBEYHEhgVqP2a9vuUr49iHiFTBsOZGZqxsOVCWpdrnT9vJjEevMfCpc3QYAR0nKstNJiP+rr0xdjAmGnHprGsaQ2GHonm0bLllPykEAxfLdsIzO3h2k6o2Su/o1EKqKzcwuqi9yiUPBWTGEPA2aNEzV6I4dVbhNVw4tTA5twsdovAfUMIjA4kNPbFLiZLI0tKmJeghEUJqthWoYRFCe1z8xJUs6uGo7ljPrwbIPYJ/DIIbv8NrT6D5h8UmGKAPav0xMjAiE+OfEKdRsWp67URu4EDMHZ2zu/QMqYxAJdOcHmrdo6MYSHou1HyXFaG8eZ2AqGShyLjI5+5SngY/fCZq4jw0Id03BdGRx9JlBn82EXD4ZoBFIuPoUSUNhnUsK+RmhhKWJSgpHlJHM0dsTS2zO+396KIh/DTGxB4Bbovg7oD8zuiF3Sp2AUjjRGzI6fwjY/k/vy5VFia9VXt8o1LFzj7I9w+BFXa5nc0SgGUnU50JZ8lJifiG+b70ltKgdGBRCVEvdDOztSOEmaOtLhuSItfYzENlzzp2BjN6MFMKVmRr80dMTPM/VDWPPfoBmzopZ0UOGATVHnpGIx81965PcZdjNl+/l3e2HeAoON/49ik4K7zAECF18HYEq7uUAlESZdKIIXIF8e/YNvNbanPBQIHMwdKWJSgYrGKNCnd5JkrhxLmJbS3lfwDeDjjS6KOHsW0enVKrpqGmVuWamAWXPdOwcY3QRiA5w4oUz+/I8pUy3ItMZq8iMdn3+HO5xMotnYZTcq8lt9hvZyRqTZxXN8FyQt11qckpSyY5WxeQVldzuNlVAIpJCLjI/nj9h+0Ld+WwdUHU9KiJMXNimc42ik5Lo6Q5d8RsmoVwsiIEp98gu2A/giD/O1czrVru2DLMLAqCYN+heL5v356VjWr3JpzY4ZhO281ixa/zbq2zZlYfyJVbavmd2jpc+kC/2zVJuzyTXJ9OFNTU0JCQihevLhKIvlMSklISAimpqY5PkZWRmF9CbgAUcBsKeX1HJ9NybG9d/YSmxTLkBpDslQWJPLoUR7OmEHCnbtYd+qE49QpGDnmUye3LvmshZ3vQ6naMGBzoSy1UcfzfW7tOMKYowFMqHGBPg/60L1Sd8bVGUcJixL5Hd6zqrTTrlR4badOEoiTkxP+/v4EBwfrIDglt0xNTXFyynmliKxcgdhIKfsIIYyBhYCqsJYPvH29cbZ2xs0+41tPCYFBBM6ZTcTuPzAuX56yq7/HsmnTPIpSj6SEg3Pg7znameV91oFJAezUzwJhYEDJqR8R7+nJT5Fj8GoQh9c1L3bf3s3gGoMZVnMYFkYvL4+fp0yttX0hV3dAu//lenSbkZERFSpU0FFwSn7LykTC+JQaVRIoID/Vrxb/CH98An3oVqnbSy/7ZWIioT/+yK1OnYj8az/274ynwnbvopE8khJhx7va5FFnIPT/udAmj6csGjfCsnVror5fx/vOnnj38MajrAerLq6i02+d+OXaLyTqdO30XHDtAmF3IFAtrKQ8KysJ5BOgDdoKt5v0G46Snh23diAQdKmY/pScmAsXuP3mmwTOmo1Z3bpU3LEdh3HjCkfdpczER8EvA7XDSZtPgu7fQkFfjCqLSnw4ieSEBIIXL6asVVnmvT6PjZ024mztzP9O/o+e3j3Zf3d/rjs6c61aJ0Boa2MpShqZJhApZayUcp6UcmhKLSslD0kp2eG7g4YlG1LKstQz25LCwwmYNh2/fv1JehRCmUULKfvdKozLF651xl8qKgTWdYN/90Dnr6H1ZwVmgqAuGDs7YzdgAGFbfiX2mnZp11oOtVjbYS3ftPwGgAkHJjB0z1AuBV/Kv0AtHbULal1TCUR5VpZqYQkhXIQQU4QQi4UQ36R876rv4BQ4F3SOexH36Fa5W+prUkrCtm3Dt2MnwjZvxm7wW1Tc9TvWHToUnZEtj/1gTTvtbZO+67XlSYog+7FjMLC2JnDOV6lXGkIIWpVrxW/df+PTRp9yO/w2A3YN4MO/P+RexL1MjqgnLl20/xeht/Pn/EqBlGkCEUJMAX5GuyrgKbQl1QXgJYSYqt/wlO2+2zEzNKNNOe0kubibN7k7eAgBUz/CuGxZKvy6hRIffYSBZeHuE3hGwAVY3Q6iHsFgb3AtJOto5IBBsWLYjx9P9IkTRB448Mw2I40RfV36sqvXLka5jeLgvYN029aNuafnEh4XnreBuqbcPlVXIUoaIrP7q0KIf4EaUsqE5143Bv6RUlbRY3y54u7uLs+cOZPfYeRYbGIsLTe1pFW5VsxsNpOwX38jYNo0NBYWOE6ciE2fNwrPKndZ5bsffnlLu/DToF/BMeM114sCmZDAre49ICmJiju2I4yN090vKDqIb89/y7ab27AwsmBkrZH0d+2PiUEe9XUtbwbGFjB8T96cT8k3QggfKWWmK8hl5dMnGUivrneplG2Knhy4d4DIhEi6VepGclwcQV9/jVmtWlTavQvbvm8WveRxcRP81AdsysOIva9E8gAQRkY4Tv6Q+Dt3eOzl9dL9HM0d+eK1L9jcdTNuDm587fM13bd15/dbv5Ms8+BX0bWLdnXHyKDM91VeCVn5BHoP+EsIsVsIsSrl8QfwV8o2RU+8fb0paVGSBiUb8GTHDpJCQ3GYMCFLa5IXKlLC0W/gt7e1qwYO2w3WWVvIqqiwfP11LF57jeBvl2W6ZkhV26qsaLOCVW1XYWVsxdTDUxnw+wBOPzydYbtcc+kCSLj2u37PoxQaWRmF9QdQFfgC7XK0fwLT0a5QuFuv0b3CgqODOf7gOF0rdkUgCFm7FhNXV8wbNczv0HQrORn++Aj2fg41empvW5kWy++o8pwQAsepU0iOjOTRt1mr1NukdBN+6fILM5vNJCQ2hGF7hjH+r/H4hvnqJ8gSNcDWWfWDKKmydA9ESpkspTwhpfxVSrkl5fskIcRQfQf4qnp6W6Jrpa5EHTlC/E1finsOKTqjrAASYmHLUDi5HBqPhd5rXul1J0yrVsWmTx8ee3kRd+tWltpohIZulbqxo8cO3qv3Hj6BPvTa3osvjn/Bo5hHug1QCO1VyK2/ITaPO/GVAim3N9G/0EkUyjOklHj7euPm4EaFYhUI/WEtho6OWHfsmN+h6U5MGGzoDVe2aUtkdJgNRa1PJwcc3n0HjZkZQV/NzVY7U0NThtcazq5eu+jv0p9tN7bR6bdOLD+/nOiEaN0F6NoVkhPUUrcKkLVhvBdf8rgEFLDKb0XDtdBr3Ay7SbeK3Yi9fp2oY8ewHTTopaNzCp0nD+CHjtoO2V7fw2vv5HdEBYZh8eLYjx5F5N9/E3n0aLbb25raMrXhVLx7eNOsTDOWXVhG562d2fLvFt2URnFqCBaO2tpYyisvK3/ylQAGA13TeYToL7RX13bf7RhpjOhQoQOha9chzMywfbNPfoelG0FX4fu2EHYPBm0BtyLyvnTIdvBgjJycCJrzFTIxZx/65azLscBjAes7rsfJ0okvjn9Bnx19OOR/KHelUTQa7VK3N/dpb0Eqr7SsJJCdgKWU8s5zDz/goF6jewUlJCew6/YuPMp6YB4eR/jOndj06oWBjU1+h5Z7d47DmvbaWyBDd0FFj/yOqEDSGBvjOGkScTduELbl11wdq45jHX7s+CMLPBYQnxTPuL/GMeLPEVwJuZLzg7p0hfhI7Rr0yistK2uiD89g2wDdhqMcvX+U0NhQulfqzuONGyExEbvBb+V3WLmSnCxJ/Mcbo20jSS5WlpAeG4k1Lkt8UATxiZL4pGQSkpKJT0wmPuVr6vOU7+MSk0lIkv9tS7N/wtOvqa9J4hOTSEiSlLYxY1rX6thbFq7Oeav27TBzr0/w4sVYd+6EgZVVjo8lhKBt+bZ4lPVg8/XNrLiwgr47+9K5YmferfsupS2zOWS6QgswsdbexqraPsdxKYVfpjPRC7PCOBN94sGJ+AT68GeXHdxp1Q6zBu6UXbo0v8NKl5SS036P2XzmHhf8w1I+3GXKh/1/H/79xJ/MMFzLeVmJYfEfEkbOPwwBNAKMDTUYGWgwSfn69LmxgQYjQw0mBhoMDQQ+dx5ja27MtwPrUb+8rY7eed6IuXQZvz59KD5iOI6TJunsuBHxEay5vIb1V9YjpWSg60BGuI3A2tg66wfZMhxuHYRJ/+psqVul4MjqTHSVQAqQ8LhwWm5qSd9qfRl5qzwPp39B+Z82YF4/Zb1vfx9tqY/ilcC+qvarkVmex/kwPJZfz/qzxcef24+isDA2oEkle8yNDZ79YNdAq4BVNLm/ltvFX+dgrdkYmFg880FvbKDB2FBgbGCAkYF4eWJI3VeDgSbrQ5mvPHjC6A0+PAiL4dPOrgx5zblQDYV+MGUqT3btouKu3zEuW1anx34Y9ZAl55aww3cH1ibWjHIbRb9q/TDKSrn8f7bCZk/w3AXORWDNGeUZOk0gQvsb5ySlzFUpUCFEB+AbwAD4Xko557ntLYBFgBvQT0q5Jc22JOBpTeu7UspuZKKwJZCfr/3MzJMz2dT5F4wHfYDGygrnTb9oP/AS42Gpu3Zhn1QCbMppk4l9VbCv8t/3FvY6LX0el5jEX1eD2HTmHof+DSZZQqMKdrzpXpaOtUpibvzc3dCkBNj+LlzYCPU9odPXYJCVBTB1LzwmgQ82nWff1SC61S7NnN61Xoy3gEoIDMS3Q0csW7TA6ZtFejnHtdBrLDizgOMBx6lsU5mfu/yceX2tuEiYWxEaDNcOwVaKlKwmkCz9FkkppRBiG1A/FwEZAN8CbQF/4LQQYruUMm1v3l3AE0jvej1GSlknp+cvDHb47qCKbRXKXHzIfT8/yiz4+r+/ls+u0yaPvhvAtgI8+vfZh98RSIz572CmNuknFlvnbH2QX3nwhM0+99h27j6PoxMoaW3KWI/KvFHfCWf7lyxQGRcJm4doR+q0/ARafJiv63gUMzNi1VvuLP/bl6//vM61h09YMag+FR0KfgVjoxIlKD58OI+WLiX6zBnM3TP9nc42FzsXVrVbhfdNbz49+ikH7h2gg3OHjBuZWEKlltpFptrPKlLrtChZl50/w04IIRpIKXNacKchcFNKeQtACPEz0B1ITSApI7sQQrxyRRpvh9/m4qOLTHKfxOOv1mFYuhRW7dppN8ZHw6F5UO417UxgIaBkzWcPkJwMT/xTEsqN/77e3AvnN/y3n8YI7Cr+l1Qcqmm/L15Fu/41EB6dgPeF+2w6c4/L959gbKChbY0SvOlelmaV7TO+hRQZDBv7QMBF6LoY6g/R8b9Uzmg0gnEtK1PbyYZ3fz5Ht6VHmd/HjQ41S2XeOJ8VHzaUsM2bCZw9B+fNm/RWRLNLxS4sPb8U75vemScQ0P4s/vsHPLwIpWrrJSalYMtOAmkJjBZC+AFRaNcEkVJKtyy2LwOkvQXmDzTKxvlNhRBngERgjpRyWzbaFnjbfbejERraxVUm7NQcHCdPRhim/PecWgWRgdBn3cv/0tNotLezbMpB5TbPbosJg5Cbaa5YUhLMv39AmsllcWYluCNKczrSnltJpahtW5khbRrQpnE9bC1NM38TIb7a2eURD6HfRqiWhQ+hPNasij0732nGmJ/OMnrDWUa1qMiH7athaFBwZ8FrzM1xnPg+D6ZMJXz7dmx69NDLeQw0BnSt2JXVl1cTFB2Eo7ljxg2qdQSh0V6FqATySspOAsltHY30Pvmy04NfTkr5QAhREdgvhLgkpXyhapwQYiQwEqBcuXI5izSPJctkdvju4LXSr5Hs5Y3GwgKbPm9oN8aGw5GFUKUdlG+SsxOY2YCTu/aRVlICD25f5fSZE9y/cQGHiLtUNQygt9ENTDWREAkcAU6aQ/HKL94SS9uJf98HfnoTZDJ47nzxXAVIaRszNo1qzP92XmXloVtc8A9jSf96OFgV3KG+1l27Erp+A8ELFmLdrh0ac3O9nKd75e58d+k7dvjuYHitl47g17Kw11ZPvrYTWn2il3iUgi07CeQuMBCoKKWcIYQoB5QE7mTcLJU/kHYYiRPwIKsnl1I+SPl6SwhxEKgLvJBApJSrgFWg7UTP6vHz06mHpwiMDmRK+RE82T0Tu4ED/xv3f2wJxIZBq091dr6Y+CT++CeATaf9OX4rBCHK0LxKHd50d6KaawlMDTUQFfziFYv/Kbj8K//l/aed+FXgzjGwcIBBv4F9ZZ3Fqi8mhgZ82aMmdcvZ8PHWS3RefJhlA+vh7lwwS+ULjYYSH03lzsBBhKz5AYfx4/RynvLW5annWA9vX2+G1RyW+Yg1ly6w5yPt1WfxSnqJSSm4spNAlqFdQKoVMAOIAH4FGmSx/WmgihCiAnAf6AdkaSKiEMIWiJZSxgkh7IGmQPaqzRVg229ux8rIihoH7xCenIztWykTByOD4fgybZnzXN4ikFJy/l4Ym874s/PCAyLiEilnZ86kdlXpVc+J0jbPDQe2dNQ+nJs9+3p8NIT6PtfX8i+UbQQ9V4JV4SqP1queE66lrBmzwYd+q07wcSdXhjYtmEN9zevXx6pDB0JWr8amzxsYldDPv3X3yt2Zdmwalx5dws0hkzvULp21CeTaTmg6QS/xKAVXdhJIIyllPSHEOQAp5eOUZW2zREqZKIQYj3ZNEQNgjZTyHyHEDOCMlHK7EKIBsBWwBboKIb6QUtYAXIGVKZ3rGrR9ILmoxVBwRCVEse/uPrqXbkfEwl+xat8OY6cy2o1HFkBirHYkUw4FR8Sx7Zy2Q/xGUCRmRgZ0qlWKPu5ONHS2Q5ONORUAGJtDyVraRxHhWsoa7/HNmLT5AjN2XuHcvTDm9KqFhUnBG+rrOOkDIv/6i+AFCyn91ZzMG+RAu/LtmH1yNt43vTNPILbloaSbth9EJZBXTnZ+QxJShuJKACGEA9lc0lZKuQvY9dxrn6f5/jTaW1vPtzsGFJ1PrDT23dlHTGIMXa9akBwRQXFPT+2GsHtw+nuoM0B7iygbEpKSOXg9mE1n7nHgWhCJyZJ65WyY06sWnd1KYWWahYlir5hiZkasHFSfFYd8mb/nOtcCnrB8UH0qOxasob7GTk7YeQ4h5LvvsR00CLNaNTNvlE2Wxpa0Kd+G3bd382GDDzE1zGQAhWtXODBTO3jCqqTO41EKruwMPVmM9urAUQgxE2336iy9RPUK2e67nfIWZTH/bT9mdetiVjvlVtWhlDt0r0/J8rFuBkUwe9dVmszez9s/nuHc3TCGN6vAvokt+G1sU/o1LKeSRwY0GsFYj8qsH96I0Kh4ui89wq5LAfkd1guKjxqFQfHiBM6Zk7vKuhnoUbkHEQkRHLh3IPOdXbpov6qlbl85WU4gUsqfgMnAbCAA6CGl3KyvwF4FDyIfcOrhKTxDq5Pg74/dUE/thkc34dxP4D4cbDIuXxERm4DXqbv0XHaUNgsOsfrIbeqVs+H7we4c/6gVH3VypbJj7mpPvWqaVrZn57vNqFrSirE/nWXm71dISCo4U5MMLC1xePddYnx8iNjzp17O0aBkA0pZlML7pnfmOzu6aucWqaVuXznZuskrpbwGXNNTLK+cnbe0v3Bu+/zQODlh1bq1dsOBmWBoCs0npttOSsnJ26FsOnOPXZcCiE1IpoqjJZ90cqVH3TIFejhqYVGqmBm/jGzCzN+v8N3h21y4F87SgXVxtMrCfJg8YPNGbx7/9BNB8+dj2dIDjYlu/8+fLpW76uIqHkY9pKRFBremni51e2KZds6RWRFYekDJkixfgQghTIUQE4UQvwkhfhVCvC+EKBi/TYWQlJLtvtvpGlONpIv/YDd4MMLAQDuD+5/foPEY7SioNEKj4lm6/wYe8w/Sb9UJ9v4TSK96Tmwb15Q/32/B2y0qquShQ8aGGr7oXpNFfetw6X44nRcf4dTt0PwOCwBhYECJqVNI8Pfn8fr1ejlH90rdkcjUP3Qy5NpVOyn1hn6uiJSCKTt9ID8CNYAlwFK0I6P085P7CrgQfIE7T+7Q/YwGjZUVNr17aTfs/5+2jtVzy7xGxiXSb9Vx5v/5L2VszFjYtzanPmnDrJ61qFPWpkAOOy0qetQtw9Zxr2FpYkj/707w/eFbeut7yA6L117D0sODR8tXkBii+8VBy1qXpX6J+njf9M78/ZZxB8uSaqnbV0x2Ekg1KeVwKeWBlMdIoKq+AivqdvjuwCnCGOtj/2Db9000FhZw9wTc2APN3nvmNoCUkg83X+BmUCQ/DmvIxrcb07OuE2bGah2GvOJS0hrv8U1p4+rI/36/ynivc0TG6WCN8VxynDyZ5Lg4ghcv0cvxu1fqjt8TPy4EX8h4x2eWuo3JeF+lyMhOAjknhGj89IkQohFwVPchFX1xSXHs9tvN8GulQKPBdtAgkBL+mgGWJaDhyGf2X3bQl92XH/JRR1daVHXIp6gVa1MjVgyqz9SOLuy+FED3pUe4GRSRrzGZVKyAbf/+hG3eTOz1f3V+/HbO7TAzNGPbzSyUnnPpAgnR4JuFkVtKkZCdBNIIOCaE8EspqHgceF0IcUkIcVEv0RVRB+8dJPHJE6ofu491x44YlSwJvn/BnaPa0ufG/5VJP3AtiPl/Xqd7ndKMaF4hH6NWQLs87OjXK7FhRCPCYxLovvQoOy9muSKPXjiMG4vGyoqgr77S+a01CyML2pZvyx6/PcQkZnJl4dwcTIqp0VivkOwkkA5ABeD1lEcFoBPQBeiq+9CKru2+2+l+1QIRHYud55D/rj5sykG9/8qf334Uxbs/n8O1pDVzermpfo4C5LVK9ux8pznVSloxfuM5ZuzIv6G+BjY2OIwbS9SxY0T+/bfOj9+jcg8iEyLZf3d/xjsaGmvXSL++G5Ly//aeon/ZmQdyJ6OHPoMsSh7FPOL4vSN0PJ2EecOGmNWoAVe3Q8AF8PhY+0uIttN85I9nMNQIVr5VX/V3FEAli5ny88gmeL7mzJqjtxnw3QmCnsTmSyy2/ftj7OxM0FdzkQkJOj12/RL1KWNZJmtzQly7QEwo3D2m0xiUgqngLoJQRO26tQv3q4mYhURh5+kJyUnakVf21cDtTeC/TnPf4EiWDqhHWTv9lO5Wcs/YUMP0bjX4pl8dLt9/QqfFRzh5S/cjojIjjIxwnDyZ+Nu3efzzLzo99tM5IScCThAQmcnM/MpttHOYrqrbWK8ClUDy2Pab3rx51hRjZ2csPV6Hi79oq9m2+hQ02quMp53mH3dypWll+3yOWMmK7nXK4D2+Kdamhgz4/iTfHcr7ob6WLT0wb9KYR0uXkhQertNjd6vUDYlkx61MhukaW0ClVtqyJgVgqLOiX9mZSGgihBgghPhYCPH504c+gytqrodeh0vXKXMvGjvPIYjkBDgwG0rV0U7E4tlO8+HNVKd5YVK1hBXe45vS1rUEM3ddZexPZ4mI1e3tpIwIISgxdSpJERE8WrZMp8d2snKiQckGWZsT4tJFu7xywHmdxqAUPNm5AvFGu4Z5ItolbZ8+lCza7rudrqclopg1xbp3B591EH4XWn8OQqhO8yLAytSI5YPq8UknV/68Ekj3b4/yb2DeDfU1rVYNm969Cf1pI3G3b+v02N0rdeduxF3OB2eSGKp1BGGgbmO9ArKTQJyklH2llHOllF8/fegtsiImMTmRU6e9afBvMnb9+6MxSIZD87RDHyu1Up3mRYgQgrdbVOSnEY14EpNIj2+Psv1C3g31dZjwLhoTE4LmzdfpcduWb5u1OSHmdlD+NTWc9xWQnQRyTAhRJNfkyAvHHhyjydFQMDTEbuBAOLkSooKg1WdIYNIm1Wle1DSuWJzf321G9VLWvOt1junb/yE+Uf9DfQ3t7Sk+ahSR+/cTdeKEzo5rbmROe+f27PHbQ3RCdMY7u3aF4GvaytJKkZWdBNIM8BFCXBdCXFQTCLPnj4tbaHVRYt25M4aWRnB0EVTtAOUaseygL3/8ozrNi6IS1qZ4jWzMsKYVWHvMj/7fneBhuP6H+toNGYxR6dIEzp6DTErS2RrvVh4AACAASURBVHG7V+pOVEIUf939K+MdXTprv15TtbGKsuwkkI5AFaAd2omDagJhFoXHhWO84yAmCWA/dCgcWwyx4dDqU9Vp/gowMtDwedfqLOlfl6sBT+iy5DB/XH7Iv4ER3A+LITw6gUQdT0LUmJjgOOkD4q5fJ+y333R23Hol6uFk6YS3byZzQoo5Qem6qh+kiMvyeiBSyjtCiNpA85SXDkspM6mwpgD8eeN32p5OQDZww7SMLWxZDjV7c9uwIu/+fER1mr8iutYujUtJK0Zv8GH0Bp8XtpsYarA0McQi5WFpYvDf98bpvJbmq/Z7g9TXzI0NsOrYEbP1Gwj+ZjHWHTthYGmRTlTZoxEaulXuxvLzy3kQ+YDSlqVfvrNLF9j/JTx5ANYZ7KcUWllOIEKICcDbwNM/ZzYIIVZJKfVTBrQIufXbBmpGgtPb4+Dw15AYR1TTKarT/BVUpYQVO95pxmm/x0TEJhAVl0hkXBJRcYkp3yc+81poVDx3Q6NTticRFZ+YpekVQoCFsSE1S7Vi2rmvWTHqM468/uYzSSi9ZFXC2pR65Wz5f3t3Hh5VdT5w/PvOZCEJ2SBhSQICCQmLZRcEZBFEQJbgVtC61KKItQJWq2LVulVrtSBWLVq32qq4/YSAyA4iiOwggiwJBAg7ZA9JSDLn98edQISEJAOZmYT38zz3mbl3zr33ncwkb+4595xjt1X8z0xibCJvbnqTpJQkxnccX3EQbUdYCWT719D9Hhd+WsrbVWdGwrFAD2NMHoCIvIQ1oKImkPNIzUrlV4v3cDKmIfV/dRm8/h6m8208tCiXlGO5/HdsD200v8QE+vnQz8VRlR0Ow8mikrOSjTO5lNl2Jgk1Zcf+3vTdtIiNHftzwD/8F8mqsJxG/chgf0Z2jGJUp2gujw4558o4qn4UPZr0YFbyLO7tcG/FV84R8dAwzrobSxNInVSdBCJA2da4Euc2dR4rZk+n6xEIemossvzvgPCh76+Zt/UwTwzTRnNVPTabnK6yalzFfYp6P0fK0Ot45vC3RE/55Z33RSUOThaWkHuqmNyCYpKP5jJr0wE+XJXKuyv20CoyiFGdohnVKZrmDc/8o5MYl8jjKx5n/ZH1dGvSrfwTl051u+p1yM+AgHDX3rTyWtVpRH8fWC0iT4vI08APwLs1ElUd4TAO6n0+n5P1fYnp1x02fcz+uFt5enkWo7TRXLmJb9OmNPzdXWTPncvJjRt/+ZrdRmigL9FhASQ0CWZYh6a8fUc31jpnu4yo78+UhTvp+/JSbnhzJR+uSuVEbiEDmw8kyDeo8sb00qlud86vuTeoPEaqM16PiHQFemNdeSw3xmysZBeP6tatm1m3bp3Hzr9u9SyC7nyM9N9cS++ETBy7FtK/aBr1w5vw5X29tN1DuY0jL4+UIUPxiWpKi08+QWxV/98xLeMkSZsPMmvjQXYcycHHJvSNj8TR8FO2ZS9n2a+XEehbQTWswwFT20F0Vxjz0UV6N6qmich6Y0wFl5ZnVGswRWPMemPMa8aYad6ePLzBwffeosgOHUcNh20z+ViGk2ML1UZz5Xa2oCAiH3yQgs0/kv313GrtGxMeyO/7xzH/wb58M7EPY/u05OdD2Xy3oSX5xfn87vP3+HbnsfJvRbbZrD4hyYvhVCWdD1WtU2kCEZEVzsccEckus+SISHbNh1g75Rw5wGUr97CvdyuCNr9Fri2El3MGaU9z5TGhoxKp164dR6dMwZHv2rzlbZuGMHloW1Y+OoD//mY0gdKYrdmLuPO9NVz54hKeTtrK5v2Zvxxwsc1wKM6HlEompFK1TqUJxBhzlfMx2BgTUmYJNsaEVOdkIjLE2ZM9WUQeK+f1viKyQUSKReSms167U0R2OZc7z97X22z+99/xK4amiVcjyQt5rXAYD1zXVRvNlceIzUbjyY9RfOgQ6R98cEHHstmEXnERjO30awhI4YWbm9LtsnA+Xr2PxDdWMuAf3zJt0S72nsiDFldBvTAdG6sOqs5w7i9VZdt59rcDb2D1aG8H3CIi7c4qtg/4LfDxWfs2AP6CNS97d+AvIuK1t3Q4CgupN2sp2+IDaLlnLkdMGBnt79RGc+VxgVdcQfCgQRz/9zsUHTl6wccb0WoEgpBlX83027uy9s/X8LcbfkXjEH9eXbyTfi8vY9T0NSSHX4VjxzdQ4r7h7VXNq04byKBytg2txv7dgWRjzG5jzClgBtbw8KcZY1KNMT8CZ1emDgYWGmPSjTEZwEKsOdq90v4vPyIop4isAe1ocHw9nwfdyrM3ddee5sorNPrTw1BUxLFp0y74WE3rN6VH0x7MSpmFwzgIDfRlTPfmzBjXk5WPDuCxoW0oKCrh5b2tsRVk8tJb7zJz4wFOntI50+uCqrSB3CciW4AE5yCKpcseoDqDKUYD+8uspzm31fS+bmWM4dj775HaCFrnbSGNRiTe9ag2miuv4de8OeG3307WV1+Rv3XrBR8vMS6RA7kHWH/kl8OzRIUFML5fLPMm9eWh+35Pkc2f2OPLmPTpJro9v4hJMzaydMfRiz4OmHKfqlyBfIw1aGKS87F06WqMua0a5yrv3++q3kNc5X1FZJyIrBORdceOHatycBdL3sqVBO0/wYpuYfQpSuVkr0dpFhnm9jiUOp+I+8ZjDwvj6N9euuCpdwc2H0h93/rnnSckvlkjfOMHcWPQZj69pzuJnaJZuuMYd72/lh4vLObppK1s3Jfh9mmA1YWpSiN6lrNq6RZjzN4yS3o1z5UGNCuzHgNUdZadKu9rjHnbGNPNGNMtMtK14SIuxN63Xie9PjRvlEl6UCzx19zl9hiUqow9OJjICQ9wcu1achYtuqBjBfgEMLjFYBbuXXj+eULaDEdyDtLDfx8v3vAr1vx5IG/d3pUerRrw8Zp9XP/m91z9yjKmLNzJ7mO5FxSTco/qDKb4H2CiMSbTuR4O/MMY87sqHmIt0FpEWgIHgDHArVXcdz7wQpmG82uByVWN3V0Kdu7EtnYz8/va+VNhGvV//V+wadWV8k5hN99Mxscfc/TlV6jfrx82Pz+XjzUqbhRf7vqSBXsXMCpuVPmF4gdbU91unw0xXfH3sTO4fRMGt29CdkER87YcZuamA/xzyS5eW7yLjjGhJHaKZkTHKCKD/ascS3GJg6ISw6liB6dKrKWo9HnxL9eLTm+zypeuF5U4KCx2nLPNOoY5fYxAfzsTBrSmRcSFj3RcG1W5J7qIbDTGdK5sWyXHuA54FbAD7xlj/ioizwLrjDFJInIF8BUQDhQAh40x7Z37/g543Hmovxpj3q/sfO7uib79j38if+EcPr4LXgpqgm3cEms8IKW8VO53K9h/zz00euQRGv7O9atlYwwjZ46kYUBDPhjyQcUF/zPSGt79gYp/Lw9nFTB780G+2niAbYeysduEjjGh2G1S7h/6U2WSQ1GJA8dFrgXzsQm+dht+PjZ87Tb8fWz42gU/HxsHMwsocRgeG9qG26+8DNt5RjGuTaraE706CWQz0N95F1TprbXfGmO8dppbdyaQrAOH2TtoIEs6Gbp1PMFVN82A2Kvdcm6lLsS+cePI37iJ2Pnz8GnQwOXjvLPlHaZtmMbc6+fSLKRZ+YXW/BvmPgz3r4HIhEqPuetIDjM3HWDtngzsNsHXx4af3Yafj+Bnt5Xzh/3Mup+PDT/nH/pzt5dZP308O76lxy0tY7edNykczirg0S9/5Nudx+jZqiF/v6lDnegoXBMJ5A6saqMvnJtuxroS+K/LUdYwdyUQYwz/uedxrlgxk2fuFj6ObIXPndppStUOhSkp7B6ZSPjoX9PkqadcPs7hvMNc+8W13NvxXu7vdH/5hbIPwpS2MPAp6POQy+fyJsYYZqzdz/NztgHw52HtuKV7s1p92/5FHwvLGPMhcCNwBDgK3ODNycOd/jV/K23WzGd9a6GHbx4+A/7i6ZCUqjL/2FjCR48m49PPKExOdvk4TYKa0DOqJ0nJSThMBbfmhkRZAyvWoaluRYRbujdn3qS+dIgJ4/GvtnDn+2s5lOXacDG1SbUGUwQOAWuAzUCEiPS9+CHVLku2H+GnD2YQeiqfOd1tjIzoAs2u8HRYSlVLxAN/wBYYyJELvK03MTaRg3kHWXt4bcWF2gyHgxsg64DL5/FGzRoE8tHdPXg2sT1r96Rz7dTlfLE+rU7fmlydoUzuBpZj3RH1jPPx6ZoJq3bYczyPSZ9sYHTqCg409YFGRbQe+Jynw1Kq2nzCw4l84A/krVhB5owZLh9nQPMBBPsGMyv5PPOEtB1hPW7/2uXzeCubTbijZwu+mdiHNk2Cefjzzdzz4TqO5hR4OrQaUZ0rkInAFcBeY8zVQGfA/T31vERuYTHjPlxHt8M/0yjzCF9c4WBkSGtocrmnQ1PKJeG33UZQv74cfuFF8jdvdukY9XzqMaTlEBbuXUjuqQr6ckS0hogE63beOqpFRBAzxvXkiWFtWb7rONdOXc7szVXt9lZ7VCeBFBhjCgBExN8Ysx2o/DaKOsgYw8OfbWb38TwmZawnP8SHdQlwXb9nPR2aUi4Tm43ol17Ct1Ej0iZOoji9un2FLYlxiRSUFLBg74KKC7UdDqkr4aRr56gN7Dbh7j6tmDuhD5c1DOKBTzZy/0cbOJFb6OnQLprqJJA0EQkDZgILRWQWVe9JXqe8sTSZeVsP83x7X3x/3MA3nYvpGdCIBlFdPB2aUhfEHhZG9GvTKElP5+DDD2NKSqp9jA4RHWgZ2vL81VhthoMpgZ3zLiDa2iGuUX2+HN+TPw1OYMG2wwx+dTnzfjrs6bAuiiolELHuR5tgjMk0xjwNPIk1H3oFXU7rriXbj/CPhTsZ1SmKvpsWYvxsJHWxk9j5Pk+HptRFEdC+PU2eepK871dx7PXXq72/iJAYm8iGoxvYl72v/EJRnSEkpk7djXU+PnYb918dx+wHrqJxSD3G/289k2ZsJOtk7R7evkoJxFi3Ecwss/6tMSbJOSz7JWPP8TwmzthEu6YhPNe7Mdlz57KtbRE+AX70jb/kcqmqw8JuuonQG2/gxL+mk7NsWbX3H95qODaxMSulgqsQEWuq25TFcCrvwoKtRdo0CWHm/b2ZOLA1c348xKCp37J0+4XPy+Ip1anC+sE51MglqbTR3McmTL+tK/mfzQBHCe/29GVoq2H42V0fR0gpb9TkySfxb9eWg488yqn9+yvfoYzGQY2tPiEpSZQ4KqgGazscigus+dIvIb52Gw8Oimfm/b0JD/Tjrg/W8sgXm8kuqH1XI9VJIFdjJZEU53wgW0SkOvOB1FplG83fuLUL0f6GjE8+IrtFEWnhNhITfu3pEJW66Gz16hHjnHQqbeJEHAXVuxV1VNwoDucdZs3hNeUXaN4LAsIv2aluL48OJemB3vy+fyxfrE9jyNTlrNh13NNhVUtVJpRq7nw6FGgFDMCaD2S487HOK200nzy0Db3iIsj8aiaO3JPMvNKXFsHNuTxCb91VdZNfs2ZE/f0lCrf9zOHnqtfH6epmVxPsF1xxNZbdB+KHWg3pl+hUt/4+dh4Z0oYv7+tFPT87t727midmbiGvsHbM2FiVK5CZAMaYvcCUs+YE2Vuz4Xle2UbzsVe1xJSUkP7uW9giTjGnhQ+Jra+v1WPeKFWZ4P79aXjfeLK+/D8yv/ii8h2c/O3+XNfyOhbvXUzOqZzyC7UdDgVZkPrdRYq2durcPJy5E/pw91Ut+Wj1PoZO+47Vu094OqxKVSWBlP3r2KqmAvFGZRvNX7yhAyJCzuIlFB06xo/d/RCE4a2GezpMpWpc5B/+QFCvXhx+9rlqTYObGGv1CZmfOr/8ArEDwDfwkrkb63zq+dp5Yng7Ph3XExEY8+8feHb2NvJPVf9WanepSgIxFTyv00obzX3tNt66vevpOc3Tp7+Kb1Ax73UJp0fTHjQJauLhSJWqeWK3E/WPV7A3bMiBCRMpycys0n6XR1xObGhsxX1CfAMgbqA1rIlD50YH6N6yAd9M7MPtV17Geyv3MOy171i/N8PTYZWrKgmko4hki0gO0MH5PFtEckQku6YD9ASHw/DQZ5vYfTyP12/tTEy4Nb5//qZN5G/bTV4Xf9JKchgZO9LDkSrlPj7h4cS8OpWio0c58OijmCr8wRcREuMS2XRsE6lZqeUXajMCcg/DgfUXN+BaLNDPh2cTL+eju3tQWOzg5unf87dvtlNY7F1XI1WZE91ujAkxxgQbY3ycz0vXQ9wRpLu9uSyZ+VuPWI3msRGnt6e/9gI2XwdJgzsS6BPIwOYDPRilUu4X0LEjTR6fTN63yzk+fXqV9intE5KUklR+gfhrweZTp8fGclXvuAjmTerDr7s1Y/q3KYz45wq2pGV5OqzTqjuc+yWhoMjB9Z2jGXtVy9PbivbvI3vVFur/KoCvT6Uw6LJBBPrW/pnHlKqusDFjCBk5guP/fJ3cFSsrLR8ZGEnvqN7MSplVfp+QgHBo0cdqB6nDQ5+7KrieL3+7sQPv33UFWflFjHpzJVMW7uRUseer/DSBlOPhwQn84+aOv7i7Kv3VJwHDT2OGkVeUp9VX6pIlIjR95hn8W7fm4MMPU3Sg8nk9RsWN4ujJo6w+tLr8Am2HQ3oKHNt+kaOtO65OaMSCSf1I7BjFa4t3MeqNlfx8yLOtCJpAKlB2HuSSjONkLlhDSEIgXwUcJyooim5NKp3tUak6yxYQQMxr0zDFxaRNehDHqfOPatS/WX9C/EKYmTKz/AIJw6xHvRvrvEIDfZkyuhNv396VozkFjHx9Ba8v2UVxiWeuRjSBVEHmtMk4ikDGjmXVoR8YHmvV6Sp1KfNr0YKmL75AwZYtHHnxxfOXtftxXcvrWLJvCdmnyvmvOaQpxFyh7SBVdG37Jix4sB/Xtm/CKwt2cuO/vif5aAV9bWqQ/hWshMnLIGPOdwQ2C2B+y0AcxsGIVpdEB3ylKhUyaBAN7x5L5iczyJp1nuHbsaqxCksKmbengiHc2wyHQ5shs4IRfNUvNAjy441bu/D6rZ3Zl36S615bwdvLUyhxuK8dSRNIJXKmP05RrhB+970kpSTRMbIjLUJbeDospbxG5KRJBHbvzqG/PE3Bjh0VlmvXsB1xYXEVD21Sh6e6rUnDO0Sx4MF+9IuP5IW52xn91ir2HHfPCMeaQM7D5J3gxFeL8W3gz/4BvUjOTNbGc6XOIj4+RE/5B/bgYNImTKAku/yGXRFhVNwofjz2I7uzdp9boGEsRLbVdhAXRAb78/btXZk6uiM7j+QwdNpyNuyr+c6HmkDOI/9/T1Jw3E6DO+4gafcc/Gx+DG4x2NNhKeV1fCIiiJ72KkUHDnJw8uOYCm7HHdZqGHaxV9wzve1w2Pc95NWuUWm9gYhwfecYFjzYjzFXNOfyqNAaP6cmkIpkHyL9i/nYAnwI+s1YvtnzDf2b9SfUv+Y/FKVqo8AuXWj8yJ/IXbyYE++8U26ZiIAI+kT3YU7KnPL7hLQZDsZxSUx1W1OahNbj6ZHt8fOp+T/vbk0gIjJERHaISLKIPFbO6/4i8qnz9dUi0sK5vYWI5IvIJudStS6wF+DUV8+Qs9+X8JtvYGXGejIKM0iMS6zp0ypVq4XffjvBQ4dwbOqr5P1Qfp+PxLhEjuYfZdWhVee+2LQjhDbXaqxawm0JRETswBtY84q0A24RkXZnFRsLZBhj4oCpwEtlXksxxnRyLuNrNNj0PaR/+Q3YbYSPvZ+klCQa1GtAr6heNXpapWo7EaHpc8/j16IFBx56iKIjR84p0y+mH2H+YeVXY52e6nYJFOa6IWJ1Idx5BdIdSDbG7HbOpT4DOPtf+kTgP87nXwADxQOTbZTMe57M3QGEDh5EXqgf36Z9y7BWw/Cx+bg7FKVqHXv9IGL++RqO/HwOTHoQc1YnQ1+77+k+IVmF5Yzr1HY4lBRC8iI3Raxc5c4EEg2UnVg5zbmt3DLGmGIgC2jofK2liGwUkW9FpE9NBpq5JwRTLDQYdx/fpH5DsaOYxFitvlKqqvxjY4n66/Pkb9zIkVdeOef1UXGjOOU4VX6fkOY9IbDhJTvVbW3izgRS3pXE2bdqVFTmENDcGNMZ+CPwsYiUOxKwiIwTkXUisu7YsWMuBVqYG0BQr57Ua9OGpOQkEsITSGiQ4NKxlLpUhQwdSoM77yDjw/+S9fUv+3a0adCG+PD48vuE2OyQMBR2LoDi8w+RojzLnQkkDWhWZj0GOFhRGRHxAUKBdGNMoTHmBIAxZj2QAsSXdxJjzNvGmG7GmG6RkZEuBRr14gvE/Otf7M7czU8nftK+H0q5qNHDDxPQpQuHnnyKwuTk09tFhMTYRLYc30JKZsq5O7YZAYVZkLrcjdGq6nJnAlkLtBaRliLiB4wBzp4gIAm40/n8JmCJMcaISKSzER4RaQW0BsrpiXTx2Pz9SUpJwi52rmt1XU2eSqk6S3x9iZ46FVtAAGkTJlKSe6aH9LBWw/ARn/Ib01v1B7/6ejeWl3NbAnG2afwBmA/8DHxmjNkqIs+KSOm/+O8CDUUkGauqqvRW377AjyKyGatxfbwxJr0m4y1xlDB792x6R/cmIiCi8h2UUuXybdyI6ClTOJWayqEnnjjdybBhQEP6xPRh9u7ZFDuKz9qpHsRdAzvm6lS3Xsyt/UCMMXONMfHGmFhjzF+d254yxiQ5nxcYY242xsQZY7obY3Y7t39pjGlvjOlojOlijKnxITtXH17N0ZNHtfpKqYsgqEd3Gv3xQXLmzSPjww9Pb0+MS+R4/nG+P/j9uTu1HQG5RyBtrRsjVdWhPdErMDtlNsF+wfRv1t/ToShVJzQYO5b61wzkyMuvcHK9Nf953+i+hPuHl1+N1XoQ2Hx1iHcvpgmkHHlFeSzet5ghLYbgb/f3dDhK1QkiQtSLL+IXHc2BSQ9SfOwYvnZfhrUaxtL9S8/tE1IvFFr106luvZgmkHIsSF1AfnG+Vl8pdZHZg4OJfu01SnJyOPDHhzDFxYyKG0WRo4i5e+aeu0ObYZCxB45uc3+wqlKaQMrx9Z6vuSzkMjpGdvR0KErVOfUS4mn67DOcXLuWo1OnktAggTYN2pRfjZUwDBC9G8tLaQIpx8t9X+alPi/hgVFUlLokhI4cSfitt5D+7ntkL1hAYmwiW09sZVfGrl8WDG4MzbprO4iX0gRSjvB64bSPaO/pMJSq0xo99hj1OnTg0OTHudZ+ecV9QtpfD4e3wJd36zwhXkYTiFLKI2x+fsS8OhXx9SX34acY2Kg3c3bPochR9MuCV9wN/SfD1pnw+hWw+VNtVPcSmkCUUh7jGxVF1D9eoTA5mVuTsjmRf5zvD5zVJ8TuC/0fg/HfWdPefjUO/ncjZOz1TNDqNE0gSimPqt+7N5ETHiBgyVqu3xLAzOSZ5Rds1BZ+Nx+Gvgz7V8ObV8KqN6G8mQ2VW2gCUUp5XMN776V+v36MnneStNVLyCjIKL+gzQ49xsHvf4AWV8H8yfDuIDiy1b0BK0ATiFLKC4jNRtTfX8LWOJIJX55iwabPz79DWDO49TO48V3ISIW3+sKS56GowC3xKosmEKWUV7CHhtLy9TcJzReC/vpvTEklVVMi8Kub4P618KubYfnL8FYf2FvOXOuqRmgCUUp5jXrt2nHw3mHE7spl+9+fqdpOQQ3h+ulw25fWFcj7Q2DOH6Egu2aDVZpAlFLe5cp7/szSjnb4z+fkLF1a9R3jroHfr4Ir74f178MbPWB7OcOjqItGE4hSyquE1Qtjz90D2dvUh4OPPsqp/furvrN/fRjyAoxdBAHhMOMW+Py3kHu0xuK9lGkCUUp5neFtbuDvowzFpoS0CRNxFFSzcTymK4xbBgOegO1fWx0QN/5POyBeZJpAlFJep3d0bxxNIph3ewKFP//MoSefoiQzs3oH8fGDvn+C8SuhUTuYdT98mAjpNTob9iVFE4hSyuv42HwYETuC/4X9TND4sWTPns3O3lex9667SP/4Y4qOVKNKKjIefvs1DJsCBzbAm71g5WtQUlz5vuq8NIEopbzSyNiRFJtilg9qTIvPP6fh2LEUHz7CkWefI7lfP1JHj+HEu+9yam8VhjSx2eCKsfCHNRB7NSx8Et4ZAId+rPk3UoeJqcN1gt26dTPr1q3zdBhKKReNmTOGElPC5yPOdCwsTEkhZ+FCchYspGCbNdGUf3w8wYMGETzoGvwTEs4/FYMxsG0WzP0TnDwBvSdAv0fBN6Cm306tISLrjTHdKi2nCUQp5a0+2f4JL6x+gc9HfE6bBm3Oef1U2gFyFy8ie+FC8tdvAGPwbdbsdDIJ6NgRsVVQ0XIy3boS2fg/aNAKRrwGLfvU8DuqHTSBoAlEqdouqzCLqz+7mtEJo3m0+6PnLVt8/Dg5S5aQs2gReat+gKIifCIjqX/NQIKvuYag7t0RX99zd9y9DGZPtIZE6XIHDHoOAsJq5P3UFppA0ASiVF3w0LKHWHVoFWMSxpDQIIH48HiaBzfHbrNXuE9JTg65y74lZ9Eicpcvx+TnYwsNJbh/f4IHXUNQ797YAspUWZ06CctehFWvQ1AkXPcKtBvphnfnnTSBoAlEqbpgR/oOJq+YzO7M3ZQYa3ysevZ6xIXFEd8gnvjwM0uof+g5+zsKCsj7/ntyFiwkZ+lSHFlZSEAA9fv0IXjQNdTv1w97SIhV+OBGSHrAmgGxzXArkYQ0defb9QqaQNAEolRdUlhSSEpmCjszdlpL+k52ZOwgs/BM/5DGgY2JD48/faUSHx7PZSGX4WPzAcAUFXFy7VpyFi0iZ+Eiio8dA19fgq68kuBrriF44AB8wkOtK5FlfwO7P1z7LHS+w7qT6xKhCQRNIErVdcYYjucfZ2eGlUxKk8uezD0UG6ufh5/Nj9iw2DNXKg3iSQhPIMwvlIIffyR74UJyFi6iaN8+ECGgaxcrmXSLx2/dC5D6HVx2FYyYBhFxHn7H7uGVCUREhgDTe25pUAAACVpJREFUADvwjjHmb2e97g98CHQFTgCjjTGpztcmA2OBEmCCMWZ+ZefTBKLUpamopIjdWbvPXK1k7GRH+g5OFJw4XSYyIPJ0QokPa03rdH/CfthO3qIlFO7YAVijAwe3a0hw0Xz8gvKRqx+FXhOsaXbrMK9LICJiB3YCg4A0YC1wizFmW5kyvwc6GGPGi8gY4HpjzGgRaQd8AnQHooBFQLwx5rwTBmgCUUqVdTz/OLsydv0isaRkplDkKAKsHvCxobF0KYqiy/Yiotfvx2dbCgB+EfUIjjxGcIcY6t3zBhLT1ZNvpUZ5YwLpCTxtjBnsXJ8MYIx5sUyZ+c4yq0TEBzgMRAKPlS1bttz5zqkJRClVmSJHEalZqb9IKjvTd3I03xouJTzH0G9PAL2TfWi2Kwubw0BQCaFd4ggZMxFbaEOw2RGbgN0ONhtit4PYELvNes1uA7vd6uBYtozNZvVTsdutxzLr2Gzn7xBZg6qaQHzcEYxTNFB2XOY0oEdFZYwxxSKSBTR0bv/hrH2jay5UpdSlwtfmS+vw1rQOb80whp3enlGQcfpqZUfGDt7P2MnhQ8lcvqOA7juFTqv2kPXdpBqNzQEYGzikzKNzcZSzfvq5QPTfniSu7601Gp87E0h5qfTsy5+KylRlX+sAIuOAcQDNmzevTnxKKXVaeL1wujftTvem3U9vK3YUsy9nHzvTd/LdjvkUbPgBih3YHIABmwFxGMRwZildd5zZZquoTNlyZbbZTm8/91il5UrLYAw2A/71a74zpDsTSBrQrMx6DHCwgjJpziqsUCC9ivsCYIx5G3gbrCqsixK5UkphtZG0Cm1Fq9BW0HIIDPF0RJ7lzhub1wKtRaSliPgBY4Cks8okAXc6n98ELDFWI00SMEZE/EWkJdAaWOOmuJVSSpXDbVcgzjaNPwDzsW7jfc8Ys1VEngXWGWOSgHeB/4pIMtaVxxjnvltF5DNgG1AM3F/ZHVhKKaVqlnYkVEop9QtVvQvr0umbr5RS6qLSBKKUUsolmkCUUkq5RBOIUkopl2gCUUop5ZI6fReWiBwD9no6DiACOO7pIMqhcVWPxlU9Glf1eFNclxljIisrVKcTiLcQkXVVuSXO3TSu6tG4qkfjqh5vjet8tApLKaWUSzSBKKWUcokmEPd429MBVEDjqh6Nq3o0rurx1rgqpG0gSimlXKJXIEoppVyiCeQiE5H3ROSoiPxUZlsDEVkoIrucj+EeiKuZiCwVkZ9FZKuITPSG2ESknoisEZHNzriecW5vKSKrnXF96pwCwK1ExC4iG0VkjrfE5IwjVUS2iMgmEVnn3OYN37EwEflCRLY7v2c9PR2XiCQ4f06lS7aITPJ0XM7YHnR+538SkU+cvwte8R2rKk0gF98HnDvNzGPAYmNMa2Cxc93dioGHjDFtgSuB+0WknRfEVggMMMZ0BDoBQ0TkSuAlYKozrgxgrJvjApgI/Fxm3RtiKnW1MaZTmds+Pf05AkwD5hlj2gAdsX52Ho3LGLPD+XPqBHQFTgJfeTouEYkGJgDdjDGXY01xMQbv+o5Vzhijy0VegBbAT2XWdwBNnc+bAju8IMZZwCBvig0IBDYAPbA6VPk4t/cE5rs5lhisPywDgDlY0yp7NKYysaUCEWdt8+jnCIQAe3C2q3pLXGfFci2w0hviAqKB/UADrHmZ5gCDveU7VtVFr0Dco7Ex5hCA87GRJ4MRkRZAZ2A1XhCbs6poE3AUWAikAJnGmGJnkTSsXzh3ehV4BHA41xt6QUylDLBARNaLyDjnNk9/jq2AY8D7zmq/d0QkyAviKmsM8InzuUfjMsYcAF4B9gGHgCxgPd7zHasSTSCXGBGpD3wJTDLGZHs6HgBjTImxqhhigO5A2/KKuSseERkOHDXGrC+7uZyinrqFsbcxpgswFKsqsq+H4ijLB+gC/MsY0xnIwzPVaOVytiWMBD73dCwAzjaXRKAlEAUEYX2eZ/Pq22Q1gbjHERFpCuB8POqJIETEFyt5fGSM+T9vig3AGJMJLMNqowkTkdIpl2OAg24MpTcwUkRSgRlY1Vivejim04wxB52PR7Hq87vj+c8xDUgzxqx2rn+BlVA8HVepocAGY8wR57qn47oG2GOMOWaMKQL+D+iFl3zHqkoTiHskAXc6n9+J1f7gViIiWHPO/2yMmeItsYlIpIiEOZ8HYP1i/QwsBW7yRFzGmMnGmBhjTAusao8lxpjfeDKmUiISJCLBpc+x6vV/wsOfozHmMLBfRBKcmwYC2zwdVxm3cKb6Cjwf1z7gShEJdP5ulv68PP4dqxZPN8LUtQXrS3oIKML6r2wsVv35YmCX87GBB+K6Cuty+Edgk3O5ztOxAR2Ajc64fgKecm5vBawBkrGqHfw99Hn2B+Z4S0zOGDY7l63An53bveE71glY5/wsZwLhXhJXIHACCC2zzRviegbY7vze/xfw94bvWHUW7YmulFLKJVqFpZRSyiWaQJRSSrlEE4hSSimXaAJRSinlEk0gSimlXKIJRKkqEpHcapTtLyK9yqyPF5E7nM9/KyJRLpw/VUQiqrufUjXFp/IiSikX9Adyge8BjDHTy7z2W6x7/726l7FSldEEotQFEJERwBOAH1Zntd8AAcB4oEREbgMewOppnIs1km434CMRyccacfVnrGG9j4tIN+AVY0x/EWmI1TE1EqtzmZQ5721Yw4H7YQ2K+XtjTEnNv2OlztAqLKUuzArgSmMNIDgDeMQYkwpMx5rXoZMx5rvSwsaYL7B6a//G+Vr+eY79F2CF89hJQHMAEWkLjMYaVLETUIKVuJRyK70CUerCxACfOgfk88OaE+Ni6QvcAGCM+VpEMpzbB2JNjrTWGkaJADw4CKa6dGkCUerC/BOYYoxJEpH+wNMuHKOYM7UB9c56rbyxhgT4jzFmsgvnUuqi0SospS5MKHDA+fzOMttzgOAK9jn7tVSsKwqAG8tsX46zakpEhmINTgjW4H83iUgj52sNROQyF+NXymWaQJSqukARSSuz/BHriuNzEfkOazrSUrOB60Vkk4j0Oes4HwDTna8FYI3KOs15jLIN4c8AfUVkA9aw7fsAjDHbsBruF4jIj1izODa92G9WqcroaLxKKaVcolcgSimlXKIJRCmllEs0gSillHKJJhCllFIu0QSilFLKJZpAlFJKuUQTiFJKKZdoAlFKKeWS/wciNbvC5BgdeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataSet = \"assets_by_latitude.csv\"\n",
    "latitudes = []\n",
    "stuffByLats = [  [], [], [], [], []  ]\n",
    "cumStuffs = [ 0, 0, 0, 0, 0 ]\n",
    "with open(dataSet,\"r\") as ins:\n",
    "    for lineIndex, line in enumerate(ins):\n",
    "        values = line.split(\",\")\n",
    "        if lineIndex == 0:\n",
    "            headers = values[1:5]\n",
    "        else:\n",
    "            values = line.split(\",\")\n",
    "            for i in range(0,5):\n",
    "                value = float(values[i])\n",
    "                stuffByLats[i].append( value )\n",
    "                cumStuffs[i] += value\n",
    "for iStuff in range(0,4):\n",
    "    for iLat in range(0,9):\n",
    "        stuffByLats[iStuff+1][iLat] /= cumStuffs[iStuff+1]\n",
    "    header = headers[iStuff].replace(\" (km2)\",\"\")\n",
    "    plt.plot(stuffByLats[0],stuffByLats[iStuff+1], label=header)\n",
    "plt.legend()   \n",
    "plt.xlabel(\"Latitude\")\n",
    "plt.ylabel(\"Fraction per 10$^o$ bin\")\n",
    "plt.savefig(\"figure_05.pdf\")\n",
    "plt.show()\n",
    " "
   ]
  }
 ],
 "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
