notebooks/post-processing/pyCIAM-results-figures.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append(\"../\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.9/site-packages/dask_gateway/client.py:21: FutureWarning: format_bytes is deprecated and will be removed in a future release. Please use dask.utils.format_bytes instead.\n",
      "  from distributed.utils import LoopRunner, format_bytes\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from ast import literal_eval\n",
    "\n",
    "import cartopy.crs as ccrs\n",
    "import dask\n",
    "import dask.config\n",
    "import geopandas as gpd\n",
    "import matplotlib.colors as mcols\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "from matplotlib.colors import LinearSegmentedColormap\n",
    "from pyCIAM.constants import COSTTYPES\n",
    "from shared import (\n",
    "    DIR_FIGS,\n",
    "    PATH_BORDERS,\n",
    "    PATH_COASTLINES,\n",
    "    PATH_DIAZ_INPUTS_INT,\n",
    "    PATH_DIAZ_RES,\n",
    "    PATH_GADM,\n",
    "    PATH_MOVEFACTOR_DATA,\n",
    "    PATH_OUTPUTS,\n",
    "    PATH_PARAMS,\n",
    "    PATH_PARAMS_DIAZ,\n",
    "    PATH_PWT,\n",
    "    PATH_SLIIDERS,\n",
    "    PATH_SLIIDERS_INCOME_INTERMEDIATE_FILE,\n",
    "    PATH_SLR_AR5_QUANTILES,\n",
    "    PATH_SLR_AR6,\n",
    "    PATH_SLR_SWEET,\n",
    "    QUANTILES,\n",
    "    open_zarr,\n",
    "    read_shapefile,\n",
    "    start_dask_cluster,\n",
    ")\n",
    "\n",
    "# SLR scenarios modeled but to exclude from analysis\n",
    "drop_scens = [\"ssp245_low\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "PARAMS = pd.read_json(str(PATH_PARAMS))[\"values\"]\n",
    "PARAMS_DIAZ = pd.read_json(str(PATH_PARAMS_DIAZ))[\"values\"]\n",
    "\n",
    "# Selected constants for plotting\n",
    "CASES = [\"optimalfixed\", \"noAdaptation\"]\n",
    "\n",
    "RESENDYR = 2100  # last year of results to consider\n",
    "POPWTYR = 2000  # year of population to use in % pop adaptation strategy plots\n",
    "PPP_BASELINE_YEAR = 2019  # dollar value year of results\n",
    "\n",
    "# When running on larger/scalable dask cluster, may wish to specify number of workers\n",
    "# Default is LocalCluster which will use the number of CPUs available on local machine\n",
    "N_WORKERS = None\n",
    "# N_WORKERS = 128\n",
    "\n",
    "# max elev in Diaz, and max to consider in % pop and length comparisons w pyCIAM\n",
    "MAXELEV = 15\n",
    "\n",
    "# pyCIAM SLR, IAM, SSP scenarios to focus on for global maps\n",
    "NCC_SCEN = (\"AR6\", \"NCC\")\n",
    "SCEN = (\"AR6-Medium\", \"SSP2-4.5\")\n",
    "IAM = \"IIASA\"\n",
    "SSP = \"SSP2\"\n",
    "\n",
    "# color-blind robust palette for scatter plots\n",
    "cols = [\"#CC79A7\", \"#D55E00\", \"#0072B2\", \"#E8C602\", \"#50CAA8\", \"#56B4E9\", \"#E69F00\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start Dask Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-c1375cf8-c815-11ed-bcc9-665816eadc72</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_gateway.GatewayCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/services/dask-gateway/clusters/daskhub-dev.61b117f50a8647b39d280c62a01865fc/status\" target=\"_blank\">/services/dask-gateway/clusters/daskhub-dev.61b117f50a8647b39d280c62a01865fc/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div style='background-color: #f2f2f2; display: inline-block; padding: 10px; border: 1px solid #999999;'>\n",
       "  <h3>GatewayCluster</h3>\n",
       "  <ul>\n",
       "    <li><b>Name: </b>daskhub-dev.61b117f50a8647b39d280c62a01865fc\n",
       "    <li><b>Dashboard: </b><a href='/services/dask-gateway/clusters/daskhub-dev.61b117f50a8647b39d280c62a01865fc/status' target='_blank'>/services/dask-gateway/clusters/daskhub-dev.61b117f50a8647b39d280c62a01865fc/status</a>\n",
       "  </ul>\n",
       "</div>\n",
       "\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tls://10.4.4.2:8786' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = start_dask_cluster(n_workers=N_WORKERS)\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper funcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rename_pc(val):\n",
    "    split = val.split(\"_\")\n",
    "    if len(split) == 1:\n",
    "        cat = \"Sweet\"\n",
    "        scen = split[0]\n",
    "    else:\n",
    "        split = [\"_\".join(split[:-1]), split[-1]]\n",
    "        if \"190726\" in split[0]:\n",
    "            cat = \"B19\"\n",
    "            scen = split[1]\n",
    "        elif split[1].startswith(\"rcp\"):\n",
    "            scen = \"RCP \" + split[1][-2] + \".\" + split[1][-1]\n",
    "            if \"170113\" in split[0]:\n",
    "                cat = \"K14\"\n",
    "            elif \"190726\" in split[0]:\n",
    "                cat = \"B19\"\n",
    "            elif \"200204\" in split[0]:\n",
    "                cat = \"D21\"\n",
    "            elif \"210628\" in split[0]:\n",
    "                cat = \"SROCC\"\n",
    "        elif split[0] == \"ncc\":\n",
    "            if split[1].startswith(\"ar\"):\n",
    "                cat = split[1].upper()\n",
    "            else:\n",
    "                cat = split[1].title()\n",
    "            scen = split[0].upper()\n",
    "        elif split[0].startswith(\"ssp\"):\n",
    "            scen = split[0][:4].upper() + \"-\" + split[0][4] + \".\" + split[0][5:]\n",
    "            cat = \"AR6-\" + split[1].title()\n",
    "\n",
    "    return (cat, scen)\n",
    "\n",
    "\n",
    "def rename_d16(scen_val):\n",
    "    if scen_val == \"ncc\":\n",
    "        return \"NCC\"\n",
    "    else:\n",
    "        return f\"RCP {int(scen_val[3:]) / 10:.1f}\"\n",
    "\n",
    "\n",
    "def ds_filt(\n",
    "    pyCIAM_da,\n",
    "    diaz_da,\n",
    "    case=\"optimalfixed\",\n",
    "    costtypes=COSTTYPES,\n",
    "    cconly=False,\n",
    "):\n",
    "    \"\"\"Filter pyCIAM and Diaz2016 results datasets by variable, case, scenarios, years\n",
    "    (used for plotting results subsets)\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    pyCIAM_ds : xr.Dataset\n",
    "        dataset of pyCIAM results to be filtered\n",
    "    diaz_ds : xr.Dataset\n",
    "        dataset of pyCIAM (configured in Diaz2016 mode) results to be filtered\n",
    "    val : str\n",
    "        name of data variable to be selected in filtered output (\"npv\", \"costs\")\n",
    "    case : str\n",
    "        name of model case to be selected (\"optimalfixed\",\"noAdaptation\")\n",
    "    costtypes : list of str\n",
    "        cost types to be selected (\"inundation\", \"protection\", ...)\n",
    "    cconly : True/False\n",
    "        conditional to select just climate-change scenarios (True) or all (False)\n",
    "    pyCIAM_quantiles : list\n",
    "        SLR quantiles of pyCIAM results to consider\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    two xr.DataArray objects representing filtered results from input Datasets\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    diaz_da = diaz_da.sel(quantile=0.5)\n",
    "    pyCIAM_da = pyCIAM_da.sel(quantile=0.5)\n",
    "\n",
    "    if cconly:\n",
    "        pyCIAM_da = pyCIAM_da.sel(scenario=pyCIAM_da.scen != \"NCC\")\n",
    "        diaz_da = diaz_da.sel(scenario=diaz_da.scen != \"NCC\")\n",
    "\n",
    "    selectors = {\n",
    "        k: v\n",
    "        for k, v in [\n",
    "            (\"case\", case),\n",
    "            (\"costtype\", costtypes),\n",
    "        ]\n",
    "        if k in pyCIAM_da.dims\n",
    "    }\n",
    "    sum_dims = [d for d in [\"costtype\"] if d in selectors.keys()]\n",
    "\n",
    "    return [(da.sel(selectors).sum(sum_dims)) for da in [pyCIAM_da, diaz_da]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SLR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmsl_da = xr.concat(\n",
    "    [\n",
    "        open_zarr(p).gsl_msl05.sel(year=2100, quantile=QUANTILES).drop(\"year\").load()\n",
    "        for p in (PATH_SLR_AR5_QUANTILES, PATH_SLR_AR6, PATH_SLR_SWEET)\n",
    "    ],\n",
    "    dim=\"scenario\",\n",
    ").drop_sel(scenario=\"ssp245_low\")\n",
    "\n",
    "scen_names = gmsl_da.scenario.to_series().apply(rename_pc)\n",
    "gmsl_da = gmsl_da.assign_coords(\n",
    "    ID=scen_names.str[0], scen=scen_names.str[1]\n",
    ").set_xindex([\"ID\", \"scen\"])\n",
    "gmsl = (\n",
    "    gmsl_da.to_series().unstack(\"quantile\").reorder_levels([\"ID\", \"scen\"]).sort_index()\n",
    ")\n",
    "\n",
    "# AR6-Medium and Sweet are the colored in scenarios for scatter plots\n",
    "pc_sel_scens = gmsl.loc[pd.IndexSlice[[\"AR6-Medium\", \"Sweet\"]]].index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "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>quantile</th>\n",
       "      <th>0.17</th>\n",
       "      <th>0.50</th>\n",
       "      <th>0.83</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th>scen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AR6-Low</th>\n",
       "      <th>SSP1-2.6</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSP5-8.5</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AR6-Medium</th>\n",
       "      <th>SSP1-1.9</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSP1-2.6</th>\n",
       "      <td>0.32</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSP2-4.5</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSP3-7.0</th>\n",
       "      <td>0.55</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSP5-8.5</th>\n",
       "      <td>0.63</td>\n",
       "      <td>0.77</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">B19</th>\n",
       "      <th>2p0degree+L</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rcp85+H</th>\n",
       "      <td>0.79</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">D21</th>\n",
       "      <th>RCP 2.6</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCP 4.5</th>\n",
       "      <td>0.52</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCP 8.5</th>\n",
       "      <td>0.90</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">K14</th>\n",
       "      <th>RCP 2.6</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCP 4.5</th>\n",
       "      <td>0.43</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCP 8.5</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">SROCC</th>\n",
       "      <th>RCP 2.6</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCP 4.5</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCP 8.5</th>\n",
       "      <td>0.71</td>\n",
       "      <td>0.88</td>\n",
       "      <td>1.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Sweet</th>\n",
       "      <th>High</th>\n",
       "      <td>1.95</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Int</th>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IntHigh</th>\n",
       "      <td>1.47</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IntLow</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "quantile                0.17  0.50  0.83\n",
       "ID         scen                         \n",
       "AR6-Low    SSP1-2.6     0.32  0.45  0.79\n",
       "           SSP5-8.5     0.63  0.88  1.61\n",
       "AR6-Medium SSP1-1.9     0.28  0.38  0.55\n",
       "           SSP1-2.6     0.32  0.44  0.61\n",
       "           SSP2-4.5     0.44  0.56  0.76\n",
       "           SSP3-7.0     0.55  0.68  0.90\n",
       "           SSP5-8.5     0.63  0.77  1.02\n",
       "B19        2p0degree+L  0.48  0.68  0.96\n",
       "           rcp85+H      0.79  1.10  1.71\n",
       "D21        RCP 2.6      0.43  0.52  0.61\n",
       "           RCP 4.5      0.52  0.62  0.74\n",
       "           RCP 8.5      0.90  1.10  1.32\n",
       "K14        RCP 2.6      0.35  0.48  0.65\n",
       "           RCP 4.5      0.43  0.58  0.76\n",
       "           RCP 8.5      0.61  0.78  1.00\n",
       "SROCC      RCP 2.6      0.39  0.49  0.60\n",
       "           RCP 4.5      0.48  0.60  0.76\n",
       "           RCP 8.5      0.71  0.88  1.11\n",
       "Sweet      High         1.95  1.98  2.02\n",
       "           Int          0.98  0.98  0.99\n",
       "           IntHigh      1.47  1.48  1.50\n",
       "           IntLow       0.48  0.48  0.49\n",
       "           Low          0.28  0.28  0.29"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add epsilon to make sure .5 gets rounded up (allowing for floating point precision)\n",
    "(gmsl + 1e-10).round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pyCIAM/Diaz Inputs and Results\n",
    "\n",
    "Load and harmonize scenario names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pyCIAM inputs\n",
    "inputs = open_zarr(PATH_SLIIDERS, chunks=None)\n",
    "\n",
    "# diaz16 inputs\n",
    "d16inputs = open_zarr(PATH_DIAZ_INPUTS_INT, chunks=None)\n",
    "\n",
    "# pyCIAM results\n",
    "with dask.config.set(**{\"array.slicing.split_large_chunks\": False}):\n",
    "    res = (\n",
    "        open_zarr(PATH_OUTPUTS)\n",
    "        .sel(case=CASES, quantile=QUANTILES)\n",
    "        .drop_sel(scenario=drop_scens)\n",
    "        .persist()\n",
    "    )\n",
    "\n",
    "# Diaz 2016 results ($2010)\n",
    "d16 = open_zarr(PATH_DIAZ_RES)\n",
    "\n",
    "# harmonize scenario names\n",
    "scen_names = res.scenario.to_series().apply(rename_pc)\n",
    "res = res.assign_coords(ID=scen_names.str[0], scen=scen_names.str[1]).set_xindex(\n",
    "    [\"ID\", \"scen\"]\n",
    ")\n",
    "\n",
    "d16 = (\n",
    "    d16.assign_coords(scenario=d16.scenario.to_series().apply(rename_d16))\n",
    "    .rename(scenario=\"scen\")\n",
    "    .expand_dims(ID=[\"K14\"])\n",
    "    .stack(scenario=[\"ID\", \"scen\"])\n",
    ")\n",
    "\n",
    "# add Diaz NPV consistent with original paper\n",
    "d16[\"npv\"] = (\n",
    "    (\n",
    "        10\n",
    "        * d16.costs\n",
    "        * ((1 / (1 + PARAMS_DIAZ.dr)) ** (d16.year - PARAMS_DIAZ.npv_start)).clip(max=1)\n",
    "    )\n",
    "    .sel(year=slice(PARAMS_DIAZ.npv_start, RESENDYR))\n",
    "    .sum(\"year\")\n",
    ")\n",
    "\n",
    "# Add NPV to Diaz dataset that is consistent with pyCIAM (2005-2100)\n",
    "with dask.config.set(**{\"array.slicing.split_large_chunks\": False}):\n",
    "    d16a = (\n",
    "        d16.reindex(year=np.arange(d16.year.min().item(), RESENDYR + 1))\n",
    "        .reset_index(\"scenario\")\n",
    "        .ffill(\"year\")\n",
    "        .set_xindex([\"ID\", \"scen\"])\n",
    "    )\n",
    "    d16[\"npv_consistent\"] = (\n",
    "        d16a.costs\n",
    "        * ((1 / (1 + PARAMS.dr)) ** (d16a.year - PARAMS.npv_start))\n",
    "        .clip(max=1)\n",
    "        .sel(year=slice(PARAMS.npv_start, RESENDYR))\n",
    "    ).sum(\"year\")\n",
    "d16 = d16.load()\n",
    "\n",
    "# add NPV to pyCIAM results\n",
    "res[\"npv\"] = (\n",
    "    (res.costs * ((1 / (1 + PARAMS.dr)) ** (res.year - PARAMS.npv_start)).clip(max=1))\n",
    "    .sel(year=slice(PARAMS.npv_start, RESENDYR))\n",
    "    .sum(\"year\")\n",
    "    .persist()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "mf = open_zarr(PATH_MOVEFACTOR_DATA, chunks=None)\n",
    "mfdf = mf.sum(\"seg\").to_dataframe()\n",
    "\n",
    "# Convert to desired units\n",
    "mfdf[\"K_2019\"] = mfdf.K_2019 / 1e12  # trillions\n",
    "mfdf[\"pop_2019\"] = mfdf.pop_2019 / 1e6  # millions\n",
    "\n",
    "# Find midpoint values of K and pop\n",
    "# y vals\n",
    "mid = mfdf.max() / 2\n",
    "\n",
    "xint = np.abs(1 - mfdf / mid).idxmin()\n",
    "xmid = xint.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lw = 4  # linewidth\n",
    "ms = 60  # marker size\n",
    "mc = \"grey\"  # marker color\n",
    "mt = \"o\"  # marker type\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(18, 10), dpi=300)\n",
    "ax2 = ax.twinx()\n",
    "ax.xaxis.set_tick_params(labelsize=25)\n",
    "ax.yaxis.set_tick_params(labelsize=20)\n",
    "ax2.yaxis.set_tick_params(labelsize=20)\n",
    "\n",
    "ax.plot(mfdf.K_2019, lw=lw, c=\"dodgerblue\", zorder=1)\n",
    "ax2.plot(mfdf.pop_2019, lw=lw, c=\"coral\", zorder=1)\n",
    "\n",
    "# Midpoint line (same height as popmid)\n",
    "ax.axhline(mid.K_2019, linestyle=\"--\", lw=2, c=\"k\", alpha=0.2, zorder=3)\n",
    "\n",
    "# Line intersections\n",
    "ax.scatter([xint.K_2019], [mid.K_2019], marker=mt, s=ms, zorder=4, c=mc)\n",
    "ax2.scatter([xint.pop_2019], [mid.pop_2019], s=ms, zorder=4, c=mc)\n",
    "ax.grid(alpha=0.3, linestyle=\"--\")\n",
    "\n",
    "# Midpoint of intersections\n",
    "ax.scatter([xmid], 0, marker=\"D\", c=\"r\", s=80, alpha=0.5)\n",
    "ax.scatter([xmid], mid.K_2019, marker=\"x\", c=\"r\", s=80, alpha=0.5)\n",
    "ax.vlines(xmid, 0, mid.K_2019, linestyles=\"--\", colors=\"r\", alpha=0.5)\n",
    "\n",
    "# Align y-axes\n",
    "ax.set_ylim(0, None)\n",
    "ax2.set_ylim(0, None)\n",
    "ax.set_xlabel(\"Move Factor\", fontsize=20)\n",
    "ax.set_ylabel(\"Capital [Trillion $2019]\", fontsize=20)\n",
    "ax2.set_ylabel(\"Population in 2019 [Million]\", fontsize=20)\n",
    "\n",
    "out_path = DIR_FIGS / \"movefactor_K_pop.png\"\n",
    "if os.path.isfile(out_path):\n",
    "    os.remove(out_path)\n",
    "plt.savefig(out_path, dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## pyCIAM (CIAM config) Diaz16 paper results comp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "    scen      &lt;U7 &#x27;RCP 8.5&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'npv'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-25dd7cd7-5416-4c4a-940d-bacfb78c9700' class='xr-array-in' type='checkbox' checked><label for='section-25dd7cd7-5416-4c4a-940d-bacfb78c9700' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.692e+12</span></div><div class='xr-array-data'><pre>array(1.69224573e+12)</pre></div></div></li><li class='xr-section-item'><input id='section-0e331f15-370b-40c8-b515-272f391f2643' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0e331f15-370b-40c8-b515-272f391f2643' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>case</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;optimalfixed&#x27;</div><input id='attrs-c0fd4cc2-a45f-417c-86a5-99faca852562' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0fd4cc2-a45f-417c-86a5-99faca852562' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7c2a171-52ed-4de0-b1df-38c248da45dd' class='xr-var-data-in' type='checkbox'><label for='data-b7c2a171-52ed-4de0-b1df-38c248da45dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Adaptation strategy chosen by all segments.\n",
       "noAdaptation: Reactive retreat\n",
       "protectX: Proactive protection to the 1-in-X year return value\n",
       "retreat1: Proactive retreat to MSL\n",
       "retreatX: Proactive retreat to the 1-in-X year return value</dd><dt><span>long_name :</span></dt><dd>Adaptation Strategy</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;optimalfixed&#x27;, dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quantile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5</div><input id='attrs-3ddaf7d7-1d16-45a6-a13a-fd31571c90fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ddaf7d7-1d16-45a6-a13a-fd31571c90fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b246262f-2d1f-43dd-96ce-8a475c6f58c1' class='xr-var-data-in' type='checkbox'><label for='data-b246262f-2d1f-43dd-96ce-8a475c6f58c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Quantile of corresponding probabilistic local SLR projections used at each segment</dd><dt><span>long_name :</span></dt><dd>SLR quantile</dd></dl></div><div class='xr-var-data'><pre>array(0.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scenario</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(&#x27;K14&#x27;, &#x27;RCP 8.5&#x27;)</div><input id='attrs-b0570fa3-bc19-494f-ba5e-f7673dab4ec8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0570fa3-bc19-494f-ba5e-f7673dab4ec8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c797e97-a0de-4793-9b68-592ed7a8bdd3' class='xr-var-data-in' type='checkbox'><label for='data-3c797e97-a0de-4793-9b68-592ed7a8bdd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array((&#x27;K14&#x27;, &#x27;RCP 8.5&#x27;), dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ID</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;K14&#x27;</div><input id='attrs-118d3201-e9f4-4d1a-af06-fa1be93d4dcf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-118d3201-e9f4-4d1a-af06-fa1be93d4dcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29412192-fffd-4ceb-a394-2c46f5495075' class='xr-var-data-in' type='checkbox'><label for='data-29412192-fffd-4ceb-a394-2c46f5495075' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;K14&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scen</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>&#x27;RCP 8.5&#x27;</div><input id='attrs-be301f3d-67ec-44c6-877c-36ac97ec8c55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be301f3d-67ec-44c6-877c-36ac97ec8c55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b7e3046-6b81-4ff5-9c9b-36582da6996c' class='xr-var-data-in' type='checkbox'><label for='data-3b7e3046-6b81-4ff5-9c9b-36582da6996c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;RCP 8.5&#x27;, dtype=&#x27;&lt;U7&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-752f1510-8506-4e2a-b78f-909dd8d17a82' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-752f1510-8506-4e2a-b78f-909dd8d17a82' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-03adb2b6-96d9-475b-a114-7e74ebd945a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-03adb2b6-96d9-475b-a114-7e74ebd945a0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'npv' ()>\n",
       "array(1.69224573e+12)\n",
       "Coordinates:\n",
       "    case      <U12 'optimalfixed'\n",
       "    quantile  float64 0.5\n",
       "    scenario  object ('K14', 'RCP 8.5')\n",
       "    ID        <U3 'K14'\n",
       "    scen      <U7 'RCP 8.5'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d16_85 = d16.sel(scenario=(\"K14\", \"RCP 8.5\"), quantile=0.5)\n",
    "d16_85_opt = d16_85.sel(case=\"optimalfixed\")\n",
    "\n",
    "# Global NPV (2010-2100) [$Tn]\n",
    "d16_85_opt.npv.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.29315740954944"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Wetland Costs in 2100 [$Bn] (annualizing the decadal costs)\n",
    "d16_85_opt.costs.sel(year=RESENDYR, costtype=\"wetland\").sum().item() / 1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "282.0671058445753"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Global Costs in 2100 (optimal adapt) [$Bn]\n",
    "d16_85_opt.costs.sel(year=RESENDYR).sum().item() / 1e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.251455307220169"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Global Costs in 2100 (no adapt) [$Tn]\n",
    "d16_85.costs.sel(year=RESENDYR, case=\"noAdaptation\").sum().item() / 1e12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Country NPV values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;npv&#x27; (country: 3)&gt;\n",
       "array([419.73063722,  97.4954307 , 208.55586383])\n",
       "Coordinates:\n",
       "    quantile  float64 0.5\n",
       "    scenario  object (&#x27;K14&#x27;, &#x27;RCP 8.5&#x27;)\n",
       "    ID        &lt;U3 &#x27;K14&#x27;\n",
       "    scen      &lt;U7 &#x27;RCP 8.5&#x27;\n",
       "    case      &lt;U12 &#x27;optimalfixed&#x27;\n",
       "  * country   (country) object &#x27;UnitedStates&#x27; &#x27;Brazil&#x27; &#x27;Australia&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'npv'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>country</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-41b7ef85-d0dc-44e0-bde8-462aac74ffde' class='xr-array-in' type='checkbox' checked><label for='section-41b7ef85-d0dc-44e0-bde8-462aac74ffde' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>419.7 97.5 208.6</span></div><div class='xr-array-data'><pre>array([419.73063722,  97.4954307 , 208.55586383])</pre></div></div></li><li class='xr-section-item'><input id='section-615598b7-bb55-4731-b27c-a036f9f28dd8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-615598b7-bb55-4731-b27c-a036f9f28dd8' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>quantile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5</div><input id='attrs-1f2ac9b2-9137-452d-8e32-465381b4c95e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f2ac9b2-9137-452d-8e32-465381b4c95e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b80e9fe-3320-4d0c-ac71-f2b94d974855' class='xr-var-data-in' type='checkbox'><label for='data-7b80e9fe-3320-4d0c-ac71-f2b94d974855' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Quantile of corresponding probabilistic local SLR projections used at each segment</dd><dt><span>long_name :</span></dt><dd>SLR quantile</dd></dl></div><div class='xr-var-data'><pre>array(0.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scenario</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(&#x27;K14&#x27;, &#x27;RCP 8.5&#x27;)</div><input id='attrs-e24c969d-fb5f-48aa-be5e-475b135ccef2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e24c969d-fb5f-48aa-be5e-475b135ccef2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-317bc3a6-67d7-4d55-a817-c25e9312aa97' class='xr-var-data-in' type='checkbox'><label for='data-317bc3a6-67d7-4d55-a817-c25e9312aa97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array((&#x27;K14&#x27;, &#x27;RCP 8.5&#x27;), dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ID</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;K14&#x27;</div><input id='attrs-f7ee2560-7337-4ed0-a14c-eb0b32d4e089' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7ee2560-7337-4ed0-a14c-eb0b32d4e089' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1f934dc-05d2-4ca0-85ec-b2cd94d1d281' class='xr-var-data-in' type='checkbox'><label for='data-d1f934dc-05d2-4ca0-85ec-b2cd94d1d281' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;K14&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scen</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>&#x27;RCP 8.5&#x27;</div><input id='attrs-19cb9689-8ccc-4a3e-b5fd-f8c3b86e02db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19cb9689-8ccc-4a3e-b5fd-f8c3b86e02db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1aefe72-a3d6-4470-af01-81b8b3ab2a34' class='xr-var-data-in' type='checkbox'><label for='data-d1aefe72-a3d6-4470-af01-81b8b3ab2a34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;RCP 8.5&#x27;, dtype=&#x27;&lt;U7&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>case</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;optimalfixed&#x27;</div><input id='attrs-62c725a5-d0f3-4e4d-aa3c-7d3c03bc55e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62c725a5-d0f3-4e4d-aa3c-7d3c03bc55e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb99c34a-8950-4603-ba58-bf044e03e75e' class='xr-var-data-in' type='checkbox'><label for='data-cb99c34a-8950-4603-ba58-bf044e03e75e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Adaptation strategy chosen by all segments.\n",
       "noAdaptation: Reactive retreat\n",
       "protectX: Proactive protection to the 1-in-X year return value\n",
       "retreat1: Proactive retreat to MSL\n",
       "retreatX: Proactive retreat to the 1-in-X year return value</dd><dt><span>long_name :</span></dt><dd>Adaptation Strategy</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;optimalfixed&#x27;, dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>country</span></div><div class='xr-var-dims'>(country)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;UnitedStates&#x27; &#x27;Brazil&#x27; &#x27;Australia&#x27;</div><input id='attrs-76f6e11f-aec6-4229-86c8-53037a548f45' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76f6e11f-aec6-4229-86c8-53037a548f45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b754f592-5c7d-4973-9056-fba7e6359bbe' class='xr-var-data-in' type='checkbox'><label for='data-b754f592-5c7d-4973-9056-fba7e6359bbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;UnitedStates&#x27;, &#x27;Brazil&#x27;, &#x27;Australia&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d66fb13-a787-4d5b-b5b1-1720af4717b4' class='xr-section-summary-in' type='checkbox'  ><label for='section-2d66fb13-a787-4d5b-b5b1-1720af4717b4' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>country</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0d802d4f-4922-4c11-a31d-97aac3e54331' class='xr-index-data-in' type='checkbox'/><label for='index-0d802d4f-4922-4c11-a31d-97aac3e54331' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;UnitedStates&#x27;, &#x27;Brazil&#x27;, &#x27;Australia&#x27;], dtype=&#x27;object&#x27;, name=&#x27;country&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-500981f8-c89a-4eff-9395-6cb4d0a84147' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-500981f8-c89a-4eff-9395-6cb4d0a84147' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'npv' (country: 3)>\n",
       "array([419.73063722,  97.4954307 , 208.55586383])\n",
       "Coordinates:\n",
       "    quantile  float64 0.5\n",
       "    scenario  object ('K14', 'RCP 8.5')\n",
       "    ID        <U3 'K14'\n",
       "    scen      <U7 'RCP 8.5'\n",
       "    case      <U12 'optimalfixed'\n",
       "  * country   (country) object 'UnitedStates' 'Brazil' 'Australia'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_npv = (\n",
    "    d16_85_opt.npv.sum(\"costtype\")\n",
    "    .groupby(d16_85_opt.seg.str.replace(\"\\d+\", \"\").rename(\"country\"))  # noqa: E261\n",
    "    .sum()\n",
    "    / 1e9\n",
    ")\n",
    "country_npv.sel(country=[\"UnitedStates\", \"Brazil\", \"Australia\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
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       "  padding-right: 10px;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;npv&#x27; ()&gt;\n",
       "array(86.99817109)\n",
       "Coordinates:\n",
       "    quantile  float64 0.5\n",
       "    scenario  object (&#x27;K14&#x27;, &#x27;RCP 8.5&#x27;)\n",
       "    ID        &lt;U3 &#x27;K14&#x27;\n",
       "    scen      &lt;U7 &#x27;RCP 8.5&#x27;\n",
       "    case      &lt;U12 &#x27;optimalfixed&#x27;\n",
       "    country   &lt;U5 &#x27;China&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'npv'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-89c6e59e-a86b-4a56-8505-54cf483620d2' class='xr-array-in' type='checkbox' checked><label for='section-89c6e59e-a86b-4a56-8505-54cf483620d2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>87.0</span></div><div class='xr-array-data'><pre>array(86.99817109)</pre></div></div></li><li class='xr-section-item'><input id='section-46ed422e-a227-479a-b0aa-31de366485e0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46ed422e-a227-479a-b0aa-31de366485e0' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>quantile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5</div><input id='attrs-98aa3357-614e-42ed-b314-9c4fd7b273b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98aa3357-614e-42ed-b314-9c4fd7b273b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfb56f3a-1586-4159-aa6e-1f7e2d328867' class='xr-var-data-in' type='checkbox'><label for='data-bfb56f3a-1586-4159-aa6e-1f7e2d328867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Quantile of corresponding probabilistic local SLR projections used at each segment</dd><dt><span>long_name :</span></dt><dd>SLR quantile</dd></dl></div><div class='xr-var-data'><pre>array(0.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scenario</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(&#x27;K14&#x27;, &#x27;RCP 8.5&#x27;)</div><input id='attrs-a77fe44d-9200-4f6b-9523-1f346fa4533f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a77fe44d-9200-4f6b-9523-1f346fa4533f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b0a3358-a19f-41c2-b475-acd45f7bf874' class='xr-var-data-in' type='checkbox'><label for='data-1b0a3358-a19f-41c2-b475-acd45f7bf874' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array((&#x27;K14&#x27;, &#x27;RCP 8.5&#x27;), dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ID</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;K14&#x27;</div><input id='attrs-dc52794b-06a3-4398-bd0b-66d8a8665807' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc52794b-06a3-4398-bd0b-66d8a8665807' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7965da07-7e9d-45d1-9ab8-c4d482599606' class='xr-var-data-in' type='checkbox'><label for='data-7965da07-7e9d-45d1-9ab8-c4d482599606' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;K14&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scen</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>&#x27;RCP 8.5&#x27;</div><input id='attrs-7b48b700-2849-4351-8e84-df7efcac60ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7b48b700-2849-4351-8e84-df7efcac60ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4ec822f-410b-4f2e-9ae2-82787bbfd8f1' class='xr-var-data-in' type='checkbox'><label for='data-e4ec822f-410b-4f2e-9ae2-82787bbfd8f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;RCP 8.5&#x27;, dtype=&#x27;&lt;U7&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>case</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;optimalfixed&#x27;</div><input id='attrs-e81b5892-407d-4b72-8cff-4837b17b5ef9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e81b5892-407d-4b72-8cff-4837b17b5ef9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d40ad32-51f3-4e4e-a18b-6510d322123b' class='xr-var-data-in' type='checkbox'><label for='data-2d40ad32-51f3-4e4e-a18b-6510d322123b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Adaptation strategy chosen by all segments.\n",
       "noAdaptation: Reactive retreat\n",
       "protectX: Proactive protection to the 1-in-X year return value\n",
       "retreat1: Proactive retreat to MSL\n",
       "retreatX: Proactive retreat to the 1-in-X year return value</dd><dt><span>long_name :</span></dt><dd>Adaptation Strategy</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;optimalfixed&#x27;, dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>country</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;China&#x27;</div><input id='attrs-cfd977b9-4c69-4003-bc00-cb6369232300' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfd977b9-4c69-4003-bc00-cb6369232300' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-152bce98-7ab4-484a-83a6-82dc0eeed1e9' class='xr-var-data-in' type='checkbox'><label for='data-152bce98-7ab4-484a-83a6-82dc0eeed1e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;China&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a72c8642-b9db-4b98-818d-27c95d62eb69' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a72c8642-b9db-4b98-818d-27c95d62eb69' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8f66f927-adfa-4938-af8d-d3ee5ef8d85b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f66f927-adfa-4938-af8d-d3ee5ef8d85b' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'npv' ()>\n",
       "array(86.99817109)\n",
       "Coordinates:\n",
       "    quantile  float64 0.5\n",
       "    scenario  object ('K14', 'RCP 8.5')\n",
       "    ID        <U3 'K14'\n",
       "    scen      <U7 'RCP 8.5'\n",
       "    case      <U12 'optimalfixed'\n",
       "    country   <U5 'China'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_npv.sel(country=\"China\") - country_npv.sel(country=\"Macau\", drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inflation Adjust Diaz, Calc NPV, CC-only Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adjust to 2019 USD from 2010 USD\n",
    "pl_data = (\n",
    "    pd.read_parquet(\n",
    "        PATH_PWT,\n",
    "        columns=[\"year\", \"pl_gdpo\"],\n",
    "        filters=[\n",
    "            (\"year\", \"in\", [2010, PPP_BASELINE_YEAR]),\n",
    "            (\"countrycode\", \"=\", \"USA\"),\n",
    "        ],\n",
    "    )\n",
    "    .set_index(\"year\")\n",
    "    .pl_gdpo\n",
    ")\n",
    "\n",
    "pl_multiplier = pl_data[PPP_BASELINE_YEAR] / pl_data[2010]\n",
    "\n",
    "# Inflation-adjust Diaz 2016 values to $2019\n",
    "d16[[\"costs\", \"npv\", \"npv_consistent\"]] *= pl_multiplier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create CC-only Costs\n",
    "with dask.config.set(**{\"array.slicing.split_large_chunks\": False}):\n",
    "    res_cc_ar5 = res.sel(\n",
    "        scenario=res.ID.isin([\"K14\", \"B19\", \"D21\", \"SROCC\"])\n",
    "    ) - res.sel(scenario=(\"AR5\", \"NCC\"))\n",
    "\n",
    "    res_cc_ar6 = res.sel(scenario=res.ID.str.startswith(\"AR6-\")) - res.sel(\n",
    "        scenario=(\"AR6\", \"NCC\")\n",
    "    )\n",
    "\n",
    "    res_cc_sweet = res.sel(\n",
    "        scenario=(res.ID == \"Sweet\") & (res.scen != \"NCC\")\n",
    "    ) - res.sel(scenario=(\"Sweet\", \"NCC\"))\n",
    "\n",
    "res_cc = xr.concat([res_cc_ar5, res_cc_ar6, res_cc_sweet], dim=\"scenario\").persist()\n",
    "d16_cc = d16.sel(scenario=d16.scen != \"NCC\") - d16.sel(scenario=(\"K14\", \"NCC\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Print Values for Abstract & Intro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RCP4.5 Scens:\n",
      "[('K14', 'RCP 4.5'), ('D21', 'RCP 4.5'), ('SROCC', 'RCP 4.5'), ('AR6-Medium', 'SSP2-4.5')]\n"
     ]
    }
   ],
   "source": [
    "# RCP4.5 scenarios\n",
    "r45 = [i for i in res.scenario.values if \"4.5\" in i[1]]  # includes AR6, Sweet\n",
    "print(f\"RCP4.5 Scens:\\n{r45}\")\n",
    "\n",
    "# high end scenarios\n",
    "highend = [(\"Sweet\", i) for i in [\"IntHigh\", \"High\"]]\n",
    "\n",
    "# AR6 2-degree (GMSL 0.40-0.69m)\n",
    "s1 = [\n",
    "    (\"AR6-Low\", \"SSP1-2.6\"),\n",
    "    (\"AR6-Medium\", \"SSP1-2.6\"),\n",
    "    (\"AR6-Medium\", \"SSP2-4.5\"),\n",
    "    (\"AR6-Medium\", \"SSP3-7.0\"),\n",
    "]\n",
    "\n",
    "# AR6 4-degree (GMSL 0.58-0.91m)\n",
    "s2 = [(\"AR6-Medium\", \"SSP3-7.0\"), (\"AR6-Low\", \"SSP5-8.5\"), (\"AR6-Medium\", \"SSP5-8.5\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res_sums = (\n",
    "    res_cc.sel(case=[\"optimalfixed\", \"noAdaptation\"])\n",
    "    .sum([\"costtype\", \"adm1\"])\n",
    "    .persist()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Annual Costs in 2100, (median) AR6 2-degree, 4-degree, High-End"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenarios: [('AR6-Low', 'SSP1-2.6'), ('AR6-Medium', 'SSP1-2.6'), ('AR6-Medium', 'SSP2-4.5'), ('AR6-Medium', 'SSP3-7.0')]\n",
      "[ 109.485115 1214.219   ]\n",
      "[ 527.89545 3995.419  ]\n",
      "\n",
      "Scenarios: [('AR6-Medium', 'SSP3-7.0'), ('AR6-Low', 'SSP5-8.5'), ('AR6-Medium', 'SSP5-8.5')]\n",
      "[ 196.36949 3113.6511 ]\n",
      "[ 752.424 6940.88 ]\n",
      "\n",
      "Scenarios: [('Sweet', 'IntHigh'), ('Sweet', 'High')]\n",
      "[  421.3369 11220.931 ]\n",
      "[ 1459.5653 30602.799 ]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "this_sums = (\n",
    "    res_sums.costs.sel(\n",
    "        scenario=pd.Series(s1 + s2 + highend).drop_duplicates().values,\n",
    "        quantile=0.5,\n",
    "        year=RESENDYR,\n",
    "    ).load()\n",
    "    / 1e9\n",
    ")\n",
    "for ss in [s1, s2, highend]:\n",
    "    tmp = this_sums.sel(scenario=ss)\n",
    "    print(f\"Scenarios: {ss}\")\n",
    "    print(tmp.min([\"scenario\", \"ssp\", \"iam\"]).values)\n",
    "    print(tmp.max([\"scenario\", \"ssp\", \"iam\"]).values)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize by GDP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section involves pre-calculated annualized GDP estimates in $2019 USD PPP from the intermediate steps of SLIIDERS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp = open_zarr(PATH_SLIIDERS_INCOME_INTERMEDIATE_FILE)\n",
    "global_gdp = (exp.gdppc * exp[\"pop\"]).load().sum(\"ccode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (ssp: 5, iam: 2)&gt;\n",
       "array([[4.74688408e+15, 5.16870668e+15],\n",
       "       [4.56611040e+15, 4.62183058e+15],\n",
       "       [3.88760430e+15, 3.86227570e+15],\n",
       "       [3.92412808e+15, 4.33656860e+15],\n",
       "       [5.57369355e+15, 6.33169986e+15]])\n",
       "Coordinates:\n",
       "  * iam      (iam) &lt;U5 &#x27;IIASA&#x27; &#x27;OECD&#x27;\n",
       "  * ssp      (ssp) &lt;U4 &#x27;SSP1&#x27; &#x27;SSP2&#x27; &#x27;SSP3&#x27; &#x27;SSP4&#x27; &#x27;SSP5&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>ssp</span>: 5</li><li><span class='xr-has-index'>iam</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-95894612-90dd-4f72-92ac-a3a294440e47' class='xr-array-in' type='checkbox' checked><label for='section-95894612-90dd-4f72-92ac-a3a294440e47' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.747e+15 5.169e+15 4.566e+15 ... 4.337e+15 5.574e+15 6.332e+15</span></div><div class='xr-array-data'><pre>array([[4.74688408e+15, 5.16870668e+15],\n",
       "       [4.56611040e+15, 4.62183058e+15],\n",
       "       [3.88760430e+15, 3.86227570e+15],\n",
       "       [3.92412808e+15, 4.33656860e+15],\n",
       "       [5.57369355e+15, 6.33169986e+15]])</pre></div></div></li><li class='xr-section-item'><input id='section-d0fb7fd9-cab0-4bfc-8eb0-042fe2fdff52' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d0fb7fd9-cab0-4bfc-8eb0-042fe2fdff52' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>iam</span></div><div class='xr-var-dims'>(iam)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;IIASA&#x27; &#x27;OECD&#x27;</div><input id='attrs-f211e931-c72f-4e14-958e-860193432829' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f211e931-c72f-4e14-958e-860193432829' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9d8daed-f592-4e69-af35-262093c66b6c' class='xr-var-data-in' type='checkbox'><label for='data-d9d8daed-f592-4e69-af35-262093c66b6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;IIASA&#x27;, &#x27;OECD&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ssp</span></div><div class='xr-var-dims'>(ssp)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;SSP1&#x27; &#x27;SSP2&#x27; &#x27;SSP3&#x27; &#x27;SSP4&#x27; &#x27;SSP5&#x27;</div><input id='attrs-a5658668-ae90-44cc-9637-aaf4acfb20a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a5658668-ae90-44cc-9637-aaf4acfb20a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9b1ec18-31e3-440a-874b-05a5f95dcd2b' class='xr-var-data-in' type='checkbox'><label for='data-b9b1ec18-31e3-440a-874b-05a5f95dcd2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SSP1&#x27;, &#x27;SSP2&#x27;, &#x27;SSP3&#x27;, &#x27;SSP4&#x27;, &#x27;SSP5&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc915523-95d1-4b1a-92cf-fa2070cfc3ca' class='xr-section-summary-in' type='checkbox'  ><label for='section-cc915523-95d1-4b1a-92cf-fa2070cfc3ca' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>iam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba806183-523f-48bb-bef7-b8b1df1a0784' class='xr-index-data-in' type='checkbox'/><label for='index-ba806183-523f-48bb-bef7-b8b1df1a0784' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;IIASA&#x27;, &#x27;OECD&#x27;], dtype=&#x27;object&#x27;, name=&#x27;iam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ssp</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-61e0789c-24f8-409c-bdd6-eff9482dad4d' class='xr-index-data-in' type='checkbox'/><label for='index-61e0789c-24f8-409c-bdd6-eff9482dad4d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;SSP1&#x27;, &#x27;SSP2&#x27;, &#x27;SSP3&#x27;, &#x27;SSP4&#x27;, &#x27;SSP5&#x27;], dtype=&#x27;object&#x27;, name=&#x27;ssp&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1343eeb0-b4e4-42df-a835-4832fa76f850' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1343eeb0-b4e4-42df-a835-4832fa76f850' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (ssp: 5, iam: 2)>\n",
       "array([[4.74688408e+15, 5.16870668e+15],\n",
       "       [4.56611040e+15, 4.62183058e+15],\n",
       "       [3.88760430e+15, 3.86227570e+15],\n",
       "       [3.92412808e+15, 4.33656860e+15],\n",
       "       [5.57369355e+15, 6.33169986e+15]])\n",
       "Coordinates:\n",
       "  * iam      (iam) <U5 'IIASA' 'OECD'\n",
       "  * ssp      (ssp) <U4 'SSP1' 'SSP2' 'SSP3' 'SSP4' 'SSP5'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_gdp_npv = (\n",
    "    (global_gdp / (1 + 0.04) ** (global_gdp.year - PARAMS.npv_start))\n",
    "    .sel(year=slice(PARAMS.npv_start, RESENDYR))\n",
    "    .sum(\"year\")\n",
    ")\n",
    "global_gdp_npv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (ssp: 5, iam: 2)&gt;\n",
       "array([[5.28759391e+14, 7.29302505e+14],\n",
       "       [5.27775926e+14, 6.94511079e+14],\n",
       "       [3.40489698e+14, 3.59602289e+14],\n",
       "       [3.02357495e+14, 4.55713678e+14],\n",
       "       [9.49600984e+14, 1.31006574e+15]])\n",
       "Coordinates:\n",
       "  * iam      (iam) &lt;U5 &#x27;IIASA&#x27; &#x27;OECD&#x27;\n",
       "  * ssp      (ssp) &lt;U4 &#x27;SSP1&#x27; &#x27;SSP2&#x27; &#x27;SSP3&#x27; &#x27;SSP4&#x27; &#x27;SSP5&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>ssp</span>: 5</li><li><span class='xr-has-index'>iam</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-3f79c51f-88c4-49cf-a997-ec9a747eec99' class='xr-array-in' type='checkbox' checked><label for='section-3f79c51f-88c4-49cf-a997-ec9a747eec99' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.288e+14 7.293e+14 5.278e+14 ... 4.557e+14 9.496e+14 1.31e+15</span></div><div class='xr-array-data'><pre>array([[5.28759391e+14, 7.29302505e+14],\n",
       "       [5.27775926e+14, 6.94511079e+14],\n",
       "       [3.40489698e+14, 3.59602289e+14],\n",
       "       [3.02357495e+14, 4.55713678e+14],\n",
       "       [9.49600984e+14, 1.31006574e+15]])</pre></div></div></li><li class='xr-section-item'><input id='section-89c73423-f11f-4c2e-89b8-8258a804453c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-89c73423-f11f-4c2e-89b8-8258a804453c' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>iam</span></div><div class='xr-var-dims'>(iam)</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;IIASA&#x27; &#x27;OECD&#x27;</div><input id='attrs-7ea7f9dd-00d2-4b24-8ded-42380c9180c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7ea7f9dd-00d2-4b24-8ded-42380c9180c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3a08059-d878-4575-b53c-ecf1da1233ff' class='xr-var-data-in' type='checkbox'><label for='data-e3a08059-d878-4575-b53c-ecf1da1233ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;IIASA&#x27;, &#x27;OECD&#x27;], dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ssp</span></div><div class='xr-var-dims'>(ssp)</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;SSP1&#x27; &#x27;SSP2&#x27; &#x27;SSP3&#x27; &#x27;SSP4&#x27; &#x27;SSP5&#x27;</div><input id='attrs-cbbe786d-fe68-462d-98ee-09707767de60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cbbe786d-fe68-462d-98ee-09707767de60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94821500-36a2-470e-9308-befc715337bc' class='xr-var-data-in' type='checkbox'><label for='data-94821500-36a2-470e-9308-befc715337bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;SSP1&#x27;, &#x27;SSP2&#x27;, &#x27;SSP3&#x27;, &#x27;SSP4&#x27;, &#x27;SSP5&#x27;], dtype=&#x27;&lt;U4&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20b04f25-780f-41bb-a56c-9caaf0c829c1' class='xr-section-summary-in' type='checkbox'  ><label for='section-20b04f25-780f-41bb-a56c-9caaf0c829c1' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>iam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ddeedd97-9b2a-48b6-baa5-e3e39dca523d' class='xr-index-data-in' type='checkbox'/><label for='index-ddeedd97-9b2a-48b6-baa5-e3e39dca523d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;IIASA&#x27;, &#x27;OECD&#x27;], dtype=&#x27;object&#x27;, name=&#x27;iam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ssp</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-71dae5b6-98d5-4bdb-b835-6a4fc4eb021c' class='xr-index-data-in' type='checkbox'/><label for='index-71dae5b6-98d5-4bdb-b835-6a4fc4eb021c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;SSP1&#x27;, &#x27;SSP2&#x27;, &#x27;SSP3&#x27;, &#x27;SSP4&#x27;, &#x27;SSP5&#x27;], dtype=&#x27;object&#x27;, name=&#x27;ssp&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-75e9cd26-f8b4-4fda-ac0d-04f3f24ea17a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-75e9cd26-f8b4-4fda-ac0d-04f3f24ea17a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (ssp: 5, iam: 2)>\n",
       "array([[5.28759391e+14, 7.29302505e+14],\n",
       "       [5.27775926e+14, 6.94511079e+14],\n",
       "       [3.40489698e+14, 3.59602289e+14],\n",
       "       [3.02357495e+14, 4.55713678e+14],\n",
       "       [9.49600984e+14, 1.31006574e+15]])\n",
       "Coordinates:\n",
       "  * iam      (iam) <U5 'IIASA' 'OECD'\n",
       "  * ssp      (ssp) <U4 'SSP1' 'SSP2' 'SSP3' 'SSP4' 'SSP5'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "global_gdp_2100 = global_gdp.sel(year=2100, drop=True)\n",
    "global_gdp_2100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scenarios: [('AR6-Low', 'SSP1-2.6'), ('AR6-Medium', 'SSP1-2.6'), ('AR6-Medium', 'SSP2-4.5'), ('AR6-Medium', 'SSP3-7.0')]\n",
      "[0.02071204 0.11144393]\n",
      "[0.06494613 1.15788926]\n",
      "\n",
      "Scenarios: [('AR6-Medium', 'SSP3-7.0'), ('AR6-Low', 'SSP5-8.5'), ('AR6-Medium', 'SSP5-8.5')]\n",
      "[0.04029534 0.29192208]\n",
      "[0.08897142 1.97495206]\n",
      "\n",
      "Scenarios: [('Sweet', 'IntHigh'), ('Sweet', 'High')]\n",
      "[0.08306414 1.24713468]\n",
      "[0.19734302 7.60298276]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "this_rats = this_sums / (global_gdp_2100 / 1e9) * 100\n",
    "for ss in [s1, s2, highend]:\n",
    "    tmp = this_rats.sel(scenario=ss)\n",
    "    print(f\"Scenarios: {ss}\")\n",
    "    print(tmp.min([\"scenario\", \"ssp\", \"iam\"]).values)\n",
    "    print(tmp.max([\"scenario\", \"ssp\", \"iam\"]).values)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Compare Diaz 2016 reported values to pyCIAM (Scatter Plots) - All Median Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compPlot(\n",
    "    pc,\n",
    "    dc,\n",
    "    col_scens=pc_sel_scens,\n",
    "    xlab=None,\n",
    "    ylab=None,\n",
    "    title=None,\n",
    "    filename=\"myplot\",\n",
    "    xmin=-0.05,\n",
    "    xmax=np.nan,\n",
    "    ymin=0,\n",
    "    ymax=np.nan,\n",
    "    cvals=cols[0:5],\n",
    "    x_iam_jitter=0.01,\n",
    "    plot_trend=True,\n",
    "    trend_poly=1,\n",
    "    y_norm=1e12,\n",
    "    dpi=300,\n",
    "):\n",
    "    \"\"\"\n",
    "    Parameters\n",
    "    ----------\n",
    "    col_scens : list\n",
    "        the selected scenarios to plot with colored markers\n",
    "    xlab, ylab, title : str\n",
    "        label for x, y, title axis values\n",
    "    filename : str\n",
    "        name of output graph file (.png)\n",
    "    xmin, xmax, ymin, ymax : float\n",
    "        figure axes ranges\n",
    "    cvals : list\n",
    "        hex color values to use for SSP colors\n",
    "    x_iam_jitter : float\n",
    "        x-axis distance to jitter (L/R) scatter markers by IAM\n",
    "    plot_trend : True/False\n",
    "        Boolean flag for including trend lines on plots. Default True\n",
    "    trend_poly : 1\n",
    "        Polynomial order for which to fit trend line. Default 1 (linear)\n",
    "\n",
    "    Returns\n",
    "    ----------\n",
    "    displayed scatter plot and saved .png of figure\n",
    "\n",
    "    \"\"\"\n",
    "    val = pc.name\n",
    "    val_dc = dc.name\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(20, 4), dpi=dpi)\n",
    "    ax.xaxis.set_tick_params(labelsize=25)\n",
    "    ax.yaxis.set_tick_params(labelsize=20)\n",
    "\n",
    "    this_pc = xr.merge((pc / y_norm, gmsl_da), join=\"left\", fill_value=0).sel(\n",
    "        quantile=0.5\n",
    "    )\n",
    "    this_dc = xr.merge((dc / y_norm, gmsl_da), join=\"left\", fill_value=0).sel(\n",
    "        quantile=0.5\n",
    "    )\n",
    "\n",
    "    # Plot median values\n",
    "    for i, s in enumerate(this_pc.ssp.values):\n",
    "        ssp_pc = this_pc.sel(ssp=s)\n",
    "        for m in this_pc.iam.values:\n",
    "            iam_pc = ssp_pc.sel(iam=m)\n",
    "            marker = \"o\"\n",
    "            xj = -1 * x_iam_jitter\n",
    "\n",
    "            if m == \"IIASA\":\n",
    "                marker = \"^\"\n",
    "                xj = x_iam_jitter\n",
    "\n",
    "            iam_pc.drop_sel(scenario=col_scens).plot.scatter(\n",
    "                x=\"gsl_msl05\",\n",
    "                y=val,\n",
    "                marker=marker,\n",
    "                edgecolor=\"none\",\n",
    "                c=\"#dbdbdb\",\n",
    "                s=30,\n",
    "                zorder=0.5,\n",
    "            )\n",
    "            iam_pc.assign(gsl_msl05=iam_pc.gsl_msl05 + xj).sel(\n",
    "                scenario=col_scens\n",
    "            ).plot.scatter(\n",
    "                x=\"gsl_msl05\",\n",
    "                y=val,\n",
    "                marker=marker,\n",
    "                edgecolor=\"none\",\n",
    "                c=cvals[i],\n",
    "                s=100,\n",
    "                alpha=0.8,\n",
    "            )\n",
    "\n",
    "        # Plot trendlines by SSP\n",
    "        if plot_trend:\n",
    "            filt = (\n",
    "                ssp_pc.sel(scenario=col_scens)\n",
    "                .reset_coords(drop=True)\n",
    "                .to_dataframe()\n",
    "                .sort_values(\"gsl_msl05\")\n",
    "            )\n",
    "\n",
    "            # calculate equation for trendline and plot\n",
    "            x = filt[\"gsl_msl05\"]\n",
    "\n",
    "            z = np.polyfit(x, filt[val], trend_poly)\n",
    "            p = np.poly1d(z)\n",
    "\n",
    "            x = [0] + list(x)\n",
    "\n",
    "            if trend_poly == 1:\n",
    "                x = [min(x), max(x)]\n",
    "            y = p(x)\n",
    "            ax.plot(x, y, c=cvals[i], alpha=0.7, linestyle=\"dotted\", linewidth=1.4)\n",
    "            coefs = p.coeffs.round(5)\n",
    "            print(f\"{s} Trend Params: {coefs}\")\n",
    "\n",
    "    # Diaz 2016\n",
    "    this_dc.plot.scatter(\n",
    "        x=\"gsl_msl05\", y=val_dc, s=120, marker=\"s\", c=\"black\", zorder=1.5\n",
    "    )\n",
    "\n",
    "    ax.grid(alpha=0.2, linestyle=\"--\")\n",
    "\n",
    "    # Set axis lims\n",
    "    if np.isnan(ymax):\n",
    "        plt.ylim(ymin, 1.05 * this_pc[val].max().item())\n",
    "    else:\n",
    "        plt.ylim(ymin, ymax)\n",
    "\n",
    "    if np.isnan(xmax):\n",
    "        plt.xlim(xmin, 1.08 * this_pc.gsl_msl05.max().item())\n",
    "    else:\n",
    "        plt.xlim(xmin, xmax)\n",
    "\n",
    "    plt.xlabel(xlab)\n",
    "    plt.ylabel(ylab)\n",
    "    plt.title(title)\n",
    "\n",
    "    out_path = DIR_FIGS / str(filename + \".png\")\n",
    "    plt.savefig(out_path, dpi=dpi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Global NPV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_global = res.sum(\"adm1\").load()\n",
    "d16_global = d16.sum(\"seg\")\n",
    "\n",
    "res_cc_global = res_cc.sum(\"adm1\").load()\n",
    "d16_cc_global = d16_cc.sum(\"seg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [],
    "toc-hr-collapsed": true
   },
   "source": [
    "#### SSP2, IIASA vals for Table 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>GMSL</th>\n",
       "      <th colspan=\"2\" halign=\"left\">npv</th>\n",
       "      <th colspan=\"2\" halign=\"left\">costs</th>\n",
       "      <th colspan=\"2\" halign=\"left\">npv_frac</th>\n",
       "      <th colspan=\"2\" halign=\"left\">costs_frac</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>N/A</th>\n",
       "      <th>optimalfixed</th>\n",
       "      <th>noAdaptation</th>\n",
       "      <th>optimalfixed</th>\n",
       "      <th>noAdaptation</th>\n",
       "      <th>optimalfixed</th>\n",
       "      <th>noAdaptation</th>\n",
       "      <th>optimalfixed</th>\n",
       "      <th>noAdaptation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th>scen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sweet</th>\n",
       "      <th>Low</th>\n",
       "      <td>0.28</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.21</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AR6-Medium</th>\n",
       "      <th>SSP1-1.9</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.80</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.03</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSP1-2.6</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.86</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweet</th>\n",
       "      <th>IntLow</th>\n",
       "      <td>0.48</td>\n",
       "      <td>0.91</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.18</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">AR6-Medium</th>\n",
       "      <th>SSP2-4.5</th>\n",
       "      <td>0.56</td>\n",
       "      <td>0.98</td>\n",
       "      <td>3.28</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.46</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSP3-7.0</th>\n",
       "      <td>0.68</td>\n",
       "      <td>1.08</td>\n",
       "      <td>4.15</td>\n",
       "      <td>0.27</td>\n",
       "      <td>3.66</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSP5-8.5</th>\n",
       "      <td>0.77</td>\n",
       "      <td>1.20</td>\n",
       "      <td>5.24</td>\n",
       "      <td>0.31</td>\n",
       "      <td>4.79</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Sweet</th>\n",
       "      <th>Int</th>\n",
       "      <td>0.98</td>\n",
       "      <td>1.34</td>\n",
       "      <td>6.19</td>\n",
       "      <td>0.42</td>\n",
       "      <td>6.64</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IntHigh</th>\n",
       "      <td>1.48</td>\n",
       "      <td>1.84</td>\n",
       "      <td>13.62</td>\n",
       "      <td>0.58</td>\n",
       "      <td>13.93</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.11</td>\n",
       "      <td>2.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2.38</td>\n",
       "      <td>24.61</td>\n",
       "      <td>0.79</td>\n",
       "      <td>24.85</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.15</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     GMSL          npv                     costs               \\\n",
       "case                  N/A optimalfixed noAdaptation optimalfixed noAdaptation   \n",
       "ID         scen                                                                 \n",
       "Sweet      Low       0.28         0.66         1.21         0.09         0.56   \n",
       "AR6-Medium SSP1-1.9  0.38         0.80         2.00         0.12         1.03   \n",
       "           SSP1-2.6  0.44         0.86         2.41         0.15         1.47   \n",
       "Sweet      IntLow    0.48         0.91         2.46         0.18         1.74   \n",
       "AR6-Medium SSP2-4.5  0.56         0.98         3.28         0.21         2.46   \n",
       "           SSP3-7.0  0.68         1.08         4.15         0.27         3.66   \n",
       "           SSP5-8.5  0.77         1.20         5.24         0.31         4.79   \n",
       "Sweet      Int       0.98         1.34         6.19         0.42         6.64   \n",
       "           IntHigh   1.48         1.84        13.62         0.58        13.93   \n",
       "           High      1.98         2.38        24.61         0.79        24.85   \n",
       "\n",
       "                        npv_frac                costs_frac               \n",
       "case                optimalfixed noAdaptation optimalfixed noAdaptation  \n",
       "ID         scen                                                          \n",
       "Sweet      Low              0.01         0.03         0.02         0.11  \n",
       "AR6-Medium SSP1-1.9         0.02         0.04         0.02         0.20  \n",
       "           SSP1-2.6         0.02         0.05         0.03         0.28  \n",
       "Sweet      IntLow           0.02         0.05         0.03         0.33  \n",
       "AR6-Medium SSP2-4.5         0.02         0.07         0.04         0.47  \n",
       "           SSP3-7.0         0.02         0.09         0.05         0.69  \n",
       "           SSP5-8.5         0.03         0.11         0.06         0.91  \n",
       "Sweet      Int              0.03         0.14         0.08         1.26  \n",
       "           IntHigh          0.04         0.30         0.11         2.64  \n",
       "           High             0.05         0.54         0.15         4.71  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_gdp_2100 = global_gdp_2100.sel(ssp=SSP, iam=IAM).item()\n",
    "this_gdp_npv = global_gdp_npv.sel(ssp=SSP, iam=IAM).item()\n",
    "\n",
    "this_econ = (\n",
    "    res_cc_global.sel(ssp=SSP, iam=IAM, quantile=0.5, year=RESENDYR)\n",
    "    .sum(\"costtype\")[[\"npv\", \"costs\"]]\n",
    "    .reset_coords(drop=True)\n",
    ")\n",
    "this_econ[\"npv_frac\"] = this_econ.npv / this_gdp_npv * 100\n",
    "this_econ[\"costs_frac\"] = this_econ.costs / this_gdp_2100 * 100\n",
    "this_econ[[\"npv\", \"costs\"]] /= 1e12\n",
    "this_econ = this_econ.to_dataframe()[\n",
    "    [\"npv\", \"costs\", \"npv_frac\", \"costs_frac\"]\n",
    "].unstack(\"case\")\n",
    "\n",
    "this_econ_diaz = (\n",
    "    d16_cc_global.sel(\n",
    "        quantile=0.5, year=RESENDYR, case=[\"optimalfixed\", \"noAdaptation\"]\n",
    "    )\n",
    "    .sum(\"costtype\")[[\"npv_consistent\", \"costs\"]]\n",
    "    .rename(npv_consistent=\"npv\")\n",
    "    .reset_coords(drop=True)\n",
    ")\n",
    "this_econ_diaz[\"costs_frac\"] = this_econ_diaz.costs / (147.6e12 * pl_multiplier) * 100\n",
    "this_econ_diaz[[\"npv\", \"costs\"]] /= 1e12\n",
    "this_econ_diaz = this_econ_diaz.to_dataframe()[[\"npv\", \"costs\", \"costs_frac\"]].unstack(\n",
    "    \"case\"\n",
    ")\n",
    "\n",
    "this_gmsl = gmsl.loc[:, [0.5]]\n",
    "this_gmsl.columns = pd.MultiIndex.from_tuples(\n",
    "    [(\"GMSL\", \"N/A\")], names=this_econ.columns.names\n",
    ")\n",
    "\n",
    "tab2 = this_gmsl.join(this_econ.loc[pc_sel_scens], how=\"right\").sort_values(\n",
    "    (\"GMSL\", \"N/A\")\n",
    ")\n",
    "tab2.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>GMSL</th>\n",
       "      <th colspan=\"2\" halign=\"left\">npv</th>\n",
       "      <th colspan=\"2\" halign=\"left\">costs</th>\n",
       "      <th colspan=\"2\" halign=\"left\">npv_frac</th>\n",
       "      <th colspan=\"2\" halign=\"left\">costs_frac</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>N/A</th>\n",
       "      <th>optimalfixed</th>\n",
       "      <th>noAdaptation</th>\n",
       "      <th>optimalfixed</th>\n",
       "      <th>noAdaptation</th>\n",
       "      <th>optimalfixed</th>\n",
       "      <th>noAdaptation</th>\n",
       "      <th>optimalfixed</th>\n",
       "      <th>noAdaptation</th>\n",
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       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th>scen</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>pyCIAM</th>\n",
       "      <th>RCP 2.6</th>\n",
       "      <td>0.48</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diaz 2016</th>\n",
       "      <th>RCP 2.6</th>\n",
       "      <td>0.48</td>\n",
       "      <td>1.05</td>\n",
       "      <td>6.84</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.58</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pyCIAM</th>\n",
       "      <th>RCP 4.5</th>\n",
       "      <td>0.58</td>\n",
       "      <td>1.11</td>\n",
       "      <td>3.80</td>\n",
       "      <td>0.18</td>\n",
       "      <td>2.53</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.48</td>\n",
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       "    <tr>\n",
       "      <th>Diaz 2016</th>\n",
       "      <th>RCP 4.5</th>\n",
       "      <td>0.58</td>\n",
       "      <td>1.17</td>\n",
       "      <td>7.93</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pyCIAM</th>\n",
       "      <th>RCP 8.5</th>\n",
       "      <td>0.78</td>\n",
       "      <td>1.31</td>\n",
       "      <td>5.83</td>\n",
       "      <td>0.27</td>\n",
       "      <td>4.68</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diaz 2016</th>\n",
       "      <th>RCP 8.5</th>\n",
       "      <td>0.78</td>\n",
       "      <td>1.42</td>\n",
       "      <td>9.70</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GMSL          npv                     costs               \\\n",
       "case                N/A optimalfixed noAdaptation optimalfixed noAdaptation   \n",
       "ID        scen                                                                \n",
       "pyCIAM    RCP 2.6  0.48         1.00         2.94         0.14         1.67   \n",
       "Diaz 2016 RCP 2.6  0.48         1.05         6.84         0.15         1.58   \n",
       "pyCIAM    RCP 4.5  0.58         1.11         3.80         0.18         2.53   \n",
       "Diaz 2016 RCP 4.5  0.58         1.17         7.93         0.20         2.04   \n",
       "pyCIAM    RCP 8.5  0.78         1.31         5.83         0.27         4.68   \n",
       "Diaz 2016 RCP 8.5  0.78         1.42         9.70         0.29         2.50   \n",
       "\n",
       "                      npv_frac                costs_frac               \n",
       "case              optimalfixed noAdaptation optimalfixed noAdaptation  \n",
       "ID        scen                                                         \n",
       "pyCIAM    RCP 2.6         0.02         0.06         0.03         0.32  \n",
       "Diaz 2016 RCP 2.6          NaN          NaN         0.08         0.92  \n",
       "pyCIAM    RCP 4.5         0.02         0.08         0.03         0.48  \n",
       "Diaz 2016 RCP 4.5          NaN          NaN         0.12         1.18  \n",
       "pyCIAM    RCP 8.5         0.03         0.13         0.05         0.89  \n",
       "Diaz 2016 RCP 8.5          NaN          NaN         0.17         1.45  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tabc2 = pd.concat(\n",
    "    (\n",
    "        this_gmsl.join(this_econ.loc[this_econ_diaz.index], how=\"right\").rename(\n",
    "            index={\"K14\": \"pyCIAM\"}, level=\"ID\"\n",
    "        ),\n",
    "        this_gmsl.join(this_econ_diaz, how=\"right\").rename(\n",
    "            index={\"K14\": \"Diaz 2016\"}, level=\"ID\"\n",
    "        ),\n",
    "    )\n",
    ").sort_values((\"GMSL\", \"N/A\"))\n",
    "tabc2.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Section 3.2 numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Ratio of adaptation benefits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.6171294289037959, 12.204702594162264)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "this_costs = res_cc_global.npv.sel(quantile=0.5).sum(\"costtype\")\n",
    "this_ratio = this_costs.sel(case=\"noAdaptation\") / this_costs.sel(case=\"optimalfixed\")\n",
    "this_ratio.min().item(), this_ratio.max().item()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Adaptation costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;npv&#x27; (costtype: 2)&gt;\n",
       "array([7.67755449e+11, 3.14260997e+11])\n",
       "Coordinates:\n",
       "    case      &lt;U12 &#x27;optimalfixed&#x27;\n",
       "  * costtype  (costtype) &lt;U15 &#x27;protection&#x27; &#x27;relocation&#x27;\n",
       "    iam       &lt;U5 &#x27;IIASA&#x27;\n",
       "    quantile  float64 0.5\n",
       "    ssp       &lt;U4 &#x27;SSP2&#x27;\n",
       "    scenario  object (&#x27;Sweet&#x27;, &#x27;Int&#x27;)\n",
       "    ID        &lt;U5 &#x27;Sweet&#x27;\n",
       "    scen      &lt;U3 &#x27;Int&#x27;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'npv'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>costtype</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2ea77c2a-d6d7-4087-9c8c-9ab7b55737e3' class='xr-array-in' type='checkbox' checked><label for='section-2ea77c2a-d6d7-4087-9c8c-9ab7b55737e3' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>7.678e+11 3.143e+11</span></div><div class='xr-array-data'><pre>array([7.67755449e+11, 3.14260997e+11])</pre></div></div></li><li class='xr-section-item'><input id='section-bc40486a-1028-4001-ba09-a6f2c477a0cc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bc40486a-1028-4001-ba09-a6f2c477a0cc' class='xr-section-summary' >Coordinates: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>case</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;optimalfixed&#x27;</div><input id='attrs-c4180527-4620-48f8-ab34-812f1c1c7f88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4180527-4620-48f8-ab34-812f1c1c7f88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-326e83f2-23bc-4d71-9a6b-5f4c052033e6' class='xr-var-data-in' type='checkbox'><label for='data-326e83f2-23bc-4d71-9a6b-5f4c052033e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Adaptation strategy chosen by all segments.\n",
       "noAdaptation: Reactive retreat\n",
       "protectX: Proactive protection to the 1-in-X year return value\n",
       "retreat1: Proactive retreat to MSL\n",
       "retreatX: Proactive retreat to the 1-in-X year return value</dd><dt><span>long_name :</span></dt><dd>Adaptation Strategy</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;optimalfixed&#x27;, dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>costtype</span></div><div class='xr-var-dims'>(costtype)</div><div class='xr-var-dtype'>&lt;U15</div><div class='xr-var-preview xr-preview'>&#x27;protection&#x27; &#x27;relocation&#x27;</div><input id='attrs-a7a7ad5d-99d2-4281-b589-0ce328dc64c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a7ad5d-99d2-4281-b589-0ce328dc64c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd088dec-c1d4-449a-988d-9c0f79444ab6' class='xr-var-data-in' type='checkbox'><label for='data-cd088dec-c1d4-449a-988d-9c0f79444ab6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>stormCapital: Value of capital stock lost due to ESL\n",
       "stormMortality: Monetary value of lost life due to ESL, using Value of a Statistical Life (VSL)\n",
       "inundation: Value of inundated or abandoned dry land and capital stock\n",
       "relocation: Value of market and non-market costs of relocation\n",
       "wetland: Value of lost wetland services\n",
       "protection: Construction and maintenance costs of coastal protection</dd><dt><span>long_name :</span></dt><dd>Cost Category</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;protection&#x27;, &#x27;relocation&#x27;], dtype=&#x27;&lt;U15&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iam</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;IIASA&#x27;</div><input id='attrs-2d683327-850c-49f9-a691-f982c0b14abd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d683327-850c-49f9-a691-f982c0b14abd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f7648eb-12b1-46b4-8d5b-eb1ccec194ee' class='xr-var-data-in' type='checkbox'><label for='data-1f7648eb-12b1-46b4-8d5b-eb1ccec194ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Affects all values that are predicated on GDP (e.g. capital stock, land value, etc.)</dd><dt><span>long_name :</span></dt><dd>GDP growth model</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;IIASA&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quantile</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5</div><input id='attrs-bbd662b5-b58f-4206-aedc-e2e7a12d6b59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbd662b5-b58f-4206-aedc-e2e7a12d6b59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e3faaac-6761-4b5f-8743-da0ba0163f8e' class='xr-var-data-in' type='checkbox'><label for='data-8e3faaac-6761-4b5f-8743-da0ba0163f8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Quantile of corresponding probabilistic local SLR projections used at each segment</dd><dt><span>long_name :</span></dt><dd>SLR quantile</dd></dl></div><div class='xr-var-data'><pre>array(0.5)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssp</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U4</div><div class='xr-var-preview xr-preview'>&#x27;SSP2&#x27;</div><input id='attrs-4f10394a-b378-4442-9984-7f1be2e96914' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f10394a-b378-4442-9984-7f1be2e96914' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d3e7087-66ce-434d-944b-51fc15af19ea' class='xr-var-data-in' type='checkbox'><label for='data-0d3e7087-66ce-434d-944b-51fc15af19ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Socioeconomic growth model used</dd><dt><span>long_name :</span></dt><dd>Shared Socioeconomic Pathway</dd></dl></div><div class='xr-var-data'><pre>array(&#x27;SSP2&#x27;, dtype=&#x27;&lt;U4&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scenario</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(&#x27;Sweet&#x27;, &#x27;Int&#x27;)</div><input id='attrs-425a4d4b-263c-473c-a2bf-6323150692bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-425a4d4b-263c-473c-a2bf-6323150692bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81d73ecb-2b0c-49e0-8b31-88cbffc8b8e5' class='xr-var-data-in' type='checkbox'><label for='data-81d73ecb-2b0c-49e0-8b31-88cbffc8b8e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array((&#x27;Sweet&#x27;, &#x27;Int&#x27;), dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ID</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U5</div><div class='xr-var-preview xr-preview'>&#x27;Sweet&#x27;</div><input id='attrs-5a11900e-7352-41d7-b1cb-5978980bbe95' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a11900e-7352-41d7-b1cb-5978980bbe95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-721455a8-07f5-4668-8eab-e78a4e56b301' class='xr-var-data-in' type='checkbox'><label for='data-721455a8-07f5-4668-8eab-e78a4e56b301' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;Sweet&#x27;, dtype=&#x27;&lt;U5&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scen</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;Int&#x27;</div><input id='attrs-9b15bda6-d630-44bd-8bce-d80f783a98ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b15bda6-d630-44bd-8bce-d80f783a98ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1f078d7-9303-4cde-bff4-0baa65f302ce' class='xr-var-data-in' type='checkbox'><label for='data-c1f078d7-9303-4cde-bff4-0baa65f302ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;Int&#x27;, dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c4d5b85d-bb01-44d2-9f95-0c937af81be0' class='xr-section-summary-in' type='checkbox'  ><label for='section-c4d5b85d-bb01-44d2-9f95-0c937af81be0' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>costtype</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0de035f4-0de5-463c-8b25-ac931e4b905a' class='xr-index-data-in' type='checkbox'/><label for='index-0de035f4-0de5-463c-8b25-ac931e4b905a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;protection&#x27;, &#x27;relocation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;costtype&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cdbbd550-3456-45e4-be9c-582b216b03ea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cdbbd550-3456-45e4-be9c-582b216b03ea' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'npv' (costtype: 2)>\n",
       "array([7.67755449e+11, 3.14260997e+11])\n",
       "Coordinates:\n",
       "    case      <U12 'optimalfixed'\n",
       "  * costtype  (costtype) <U15 'protection' 'relocation'\n",
       "    iam       <U5 'IIASA'\n",
       "    quantile  float64 0.5\n",
       "    ssp       <U4 'SSP2'\n",
       "    scenario  object ('Sweet', 'Int')\n",
       "    ID        <U5 'Sweet'\n",
       "    scen      <U3 'Int'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_cc_global.npv.sel(\n",
    "    case=\"optimalfixed\",\n",
    "    iam=IAM,\n",
    "    ssp=SSP,\n",
    "    scenario=(\"Sweet\", \"Int\"),\n",
    "    quantile=0.5,\n",
    "    costtype=[\"protection\", \"relocation\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Range of optimal adaptation NPV across all scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6041707135251856 3.4460354380162794\n"
     ]
    }
   ],
   "source": [
    "all_global_medians = (\n",
    "    res_cc_global.npv.sel(quantile=0.5, case=\"optimalfixed\").sum(\"costtype\") / 1e12\n",
    ")\n",
    "print(all_global_medians.min().item(), all_global_medians.max().item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06989439576864243 1.45956552028656\n"
     ]
    }
   ],
   "source": [
    "all_global_costs = (\n",
    "    res_cc_global.costs.sel(year=RESENDYR, quantile=0.5, case=\"optimalfixed\").sum(\n",
    "        \"costtype\"\n",
    "    )\n",
    "    / 1e12\n",
    ")\n",
    "print(all_global_costs.min().item(), all_global_costs.max().item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09927264600992203 0.5366625189781189\n"
     ]
    }
   ],
   "source": [
    "all_global_costs_k14 = (\n",
    "    res_cc_global.costs.sel(\n",
    "        year=RESENDYR,\n",
    "        quantile=0.5,\n",
    "        case=\"optimalfixed\",\n",
    "        scenario=res_cc_global.ID == \"K14\",\n",
    "    ).sum(\"costtype\")\n",
    "    / 1e12\n",
    ")\n",
    "print(all_global_costs_k14.min().item(), all_global_costs_k14.max().item())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### optimal, total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc, dc = ds_filt(res_global.npv, d16_global.npv_consistent)\n",
    "\n",
    "compPlot(\n",
    "    pc,\n",
    "    dc,\n",
    "    filename=\"global_npv_total_opt\",\n",
    "    ymax=12,\n",
    "    ymin=-0.2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### optimal, CC-only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc, dc = ds_filt(res_cc_global.npv, d16_cc_global.npv_consistent, cconly=True)\n",
    "\n",
    "compPlot(\n",
    "    pc,\n",
    "    dc,\n",
    "    filename=\"global_npv_CC_opt\",\n",
    "    xmin=0.2,\n",
    "    ymax=4,\n",
    "    ymin=-0.05,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Wetland Costs end-of-century"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### optimal, total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc, dc = ds_filt(\n",
    "    res_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    d16_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    costtypes=[\"wetland\"],\n",
    ")\n",
    "\n",
    "compPlot(\n",
    "    pc,\n",
    "    dc,\n",
    "    filename=\"wetlands_anncosts_total_opt\",\n",
    "    ymax=0.15,\n",
    "    ymin=-0.005,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### optimal, CC-only "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc, dc = ds_filt(\n",
    "    res_cc_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    d16_cc_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    costtypes=[\"wetland\"],\n",
    "    cconly=True,\n",
    ")\n",
    "\n",
    "compPlot(\n",
    "    pc,\n",
    "    dc,\n",
    "    filename=\"wetlands_anncosts_CC_opt\",\n",
    "    xmin=0.2,\n",
    "    ymin=-0.01,\n",
    "    ymax=0.15,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Global Costs end-of-century"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### optimal, total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc, dc = ds_filt(\n",
    "    res_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    d16_global.costs.sel(year=RESENDYR, drop=True),\n",
    ")\n",
    "\n",
    "compPlot(\n",
    "    pc,\n",
    "    dc,\n",
    "    filename=\"global_anncosts_total_opt\",\n",
    "    ymax=2,\n",
    "    ymin=-0.1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### optimal, CC-only "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc, dc = ds_filt(\n",
    "    res_cc_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    d16_cc_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    cconly=True,\n",
    ")\n",
    "\n",
    "compPlot(\n",
    "    pc,\n",
    "    dc,\n",
    "    filename=\"global_anncosts_CC_opt\",\n",
    "    xmin=0.2,\n",
    "    ymin=-0.1,\n",
    "    ymax=1.8,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### noAdapt, total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc, dc = ds_filt(\n",
    "    res_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    d16_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    case=\"noAdaptation\",\n",
    ")\n",
    "\n",
    "compPlot(\n",
    "    pc,\n",
    "    dc,\n",
    "    filename=\"global_anncosts_total_noAdapt\",\n",
    "    ymin=-2,\n",
    "    ymax=35,\n",
    "    trend_poly=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### noAdapt, CC-only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc, dc = ds_filt(\n",
    "    res_cc_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    d16_cc_global.costs.sel(year=RESENDYR, drop=True),\n",
    "    case=\"noAdaptation\",\n",
    "    cconly=True,\n",
    ")\n",
    "p = compPlot(\n",
    "    pc,\n",
    "    dc,\n",
    "    filename=\"global_anncosts_CC_noAdapt\",\n",
    "    xmin=0.2,\n",
    "    ymin=-2,\n",
    "    ymax=35,\n",
    "    trend_poly=2,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calc population and K stock btw 0-15m\n",
    "#### Mid-century (for main text numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyCIAM_elevs = inputs.elev <= MAXELEV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SSP Pops in 2050 (bn): [1.1936475 1.2728753 1.3501482 1.2292199 1.21453  ]\n",
      "(IIASA) SSP Ks in 2050 ($Tn): [269.92004 256.83463 223.96371 220.11751 305.20374]\n",
      "(OECD) SSP Ks in 2050 ($Tn): [311.70364 257.9663  217.42883 254.26431 374.58365]\n"
     ]
    }
   ],
   "source": [
    "seg_adm_pop_2050 = inputs.pop_2019.isel(elev=pyCIAM_elevs).sum(\n",
    "    \"elev\"\n",
    ") * inputs.pop_scale.sel(country=inputs.seg_country, year=2050)\n",
    "print(f'SSP Pops in 2050 (bn): {(seg_adm_pop_2050.sum(\"seg_adm\") / 1e9).values}')\n",
    "\n",
    "seg_adm_K_2050 = inputs.K_2019.isel(elev=pyCIAM_elevs).sum(\"elev\") * inputs.K_scale.sel(\n",
    "    country=inputs.seg_country, year=2050\n",
    ")\n",
    "print(\n",
    "    \"(IIASA) SSP Ks in 2050 ($Tn): \"\n",
    "    f'{(seg_adm_K_2050.sel(iam=\"IIASA\").sum(\"seg_adm\") / 1e12).values}'\n",
    ")\n",
    "print(\n",
    "    \"(OECD) SSP Ks in 2050 ($Tn): \"\n",
    "    f'{(seg_adm_K_2050.sel(iam=\"OECD\").sum(\"seg_adm\") / 1e12).values}'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Beginning of model time (for figures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get seg (Diaz) population and coastline length totals\n",
    "seg_pop = (\n",
    "    d16inputs.pop_2000.sum(\"elev\") * d16inputs.pop_scale.sel(year=POPWTYR, drop=True)\n",
    ").rename(\"pop\")\n",
    "seg_len = d16inputs.length\n",
    "\n",
    "# Get seg_adm (pyCIAM) population and coastline length totals\n",
    "# all ssps will have same pop for years <=2010\n",
    "seg_pop_pc = (\n",
    "    (\n",
    "        inputs.isel(elev=pyCIAM_elevs).pop_2019.sum(\"elev\")\n",
    "        * inputs.pop_scale.sel(year=POPWTYR, ssp=SSP, drop=True).sel(\n",
    "            country=inputs.seg_country, drop=True\n",
    "        )\n",
    "    )\n",
    "    .rename(\"pop\")\n",
    "    .groupby(\"seg\")\n",
    "    .sum()\n",
    ")\n",
    "seg_len_pc = inputs.length.groupby(\"seg\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Optimal Retreat Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary of what numbers mean which case\n",
    "case_dict = literal_eval(\n",
    "    open_zarr(PATH_OUTPUTS)\n",
    "    .optimal_case.attrs[\"Description\"]\n",
    "    .lstrip(\"Coded as follows: \")\n",
    ")\n",
    "protect_cases = [v for k, v in case_dict.items() if k.startswith(\"protect\")]\n",
    "retreat_cases = [v for k, v in case_dict.items() if k.startswith(\"retreat\")]\n",
    "noadapt_case = case_dict[\"noAdaptation\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gather data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get dataframes of optimal strategy by scenario for Diaz config\n",
    "dc = xr.merge(\n",
    "    (\n",
    "        d16.sel(quantile=0.5).optimal_case.sel(scenario=d16.scen != \"NCC\"),\n",
    "        seg_pop,\n",
    "        seg_len,\n",
    "    )\n",
    ")\n",
    "\n",
    "# Get dataframes of optimal strategy by scenario for pyCIAM\n",
    "pc = xr.merge(\n",
    "    (\n",
    "        res.sel(quantile=0.5).optimal_case.sel(scenario=res.scen != \"NCC\"),\n",
    "        seg_pop_pc,\n",
    "        seg_len_pc,\n",
    "    )\n",
    ").load()\n",
    "\n",
    "# Get percentages\n",
    "strategy_d16 = xr.where(\n",
    "    dc.optimal_case.isin(protect_cases),\n",
    "    \"protect\",\n",
    "    xr.where(dc.optimal_case.isin(retreat_cases), \"retreat\", \"noAdapt\"),\n",
    ").to_series()\n",
    "\n",
    "strat_by_exposure_d16 = (\n",
    "    strategy_d16.to_frame()\n",
    "    .join(dc[[\"pop\", \"length\"]].reset_coords(drop=True).to_dataframe(), on=\"seg\")\n",
    "    .groupby([\"ID\", \"scen\", \"optimal_case\"])\n",
    "    .sum()\n",
    ")\n",
    "strat_by_exposure_d16 /= strat_by_exposure_d16.groupby([\"ID\", \"scen\"]).sum()\n",
    "\n",
    "strategy_pc = xr.where(\n",
    "    pc.optimal_case.load().isin(protect_cases),\n",
    "    \"protect\",\n",
    "    xr.where(pc.optimal_case.isin(retreat_cases), \"retreat\", \"noAdapt\"),\n",
    ").to_series()\n",
    "\n",
    "strat_by_exposure_pc = (\n",
    "    strategy_pc.to_frame()\n",
    "    .join(pc[[\"pop\", \"length\"]].reset_coords(drop=True).to_dataframe(), on=\"seg\")\n",
    "    .groupby([\"ID\", \"scen\", \"ssp\", \"iam\", \"optimal_case\"])\n",
    "    .sum()\n",
    ")\n",
    "strat_by_exposure_pc /= strat_by_exposure_pc.groupby([\"ID\", \"scen\", \"ssp\", \"iam\"]).sum()\n",
    "\n",
    "# convert to xarray and to percent\n",
    "strat_by_exposure_d16 = (\n",
    "    strat_by_exposure_d16.to_xarray().stack(scenario=[\"ID\", \"scen\"]).dropna(\"scenario\")\n",
    "    * 100\n",
    ")\n",
    "strat_by_exposure_pc = (\n",
    "    strat_by_exposure_pc.to_xarray().stack(scenario=[\"ID\", \"scen\"]).dropna(\"scenario\")\n",
    "    * 100\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for v in strat_by_exposure_pc.data_vars:\n",
    "    for strat in strat_by_exposure_pc.optimal_case.values:\n",
    "        print(v, strat)\n",
    "        compPlot(\n",
    "            strat_by_exposure_pc[v].sel(optimal_case=strat, drop=True),\n",
    "            strat_by_exposure_d16[v].sel(optimal_case=strat, drop=True),\n",
    "            filename=f\"optcase_{strat}_{v}\",\n",
    "            xmin=0.20,\n",
    "            ymin=-5,\n",
    "            ymax=100,\n",
    "            plot_trend=False,\n",
    "            y_norm=1,\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare Data for Maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute Median Costs in 2100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "adm1_costs = res_cc.costs.sel(year=RESENDYR, drop=True).load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import SHPs for Maps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "adm1 = (\n",
    "    read_shapefile(PATH_GADM, layer=\"ADM_1\")\n",
    "    .rename(columns={\"GID_1\": \"adm1\", \"GID_0\": \"country\"})\n",
    "    .set_index(\"adm1\")[[\"country\", \"geometry\"]]\n",
    ")\n",
    "\n",
    "# correct Ghana adm1 code typo in GADM level 1 labels\n",
    "adm1.index = (\n",
    "    adm1.index.str.replace(\"GHA\", \"GHA.\")\n",
    "    .to_series()\n",
    "    .replace(\"CHN.HKG\", \"HKG\")\n",
    "    .replace(\"CHN.MAC\", \"MAC\")\n",
    "    .values\n",
    ")\n",
    "\n",
    "# Country\n",
    "ctys = (\n",
    "    read_shapefile(PATH_GADM, layer=\"ADM_0\")\n",
    "    .rename(columns={\"GID_0\": \"country\"})\n",
    "    .set_index(\"country\")\n",
    ")\n",
    "\n",
    "# Add single adm1 countries (e.g. Aruba) to adm1 file\n",
    "to_append = ctys.loc[adm1_costs.adm1[~adm1_costs.adm1.isin(adm1.index)].values].drop(\n",
    "    columns=\"COUNTRY\"\n",
    ")\n",
    "to_append = to_append.assign(country=to_append.index).rename_axis(index=None)\n",
    "adm1 = pd.concat((adm1, to_append))\n",
    "\n",
    "# add HKG and MAC to ctys\n",
    "ctys = pd.concat(\n",
    "    (\n",
    "        ctys,\n",
    "        adm1.loc[[\"HKG\", \"MAC\"]]\n",
    "        .assign(COUNTRY=[\"Hong Kong\", \"Macao\"])\n",
    "        .rename_axis(index=\"country\")\n",
    "        .drop(columns=\"country\"),\n",
    "    )\n",
    ")\n",
    "\n",
    "# Import borders and coastlines SHPs\n",
    "borders = read_shapefile(PATH_BORDERS)\n",
    "clines = read_shapefile(PATH_COASTLINES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Mapping Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CIL Color Map (Reds)\n",
    "colors = [\n",
    "    \"#fff3d7\",\n",
    "    \"#fbc885\",\n",
    "    \"#f39b65\",\n",
    "    \"#ea744b\",\n",
    "    \"#e45d3d\",\n",
    "    \"#db4231\",\n",
    "    \"#d51d26\",\n",
    "]\n",
    "edgecolor = \"#808080\"\n",
    "cil_cmap = LinearSegmentedColormap.from_list(\"mycm\", colors, N=100)\n",
    "crs = ccrs.Robinson()\n",
    "\n",
    "\n",
    "def costs_poly_plot(\n",
    "    polyshp,\n",
    "    val_col=\"costs\",\n",
    "    filename=\"mymap\",\n",
    "    vmin=0,\n",
    "    vmax=1,\n",
    "    cm=cil_cmap,\n",
    "    symlog=True,\n",
    "    linthresh=1,\n",
    "    linscale=1,\n",
    "    dpi=300,\n",
    "):\n",
    "    \"\"\"creates map of results values for given scenario for geographic boundaries (e.g.\n",
    "    national, ADM1)\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    polyshp : gdp.GeoDataFrame\n",
    "        gdf with joined results variables of polygons to plot (e.g. national or ADM1\n",
    "        boundaries)\n",
    "    val_col : str\n",
    "        name of variable to plot\n",
    "    filename : str\n",
    "        name of saved map file .png\n",
    "    vmin, vmax : int or float\n",
    "        range of color scale variable (z values) on map\n",
    "    cm : int\n",
    "        color map for plot\n",
    "    symlog : bool\n",
    "        enable symmetric log for color scale\n",
    "    linthresh : int or float\n",
    "        The range within which the plot is linear\n",
    "    linscale : int or float\n",
    "        This allows the linear range to be stretched relative to the logarithmic\n",
    "        range.\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(40, 20), subplot_kw={\"projection\": crs}, dpi=dpi)\n",
    "    ax.axis(\"off\")\n",
    "\n",
    "    if symlog:\n",
    "        kwargs = {\n",
    "            \"norm\": mcols.SymLogNorm(linthresh, linscale=linscale, vmin=vmin, vmax=vmax)\n",
    "        }\n",
    "    else:\n",
    "        kwargs = {\"vmin\": vmin, \"vmax\": vmax}\n",
    "    polyshp.to_crs(crs).plot(\n",
    "        column=val_col,\n",
    "        ax=ax,\n",
    "        legend=True,\n",
    "        cmap=cm,\n",
    "        markersize=120,\n",
    "        edgecolor=edgecolor,\n",
    "        linewidth=0,\n",
    "        alpha=0.8,\n",
    "        zorder=10,\n",
    "        **kwargs\n",
    "    )\n",
    "\n",
    "    if filename is not None:\n",
    "        fig.savefig(DIR_FIGS / str(filename + \".png\"), dpi=dpi)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Maps\n",
    "### Costs in 2100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Total Costs in 2100 (USD)\n",
    "cost_map_2100 = adm1.join(\n",
    "    adm1_costs.sel(\n",
    "        case=\"optimalfixed\",\n",
    "        scenario=(\"AR6-Medium\", \"SSP2-4.5\"),\n",
    "        quantile=0.5,\n",
    "        ssp=SSP,\n",
    "        iam=IAM,\n",
    "        drop=True,\n",
    "    )\n",
    "    .sum(\"costtype\")\n",
    "    .to_dataframe(),\n",
    "    how=\"right\",\n",
    ")\n",
    "\n",
    "costs_poly_plot(\n",
    "    cost_map_2100,\n",
    "    filename=\"map_adm1_global_costs_CC_opt\",\n",
    "    vmin=1e7,\n",
    "    vmax=1e10,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Benefits of Adaptation in 2100 [USD]\n",
    "tot_costs = adm1_costs.sel(\n",
    "    scenario=(\"AR6-Medium\", \"SSP2-4.5\"),\n",
    "    quantile=0.5,\n",
    "    ssp=SSP,\n",
    "    iam=IAM,\n",
    "    drop=True,\n",
    ").sum(\"costtype\")\n",
    "\n",
    "ben_adapt = adm1.join(\n",
    "    (\n",
    "        tot_costs.sel(case=\"noAdaptation\", drop=True)\n",
    "        - tot_costs.sel(case=\"optimalfixed\", drop=True)\n",
    "    ).to_series(),\n",
    "    how=\"right\",\n",
    ")\n",
    "costs_poly_plot(\n",
    "    ben_adapt,\n",
    "    filename=\"map_adm1_global_benAdapt_CC_opt\",\n",
    "    vmin=1e7,\n",
    "    vmax=1e11,\n",
    "    cm=\"Greens\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimal Adaptation Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "adapt_dict = {\n",
    "    \"RETREAT\": retreat_cases,\n",
    "    \"PROTECT\": protect_cases,\n",
    "    \"NOADAPT\": noadapt_case,\n",
    "}\n",
    "\n",
    "scen_res = res.sel(\n",
    "    quantile=0.5,\n",
    "    ssp=SSP,\n",
    "    iam=IAM,\n",
    "    scenario=SCEN,\n",
    "    case=[\"noAdaptation\", \"optimalfixed\"],\n",
    "    drop=True,\n",
    ").load()\n",
    "\n",
    "adapt = xr.merge(\n",
    "    (\n",
    "        scen_res.optimal_case,\n",
    "        inputs[[\"seg_lat\", \"seg_lon\"]]\n",
    "        .rename(seg_lat=\"lat\", seg_lon=\"lon\")\n",
    "        .load()\n",
    "        .groupby(inputs.seg)\n",
    "        .first(),\n",
    "    )\n",
    ")\n",
    "adapt = (\n",
    "    gpd.GeoSeries.from_xy(adapt.lon.to_series(), adapt.lat.to_series(), crs=\"EPSG:4326\")\n",
    "    .to_frame(name=\"geometry\")\n",
    "    .join(adapt.optimal_case.to_series())\n",
    ")\n",
    "\n",
    "\n",
    "def adapt_plot(\n",
    "    gdf,\n",
    "    val_col=\"optimal_case\",\n",
    "    adapt_type=\"RETREAT\",\n",
    "    filename=\"myplot\",\n",
    "    vmin=0,\n",
    "    vmax=300,\n",
    "    alpha=0.8,\n",
    "    msize=120,\n",
    "    cm=\"YlGnBu\",\n",
    "    dpi=300,\n",
    "):\n",
    "    \"\"\"creates map of results values for given scenario for geographic boundaries (e.g.\n",
    "    national, ADM1)\n",
    "\n",
    "    ----------\n",
    "    gdf : gdp.GeoDataFrame\n",
    "        gdf with optimal adaptation results joined with segment centroid SHP\n",
    "    val_col : str\n",
    "        name of variable to plot\n",
    "    adapt_type : str\n",
    "        which type of adaptation strategies to plot (PROTECT, RETREAT, NOADAPT)\n",
    "    filename : str\n",
    "        name of saved map file .png\n",
    "    vmin, vmax : int or float\n",
    "        range of color scale variable (z values) on map\n",
    "    alpha : float\n",
    "        opacity of plotted points\n",
    "    msize : int\n",
    "        point marker size\n",
    "    cm : int\n",
    "        color map for plot\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(40, 20), subplot_kw={\"projection\": crs}, dpi=dpi)\n",
    "    ax.axis(\"off\")\n",
    "\n",
    "    # International Borders\n",
    "    borders.to_crs(crs).plot(\n",
    "        ax=ax, facecolor=\"none\", edgecolor=edgecolor, linewidth=1, alpha=0.5, zorder=5\n",
    "    )\n",
    "\n",
    "    # Coastlines\n",
    "    clines.to_crs(crs).plot(\n",
    "        ax=ax, edgecolor=edgecolor, linewidth=1, alpha=0.5, zorder=0\n",
    "    )\n",
    "\n",
    "    filters = adapt_dict[adapt_type]\n",
    "    if isinstance(filters, int):\n",
    "        filters = [filters]\n",
    "\n",
    "    gdf[gdf[val_col].isin(filters)].to_crs(crs).plot(\n",
    "        column=val_col,\n",
    "        ax=ax,\n",
    "        legend=True,\n",
    "        cmap=cm,\n",
    "        vmin=vmin,\n",
    "        vmax=vmax,\n",
    "        markersize=msize,\n",
    "        edgecolor=edgecolor,\n",
    "        linewidth=0,\n",
    "        alpha=alpha,\n",
    "        zorder=10,\n",
    "    )\n",
    "\n",
    "    plt.savefig(DIR_FIGS / str(filename + \".png\"), dpi=dpi)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# r_colors = [\"yellow\", \"skyblue\", \"royalblue\", \"darkblue\", \"black\"]\n",
    "r_colors = [\"#f2f200\", \"#40d3e0\", \"dodgerblue\", \"blue\", \"midnightblue\"]\n",
    "p_colors = [\"#40d3e0\", \"dodgerblue\", \"blue\", \"midnightblue\"]\n",
    "na_colors = [\"grey\", \"grey\"]\n",
    "\n",
    "for adapt_type, colors, vmin, vmax in [\n",
    "    (\"RETREAT\", r_colors, 5, 9),\n",
    "    (\"PROTECT\", p_colors, 1, 4),\n",
    "    (\"NOADAPT\", na_colors, 0, 0),\n",
    "]:\n",
    "    adapt_plot(\n",
    "        adapt,\n",
    "        adapt_type=adapt_type,\n",
    "        filename=\"optAdapt_map_retreat\",\n",
    "        vmin=vmin,\n",
    "        vmax=vmax,\n",
    "        alpha=1,\n",
    "        msize=70,\n",
    "        cm=LinearSegmentedColormap.from_list(\"cm\", colors, N=len(colors)),\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.cluster.close(), client.close()"
   ]
  }
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