Figures-final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modules\n",
    "%matplotlib inline\n",
    "# Graphics\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "import colorcet as cc\n",
    "# Numerics\n",
    "import numpy as np                \n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "# System\n",
    "import sys \n",
    "import pathlib\n",
    "import subprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rerun the script to download results from the ESGF. \n",
    "#   The servers hosting the GFDL CM4 model results are often not available. \n",
    "run_download_script = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General plotting choices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Graphical choices\n",
    "wm2 = '(W/' + r'm$^2$'+ ')' # Watts per meter squared\n",
    "figdir = pathlib.Path('.')\n",
    "fig_width = 14.2/2.54 # AGU text width is 14.2 cm; here in in\n",
    "\n",
    "# Figure details \n",
    "mpl.rcParams['pdf.fonttype'] = 42\n",
    "mpl.rcParams['ps.fonttype'] = 42\n",
    "mpl.rcParams['font.sans-serif'] = \"Arial\"\n",
    "mpl.rcParams['font.family'] = \"sans-serif\"\n",
    "mpl.rcParams[\"legend.frameon\"] = False\n",
    "\n",
    "\n",
    "#\n",
    "# Choose a categorical set of colors, assign colors for each meaning\n",
    "#\n",
    "cols = cc.glasbey_dark\n",
    "col_lw = cols[0]\n",
    "col_sw = cols[1]\n",
    "#\n",
    "# Colors for each model - I guess I want a vector in the same order as realizations? \n",
    "#\n",
    "# Function that takes a list of model names and returns the colors \n",
    "#   Brittle: fixed color mapping, fixed model names\n",
    "def lbl_cols(realizations):\n",
    "    col_dict = {'ARTS-2-3':2,\n",
    "                'GFDL-GRTCODE':3, \n",
    "                'GFDL-RFM-DISORT':4, \n",
    "                'HadGEM3-GC31-LL':5,\n",
    "                'LBLRTM-12-8':6, \n",
    "                '4AOP-v1-5':12} # Colors 6 and 7 are quite similar\n",
    "    return([cols[col_dict[r[:-6]]] for r in realizations])\n",
    "\n",
    "\n",
    "def gcm_cols(realizations):\n",
    "    col_dict = {'GFDL-CM4':8, \n",
    "                'HadGEM3-GC31-LL':5,\n",
    "                'RTE-RRTMGP-181204':10,\n",
    "                'MIROC6':9, \n",
    "                'CanESM5':13}\n",
    "\n",
    "    return([cols[col_dict[r[:-6]]] for r in realizations])\n",
    "\n",
    "#\n",
    "# Forcing indexes\n",
    "#\n",
    "def is_forcingindex(index, realizations): \n",
    "    return(['-f{:01.0f}'.format(index) in r for r in realizations])\n",
    "    \n",
    "# Matplotlib takes a single marker, not a vector of them, doh\n",
    "def forcing_marker(realization):\n",
    "    marker_dict={\"f1\":\"o\", \"f2\":\"s\", \"f3\":\"D\"}\n",
    "    return(marker_dict[realization[-2:]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_figs = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampling error (figure 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data ingest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "errs_vs_N     = pd.read_csv('forcing-sampling-errors.txt', skiprows = 5, delimiter=r\"\\s+\")\n",
    "errs_rnd_vs_N = pd.read_csv('forcing-random-sampling-errors.txt', skiprows = 6, delimiter=r\"\\s+\")\n",
    "errs_100      = pd.read_csv('Forcing-and-Errors-100-cols.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 603.78x241.512 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "showRandomErrs = False\n",
    "# Set default aspect ratio for two figures side-by-side\n",
    "aspect_ratio =  .4\n",
    "size_mult    = 1.5\n",
    "mpl.rcParams[\"figure.figsize\"] = [fig_width * size_mult, fig_width * aspect_ratio * size_mult]\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "ax1.errorbar(errs_vs_N['N'], errs_vs_N['Soc-mean'], errs_vs_N['Soc-stdv'], label = \"SOCRATES mean\")\n",
    "ax1.errorbar(errs_vs_N['N'], errs_vs_N['R-mean'], errs_vs_N['R-stdv'], label=\"RRTMG mean\")\n",
    "ax1.loglog  (errs_vs_N['N'], errs_vs_N['Soc-min'], label=\"SOCRATES min\")\n",
    "ax1.set(xlabel=\"Number of samples\", ylabel=\"RMS sampling error in forcing \" + wm2)\n",
    "ax1.tick_params(\n",
    "    axis='x',          # changes apply to the x-axis\n",
    "    which='minor',      # both major and minor ticks are affected\n",
    "    bottom=False) # labels along the bottom edge are off\n",
    "if showRandomErrs:\n",
    "    ax1.loglog  (errs_rnd_vs_N['N'], errs_rnd_vs_N['Soc-mean'])\n",
    "    ax1.loglog  (errs_rnd_vs_N['N'], errs_rnd_vs_N['R-mean'])\n",
    "\n",
    "ax1.set_xticks(errs_vs_N['N'])\n",
    "ax1.set_xticklabels(errs_vs_N['N'])\n",
    "ax1.legend()\n",
    "\n",
    "ax2.scatter(np.abs(errs_100['Etrue-LW']), np.abs(errs_100['Esamp-LW']), label=\"longwave\",  c = col_lw)\n",
    "ax2.scatter(np.abs(errs_100['Etrue-SW']), np.abs(errs_100['Esamp-SW']), label=\"shortwave\", c = col_sw)\n",
    "ax2.set_ylim(bottom=-.001)\n",
    "ax2.set(xlabel=\"True absolute error \" + wm2, \n",
    "        ylabel=\"Sampling error in true error \"+ wm2)\n",
    "ax2.legend()\n",
    "sb.despine()\n",
    "fig.tight_layout()\n",
    "if save_figs: fig.savefig(figdir.joinpath(\"Sampling-Errors.pdf\"), dpi=600, transparent=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Line-by-line and GCM results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data ingest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "if run_download_script: subprocess.run([sys.executable, \"compute-lbl-gcm-global-mean-forcing.py\"])\n",
    "lw = xr.open_dataset(\"lw-lbl-models.nc\")\n",
    "sw = xr.open_dataset(\"sw-lbl-models.nc\")\n",
    "temp = xr.open_dataset(\"gcm-models.nc\")\n",
    "# GFDL, RRMTPG, and HadGEM have updated spectroscopy\n",
    "gcm = temp.sel(realization=[r for r in temp.realization.values if \"GFDL\" in r or \"RRTMGP\" in r or \"HadGEM\" in r])\n",
    "\n",
    "toa = lw.isel(expt=0).plev.argmin().values\n",
    "sfc = lw.isel(expt=0).plev.argmax().values\n",
    "\n",
    "# Some of the RFMIP experiements include changed temperatures, \n",
    "#   sometimes with associated changes in water vapor\n",
    "# We'll focus on changes to gas concentrations only \n",
    "expt_labels = lw.expt.values\n",
    "isForcing = [f for f in expt_labels if (\"+4K\" not in f and \"all\" not in f)]\n",
    "# We'll report 3 groups for forcings: \n",
    "#   scenarios (PI, future, LGM; 3 values)\n",
    "#   PI forcing (total and by constituent; 6 values)\n",
    "#   and idealized CO2 forcing (4 values)\n",
    "piForcing  = [f for f in expt_labels if (\"PI\"  in f and \"all\" not in f)]\n",
    "co2Forcing = sorted([f for f in expt_labels if \"xCO2\" in f])\n",
    "scenarios  = [\"PD\", \"future\", \"LGM\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example results: forcing at present-day (Figure 2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_forcing(ax, fluxes, expt=\"PD\", forcing = True, band=None, ylab_format_string=\"{:.3f}\"): \n",
    "    toa = fluxes.isel(expt=0).plev.argmin().values\n",
    "    sfc = fluxes.isel(expt=0).plev.argmax().values\n",
    "    if band is None: band = \"l\" if \"rld\" in fluxes else \"s\"\n",
    "    q = \"f\" if forcing else \"r\"\n",
    "    net_forcing  = fluxes[q + band + \"n\"].sel(expt=expt)\n",
    "    abs_forcing  = fluxes[q + band + \"a\"].sel(expt=expt)\n",
    "    realizations = fluxes.realization\n",
    "    #\n",
    "    # Plotting choices\n",
    "    #\n",
    "    inc = .0375 # Worth trying... \n",
    "    n   = realizations.size\n",
    "    deltaX = np.ma.masked_array(np.linspace(-inc*n, inc*n, num=n))\n",
    "    \n",
    "    for f in [1,2,3]: # f is forcing index\n",
    "        # TOA, surface, net absorption \n",
    "        mask = is_forcingindex(f, realizations.values)\n",
    "        all_lbl_cols = lbl_cols(realizations.values)\n",
    "        these_cols = [all_lbl_cols[i] for i in range(len(mask)) if mask[i]]\n",
    "        ax.scatter(0 + deltaX[mask], \n",
    "                    net_forcing.sel(level=toa, realization=mask), \n",
    "                    c=these_cols,\n",
    "                    marker=forcing_marker('-f{:01.0f}'.format(f)))\n",
    "        ax.scatter(1 + deltaX[mask], \n",
    "                    net_forcing.sel(level=sfc, realization=mask), \n",
    "                    c=these_cols,\n",
    "                    marker=forcing_marker('-f{:01.0f}'.format(f)))\n",
    "        ax.scatter(2 + deltaX[mask], \n",
    "                    abs_forcing.sel(           realization=mask), \n",
    "                    c=these_cols,\n",
    "                    marker=forcing_marker('-f{:01.0f}'.format(f)))\n",
    "\n",
    "    sb.despine(bottom=True)\n",
    "    ax.set_xticks([0,1,2])\n",
    "    ax.set_xticklabels([\"TOA\", \"Sfc\", \"Absorption\"])\n",
    "    quants = [net_forcing.sel(level=toa), net_forcing.sel(level=sfc), abs_forcing]\n",
    "    y_vals = [x.mean().values for x in quants]\n",
    "    ax.set_yticks([x.mean().values for x in quants])\n",
    "    str_fmt = ylab_format_string + \"\\n\" + \"[\" + ylab_format_string + \", \" + ylab_format_string + \"]\"\n",
    "    ax.set_yticklabels([str_fmt.format(x.mean().values, x.min().values, x.max().values) for x in quants])\n",
    "    if forcing: \n",
    "        ax.set(ylabel = \"Forcing \" + wm2)\n",
    "    else:\n",
    "        ax.set(ylabel = \"Flux \" + wm2)\n",
    "\n",
    "def make_legend(fig, realizations, x_left = 1.5, y_top = .1, cols = lbl_cols, band = \"Longwave\"):\n",
    "    #\n",
    "    # Generalize this - we're writing colored text at a set of x,y postions \n",
    "    # \n",
    "    names = np.unique([r[:-6] for r in realizations.values])\n",
    "    # Because it's last in the dictionary and everyone else is alphabetical\n",
    "    if(\"4AOP-v1-5\" in names): names = np.roll(names,-1)\n",
    "    bottom, top = fig.get_ylim()\n",
    "    y_size = top - bottom\n",
    "    left, right = fig.get_xlim()\n",
    "    ys = top - y_top - np.arange(len(names)) * y_size/22.5\n",
    "    fig.text(x_left, ys[0] + y_size/22.5, band, color = 'black')\n",
    "    for i in range(len(names)):\n",
    "        fig.text(x_left, ys[i], names[i], color=cols([names[i] + \"-p1-f1\"])[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MJfmTog6LCdRosq5wwFiduZrCYCHPzXuOqydehXOOi/a7iDgre7NvGKkJqRzd7Wiu/nFFmZAAkFsc5PqfVgBEDRgA2cWuQsAAytRoZBY1rKZT5QMGeFWK7/55AlP+83FMzrFtK8z4GuZ+C8HimBxSROpOOrA94n2xWZkv4NfwHmSNAA41s+MBnHNvA+XbxporfQKYBTStnSKLNE6zM7IpDFa898opDjJ+xcZq9z9q6sKoASN8jJFTF/LT9ootSerN5gJ4dDFMWApFpfdu/Y45gITkBChXm+GP83PI+Uft8mkb3uPmKphZZ7wq58edc69ELN8XOA7ojtdc6iUzO8059yaAc+48M/sL8I2Z9XfOxexfzpCOQ0iOSyarsGw7vibxTbh4v4sBaPpQeoX14W3aNmnL6szVFAW9a1RuUS7PzXsOgMdG/ou3Tnmbcz44m8LiQoqDxXRO78x7v3+fgPOxKjf66AFzt1X/8fKKoweQwqBjWU4+v+UVkb6LHYbqw/XpZ1ZI9FUJBw1/nJ9DLz2m0u0mfw7FUYKD3w/DR3kB44c54IKwfRvMnQP7D4Jd7PsvInUnE4h8dOlzzgUAzGuP8Ihzbnvo/UfAAcCHlRwr8gs2DdgW89KKNGJB5yp9/F6T9j0pfl+omVH0rX1mxJux/o10XKDi/ZvFpdHu9IotWGrF5gJ4ZgnkF8NvefDCMji3B8T7iEuI50+T7+Kpk/7BtrVbMJ8Rn5zAeS9fR4surXf51HveXeBOMrO2wOfAlc65L8ut3g7kAXnOuWIz2wg0N7Nz8Kqs/wHk4n3xx/QZs9/n54PTPuSY10ZR7IopdsUEg0Eu3f9SRvcYDRA1YIDXiTsyYIRFBo1/jfo3665az/xN80mOS2avFnthZgSdo0mcL2ptRdukhGrL3Szez7ai4gp/Xil+Hx8N60vf9OQafPpqrMqBuRkwpgPE7WClWyAIq3KhR8X+Jbnbslk08Ud8fh/9jjmAxCalk6ftSMCItGzGz1WGjGgBI7w8HDDCtRdBBQ2RPdF04ATgjVCfjMjJGtKB+WbWD6/vxQhgfBXH+sHMhjvnJgPHApNqp8gijdPBLVKJi9IXoYnfx7iu1d9cfzysH8dO+5lvMrIrPHRNi/Mz5Yi96ZeewrooAQOIGjx22t3zoTDKg98EH1zauzRgOCDgKgSNdv0683+L/8OGxWsJ5BfSYZ+u+PyxufFoNCEDuBloDvyfmf1faNnTQBPn3FNm9l9gmpkVAkuBCUA88JyZfR16fY1zLj/WBTuo/UGsvfI3Pl76MRkFGRzV9Si6N+teo33LB4yw/EA+by16i4ePfoQ4Xxz7t92/zHqfGdf0as+Dv66r0Cfjr306VHvemUfuw5BJ88oEjXDAGN666a6n91U58MJyCDrIKISzutU8aASC8OoKWJoNh7eBEe1KVs1+aTKvXvI4/jjvDygYdFz45p/Z+9iB1R42ISWRwnK1P/EpifQ9el/OevYqALIyvYCQlAQtWtWsb0VkwAhT0BDZ47wDjDSzGXhtlM83s7FAaugaczNeWCgAvnTOVdXO8nrgaTNLAH6mFprqijRm8T4fbx6yFyfOWETQOfKDjhS/j1Ftm3FG51bV7p/k9/HJoRWDRjhgHNCsSW1/hFLRAkZ4eWTACIsSNMyMdn07xbxojabjd20ys0uASwC6dOkycOXKlTE9vu/eyvszJscllxmZCsBnPlolt2LWud/QrVm3Svctdo4b563kiWUb8OFdFW/q25Gb+3TEzKrs9OROHcLirLySoJEcETAA1r1SeZnbj63m31w4YITbDcYbdG5Ss6ARDhgrc6DIefsObQ0j2rF5+Qbu3vsqivLKdoRPSEnk76ufoUmLNK60kys99MibTmXKYx+WBI1wwLjo7Zswn595P8DWzd62ZhAXDwMPgeRk+PKTqotdGTPYqz906rJz+zdGy7LzuXH+StbkFZLi95Hs93H/Pl35dms2f1u4mh4RNVfX9W7P1sJAyfIgXhX6bf06M6JNUyZv2s6Tyzbw2sF7VXnOH7blcMv8VWwrCpDk99E8Po7H9u9Gx+RExs1ZwvcZObRIiMPh2FIY4PreHTi+fXNOm/ULAHO357BXahIpfj/ndGnFhd3b8u7arTy6dB3Oec0T/7xXB37fqSUAP27L4ab5q8grDlIYDHJk66bc1r8TCT5fmfMFnKNVQhwP79eN7k2SuH3hal5ZvZkOEbWVI9s25ZZauLjUg3rt+F3farPjt/6mGu3fVIO3uaCI11ZvZmtRgBGtmzKsZVrJaEvVjS4FkF8c5NhpPzNjaxaJPl+FgLFL90I1ddtPO7ef37wHscPb7moJKv2Qjakmo9Y4554CngLvi74uz/3k6Ce57NPLSoJGTQMGgN+MB/ftxt/7d2Z9fhEdkhNI8pfexKdV0pwqLXSj3yctmZlH7sMpMxfznwO6lwSMGqmsei/OvDvriI5JFDlYnQMvr6g6aJQPGOF9Z2wCYM6sqQSjdWY3+PGdWQy9cGSVRT7xnrMBmPLYhzgoCRj+OD8rl3sBIxhx+OJimD8XDhpS5WHx+aN39Pb5oGkz6NCx4jqJLjdQzIkzF/H0gT1LRvaYvTWLK35YzriurRnbuRX37tO1zD4TVmwss3xDfiGHT1nAlCP2rtE51+UVctbsX3lnSB/6pHnNBN9du5Ub563i5cHeHAH379OF0e2aA7C1sIi9J/7IuK6tmRw6x/ApC3jygB4lzQxnbMni4SW/8dGwfqTG+dlSUMQhk+bTPz2ZlglxnDn7V94d0oe90pJxzvH3RWu49scV/OeAHhXON3VzJqd/8wvfjtgX8G4CL+tRWrsne65yD7hq5Rz6m9LfVEPWKjGeK3u1j7ouHCSqEq7RuO6nFVzSvS3712UNRk0k+KLfa/mB5glwcMtaPb1Cxh7unAHnAnDZp5dRECioccCIlBLnp0dqxfY4NfkD65OWzIJR+9f4XCUqq94LOKJ2pAoHjfIhIpLhhZTy60JBo3t+U4oDFUe9Cha7MhPRVMbMOPGes/HH+9n062+c++K1Jc2u1q4qGzDCsjKhsJpDHzCoYpOpcMBQU6kd88G6DEa0blpm6MDBLdKYdHh/nl+5qUbHaJuUwKkdW/Lhugx6pSZVu/0LqzZxUbc2JTdDACd3bMFJHZpH3X59fhFJPl/UscnDnl6+gWt6tSc19O+rZWI8s0fsQ7N4Pw/+uo4LurVhr9D5zIz/69uJHp/+QF6Uzj+HtUon3owl2TvX30h2X3XxgEt/U/qbkqol+X08fkCP6jesD+f1gOeXlb3n8gPNE+GinpBcuzGgTkOGmY0D7gQewWtjOj5UBgMucc4tjth2NHBT+C1wKDAAyAeeDy1bGdovt5LzNQVewut0lwBc55ybWW6bG4Az8Tp13+Oceye032tAE6AQbyK/XsC/gUXOuRoNHBwraQlpUTt/pyV4X/rhoHHntDuZeMYXOxQw9igO7/9StIARXl/ZuiLHXnHtSE1PJXt72Vk9zWDvMV6fjMS05KidvxMjLj7H3zm24qkryUyGFz78/spHl2rWomzQUMDYectzC8rcxJw0YxHbi4pZl1/IOV1a88rqzcwKzeraOjGONw/pE/U4bRPj2VwYoFfUteXOmVPAmHbNAMgrLubYaYsAWJ1XwNLR3kRkN85bxd2L1rIyt4D+6cm8eUjVTUV+yyss0wQFKJl7ZllOPiPblK01NDPaJcWzIT96H622SfFsLvAC9kO/ruO11VtK1t3StyMj2zarwSeVxkh/U/qbkp1ncWmV9k+tE51SygaNOgwYUD81Ga845x4KzT3xb+fcu2Z2DPAP4JTwRs65T4FPAczsz8B059zPZvYW8KRz7hUzuwi4DrirknNdh9fB7hEz6wO8ijfrKqHjNgOuxgsQTYC5eJ33xgHznHM3mtnFwJ+dc9eb2TV445zXqe3XVd9R+pwB55aEjT1efJTaiDiDzilec6m/z4+6W8l2gShBI97grG7svXIwP7w53au5MCMhJYHhVx9P61B16YOZr+5Ukdu0h9UroHwXp8Qk72f4qKr3DweN779VwNgVnZMTmJNROgTze0P7AnDIpHl0Sk6I2rQjmpW5BRzYPHq195LsPC76bhkA53RpReeUBJaHasKS/f6S5hrtPixtHx9uavHxugz+Mn8lPZtU/TS3a0oiq/MK2S+i6n365kzaJsXTMTmBFeUGICh2jt/yC2mbFF/J5ymkU7LXZryhNO2YMGECf/vb37jmmms499xzad269edAMvAbcH60h09mlgLMAG5yzn1qZo8A+4dWtwO2RUyIlwJMBC50zi2qrByhkaQeBQLA5865O8qtN2AN8Gto0Uzn3F/N7DDgAbzHIx875+4Mbf8+0BJv7ow84DS8oW77Oufq/H+c/qYaz9+UxF6dDFNbWZOohFDT8nDQmLAUmibUWcCA+m0udT2lExfF4dVQVGBmnYBzgINCi/oDF4deTwceruIcD+ON5FHZOXLwakOahH7C/5fmAX1Dr9OpOFGSVGOX0vs5PeDFZaVBIxwwzu5efcfvke1h4rqyQSPe4OzuWLdUzh5/FYPOPIw5r07FH+/n4HOPpOeh/Xfgk0XXvSds3ggF+V6NhfnAZ7D3fhXmuKlUsxYw9HDN+L0rTurQgnsXr2XWliwOCTXvWJKdx5q8QlZWMi9MeevyCnlv3VZu7deRn7ZXrCTtlZpcctMDsDq3gGOn/8yx7ZqVNLf4LiOb7EDFqqsx7Zszc2sWl3y/tNInvgDnd2vDTfNXcmTrdJrE+dmYX8T53y3lrUP24rwurRk57WeOb9ec3qH243csXM2Yts1JjjLs4MQN20jx++iUklijz78nGTt2LNdddx1XX301eA+wJpjZTcClRL82/IeI9pjOuWsAzCwemEbo2mJmg4AngZr03n0SOBVYBnxkZgc6576PWN8T+N45d0K5/R4Bfu+cW25mk8zsA+fcD3gPvfaOmIwPYLiZra9BWWJOf1ON629KdlOFQVi8HQY0q3hTccuA6vfvlAJX9IFkPyTV3RPMegsZzrnNAKEahgeAkyvZ9DrgYedc+NtsLnAiXpOpE/HCQWXn2BY6Rzu8ZlPXRNlsNbAQrxLpH6FlW4BRZrYQaAEcVqMPJSV2Kb13bVIaNILUPGAAHBIaei4cNEIBg27efBlmRr9RB9Bv1AE7X74o4uLh4GGwcT1kZEByCrTvCIk7eA1KisH0Io1ZapyfD4b25ab5q1g3fyUB52XUpw7owcaCIhZVMg9KuMmH37w70OcG9qJFgvcE8/MN2xj0ZenoHa8M7l1y4wPQOSWRlw/qzfU/rSQrUEx+MEjz+DgmHhY9vP5fv04c+OVPfLQug+PaR29jPqRlGpd0b8vIqQuJ9xl5xUH+MaAL+zb1vu5ePKgXl89dXjISzvDWTXlkv24l+984bxX3Lv4Nv3lDKr5+cO+SdeWbdvRJS+K/B/as+he7m5s2bRqEar6BT4B7KBcyQk1jw8PLlncVXi1EeG6LROB3wItVndfM0oFE59zS0PvPgKOAyJAxEOhoZpPwaiauDTUNPtg5FzCzVLwZvbeE5nNqBnwQqmm/1zlX2YR9dUJ/U57G9jclu5HCIDy3FNbnwbIcOLFjzZ9eRmpe/RxosVanQ9iG+mT0dc7dFHp/JPA4cI5zrsLYe2bmAxYB+znn8kLL2uP1jWgCfAkMdc79ropz7oPXv+IG59wn5dadCFyLN9kRwGfAn/H6gnzmnPtvaDbwl5xz+5rZcOCyqvpk1OYwgg1KVZPHhFP5yhyYuxWO61g2YFQ1XNsd3mgfzNrsBY1zSgOGiDQcEyZMYNGiRdx777306tWLpUuXpjjn8sysB/CCc+7Q8LZmdhRwunPuUjObALwWapJLaC6KecDg8IzcEftNxvvOj9pcKlTT/rZz7uDQ+wuAHs65WyO2ORxo65x708wOxXtodlBo3SF416eFeLUhrYDT8ZpftcCrrR/mnNtoZuurai6la4/IHqi6e6FwwNiYX/rgdJ/mOx80asfuN4RtKGA8Cox2zlU2scQAvI7WkY9KRgJ3OOd+MrPr8drMVnaO/sCbwB+ccz9G2SQD78lSgXPOmdk2vKdIGZQ25dqI12RKYqkm1Xtdm3g/5VXX/hC8Go1BLXZ8pnAR2eOkp6cDpOF9n6cB28ptciHQNRQa+gIHhm7a5wJHA1+XDxg1lBk6X1i0c8/B66+Bc26amXU0M3OeWUA3M7sL7+HWXXh9DgPARjP7AeiDdx0SkYamqon0ygcM8JqRz8vwXu9eQSOq+uyT8QjeiE/Ph4adWxx6ynQ/8JZzbjbel+uycvstBsabWQGwALgCINSBb0LoohH2DyAJeDR0ju3OuZPM7DpgiXPufTM7GphlZkG8NrkTgfnAM2Z2Od5M3xcju4+aBBRQwBBpJIYNG8YPP/wwBpiAVzM9NXK9c65kSLiImoy5oUVH4zWx2mHOuUwzKzSznnjXqmOAO8ptdhteE9z7zWw/YFWoHFOBE51zGUAW3rXqaOBK4LhQM6oBeDN+i0hjUz5ghO1BQaM++2TsV8nyGyNev4lXExG5/htgUJRdlwLZ5bY9qZJzPBTx+ja8i0Ck34AxVRRfRER2E7feeiv//ve/zwiNBrgZGAtQ7qFVZfoAL1R3jlDfvkeiNJe9DHgZr1/f56FrFGb2OXA8cC/wkpkdh1ejMS5Uc/4A8Enogdk64CLnXLaZHWNms/B6pN0c7r8oIo3M+rzS4YjKK3Iwfxsc2wESFDIijTWzjZE3+jHynnNuVYyPWSLUlvYRvD4iIiKym2jbti3OudHll0c+tIpYNq7c++MqO65zbnjE283A2ijbzAIOibI8PHB1IVDhHM6594D3oiy/prLyiEgj8rvO8N6a6MPyJ/rg/J5lm4nvhuo0ZDjnJuBVZ9fGsWstYISOP43S8dRFZDcVyF5G1g83Upy7BotLwfzJpO1/P0VbvyXrp78Rl1o6M2uTvtcRLNxautwFwYzUAbeR2G4EBRsmk/vrkzQ/9LVKz5e7bEKZ4wYLt5HQehhND/oPgewVbP54X+JblEzPQ0LbESR3P5dN73cnbf97Se3/l5J1W6eciCvKpOXRkys/R7kyBrJXsG36GbQ6ZhbF+ZvI/PYygkXZgMPfpCtNBz6GxSUTLMom68dbCGT8AObD4tJIP/BB4tL3omDDZLZNO524pv0BI1iUSVxqD5oNfZmsH/9K0dbvKM5fjwvkEpfaA19ia5ofVqaSuV688sortGnThuuuu64uTmfAP+viRGVO6jWbqtcRpkSkHuwbGimtfNAIB4z2u/9wlPXZJ0NEJKZcIJeMKSfSdPDTJLQeAkDh5tlkzrmC5B7jSO42lvT97y2zT+6yCWWWF+dtYMsXh9Py6Ck1Pm/k/s4F2TLxMAq3zMGX2Iq4pv0rhIZA9gr8qT3JX/VWScgIFmwlkPUr/qS21Z6jsjLm/PxPEtqNpElvb87Q7d9dQ86SJ0ntey3bZ19MQquhNB30KABFGT+S8fXJtBw1E/DCT2SYypg+lvy175N+4IMlv6dA5qIKv7/6Mm7cOMaNG1dn53POFQF1PleFcy4bGF7X542k4N54grvUseoGsikfNPaggAEKGSLSgOSv/YCEtiNKAgZAQqvBtDhqEnnLn6/RMfzJbUnqfCoFaz/En9Zrh8vgirJwRdvwxTetcjtfYit8iS0p2v4z8U37kbfqdZK7nEbhxq93qIwJ7Y4uXd6kK/mr3iIurRcJrYaRfsADgBHM30xg2zyaDyud0T6++X4kdjyB/NX/w5/avexnKC4kmLcOX0L0OQek8VBwbzzBXepBTQayCQeNT36Dc3vsMQEDFDJEpAEpzl5OXEQw2DrlJFzRdorz1pHc/RzyVrxC0eZZAFU+OfQntSVYsLnGIcM77kyK89ZhcWmk7n0Lcem9CWSvILB9IVu+GF6ybbOhL5e8Tup6JvkrXyN+3zsoWPMeafvdU6OQEVnGSCm9/4j5k8le+E+KtpxGQutDaXrQ4xTnrcOfWnFyMH9qD4pzVuJP7U7hhq/Y8sVwgvkbwXyk9LqExHZH1ags0nApuCu4y25g3+awT7PdeiSpaBQyRKTB8Kd0pmhr6YRkLY7w+tVu/uwQ/Cmdoj51jaY4ZyVxEc0xIgWylrD9m4sASO5+Dpi/5LiB7OVsnTQaf9peJdtX9tQVIKnTyWz54jCSe5yPL6kd5k+p9Bw1KWPhhkkkdz+XlJ4X4IoLyF54P5nfX0P6wEcpzqk4HVFx1q+h5hylT12DBVvY8tVI/E26V9heGh8FdwV32U3sYQEDYPfuli4isgMSO51EwfovKAzd9IB3w16cuybqTXY0xXnryF/7Hkkdoo9iHZfWi5ZHT6bl0ZNJ6Xlh2XWp3Wl60H/YNu00XCC32nP54lOJS+tD1twbSe5WMpVDleeoqow5ix8lb9lzAJg/kbhme4MvEX9KJ+LSepLzy39Kti3a+j35az8gqfMpZcuU2JJmQ19i++yLKM5bV+1nkIbNn9KZ4uzlJe9bHPEeLY+ejC+heUlwD/9brapPQXHOSnwpnaKuC2QtYcsXw9nyxXBylz4LEDruFFoc+RkukB01uId//CkdS9YldTqZ/LXvE8heUSG4lz9HTcoYDu4tR3xG21PWE99yMJnfX4M/pUOlwd3fpAvgBfeWR0+m5cip4EtQcJdGRzUZItJg+OJTaX7EB2TNvYms/HW4YADzxdF08FMECzYSyIw+AnXJ01jzA46mhzyHL7EFAAXrP2fzp6VT8zQb+gpx6XtFPQ5AYrujSWh3NFnzbiOl9xXVljm521lsn30JzYa+SnHWr5VuV1kZg0WZJds0PehJts+5nJxfH8f8yfgSW9P0oCe8dUNeIOuHP7P5s4PB/PgSmtP88HfxJTSrcK74pv1J2etqMudcrc6ojVxip5PIXngvhZtnkdDKG6l3Z4N7qwG3UrTtpwrrw6E6LHfZhNJ1EcG99XELqj1XZHBP6XlRjc5RvoyRf1M5ix+lOHsZKb0uLgnugcyfywT3Jnt5f+fh4N5qwK0UZcwtLVMouG/98kjiW87Fn9y+2s8h0hCYc1HG35WdNmjQIDdnzpzqNxQRkVja89oSxFBtXnsC2SvImnsTwYjg3qTfX0qCe7SO3yUjN4VCceo+t5PY5nAKNkwmY+opZUakKh/co3WI3vbNxfgSmpHS+4qSkZ/KlzG8PH/NB2yffQltTl5DcdavbP/2sqpHlypXxjKjS+X+xvY5l1Ocs7JMcPendCAYyCHrhz9TtPW7kuCedsADxDftF3UUraz5dxPImFsS3NXxWxqISr97FTJiTCFDRKReKGTo2iMida/S7171yRARERERkZhSyBARERERkZhSyBARERERkZjS6FIi0nAsz4Y5W+C0rqXLJq6DVolwQIuq9/01C+Zvg991hmIHX2+EXzMhLvQsZt9mMKglZBTCE79UnHX1vB4wZQNM2QjX9YP0eG95dgAeXAgndvLK8MV6WJbljXl+bAfolFKxLDkB+HwdbMqHeB/4DIa3ha5NvPUrc2DyBgg6KAzCAc1hcCvv/ftrYHOBt8/JnaBFYulxP/nN+10c1LLiOd9ZDQOaQe+00mUZhfDWKrh4xydQExGRxk0hQ0SkvC/Xg3NwUS/vZr2gGF5e4d3kx/mgdRKcX3EiLgBaJsKCbTCktfd+/jZomuC9XpcHa3K9m/ZtRfDqCrg8ynC4r66AYa29wAOwtQBeXwmX9IbthfDxWjinO6TGQ1EQJiyD5okQCHrbX9TLC1yfroOx3bzQ8r/VsKUAWrWO1W9JGgMFdwV3kZ2kkCEiDV8QeG81bC+CvGLolQZHtfNuON5dAwk+78Yj2e/dDC3YBlf39W4qABL9cH4P7yYmo7Dqcw1oCgu2l4aMXzKhT7r3un2yFw7MvLCQGuUreE0upPihX9PSZS0S4bLe3n4/boP9mnsBA7xyn9Pd+ww+g71C54o8fmEQjmzr3eCJ1AcFd5FGRyFDRBqW5dnw3NLS9xmF3g12pxQ4qaV3A/HQz17I+GoDjGgLPdNg6kbvaWVuwAsb/lDAmL3Fu8EpCMJ+zaBvUy+cRJ6jfTKM7uC9To33bvy3FoDDe/oaFzHCn9+8J6/fbIYxHSqWP6Ow7JPS99d4NzI5ATipE2QVQfu0svsk+cse/3+rYdF2OD309Ll5gvejkCGxouCu4C5SDYUMEWlYuqdWbNpREISN+bB8FST6IBCaH2hjPnQMNa3o0sQLGSlxkFvsNZPwGQxu6f18uwWyi7xtq3rqCrBPM5i/3bu52rc5LM0qu/7odnBYa3h6ifck95PfvJuWNkmhfSNuuk7s5P33zZVeuZsleDd2kdbneYEm3NzklM6Q1c47/pV9vJul8hZs8wIUwDGagViqoOCu4C6yExQyRKRxSPJ7N+xbCuC7rV7TjVaJsDrXazO9Ntfbzm/Qv6nXvOOodl7QKAp6T0ObJdTsXP2bwgvLvKe1R7QpDRnLsmHhdji+o9dExG/ek9Szupfu65zX5nzRdu/mC7yboM0F3pRH+zSD11Z4bb2bxHnNTj5Y651nY74XQA5v492UGZVPk7R3M++nxJaafTZpfBTcFdxFdoJChog0fIbXCXVVjnfz3TIRsgLezf6bq2D6JmjiL+2QOrK9t+y5paXtx/s2haGtvaef5Z+6ApzcufR1kt972toisbR5CEC3Jt6NyDNLvBuYg1p6T0PLlNW8Nt9frPfKAF7TlKGtvZsnM698r60IlS0IB7bwmnQUBuHd1TB+qXczdmwH7/PW1Cdr4avQE9yWiV7I2pgP//21dJtj2kO31JofUxouBfeyFNxFyjDnXH2XoUEZNGiQmzNnTn0XQ0Sksans1q9RqLVrT2WjS6XHw5ytXi1GvA8yi7zRoAwvuPusNLiHR5eavslrXhQtuEcbXerkzjB3q9dc6qCWXhBokQij2peOcLVfc/hoLWzI94L7Ac29EavKywl4wX1zvvc+iFebsm8zL5QsyfJGsooM7oNblgb37ID3GQ5rXRpUACatLy1fee+shtU5XjCC0uD++C9e2cMU3GXPVul3r0JGjClkiIjUC4UMXXtEpO5V+t2rGb9FRERERCSmFDJERERERCSmFDJERERERCSmNLqUiDQYv0yex7QnP+OC124oWfbI8Fsoyi0kPiWBotxC+o7anxPuOostKzbw3BkPcsOs+8sc48Vxj7L6+2WktEgFBzlbshhx/UkMOH4Qz57mbbt27nLa7NWR+JQEBp8znIzVm/n072/y99XP0KxDCwCyNm7jlo4XMvbpyzlk3FEVyhosLmb8Hx5g6EUj6T/6QADe+fMElk77mWCgmGGXjGLYxaMq7Pf2deNZNu1nzGf87sHz6TmsX8x+fyIiIrGikCEiDd45L/yJdn074Zzj4cNuZuWcJaS2Sqt0+5PvP6/kxj9naxZ37301h4wbwTWT7wa84HLGk3+kXV9vvP2Pbn+VNnt14Ic3pnHkNScC8N3r02jRpVXU429auo4Xz3uMjNWbGXrRSAB+mTSPTUvWccPM+ygqKOLuva/igN8PJaV56agza35czvIZi7jhm/vZtGQdz53xAH/57qFd/wWJVELBXUR2lkKGiDQagYIiiosCNO3QguLCoup3ADLXbyM+KR6zqgcvOvAPw/jhzRklIWP+B98y4ISDom5bkJ3P2KcvZ+J9/ytZ1n1IHzrt743tbwauOIg/3l9mv2YdW5KQkkigoIj8zFz88foKl/qh4C4i1dEVSkQavBfPfZT4lAS2LNtAx/26kdoqje2/ba10+3dvfJ7P7n6TrSs30a5/Zy5488Zqz5HerjnxKYlsXrYeF3Q079yK+KToE4112q97hWXxSQnEJyVQXBTgxfMeY+glo0hMLTtvgC/Oh/mMv/e9grztuYx9+opqyyVSmxTcRaQy+msSkQYv/NQ1GAzy8gX/4ov73+Ggs4+odPvwU9cFH8/h3b+8QOue7Wp0nkFnHsZ3r02luKiYQWcdwaLP5wIw5d8f8cNbMwAY9/J1NOsYZeIuIDcjm2d+fx+9hw/gmL/+HoAnjr+Lguw8OuzTlTa9O5DerjlXfHYb+Vl5PHzozXQf0qfS44nUFgV3EamOQoaINBo+n4+mHVsSKAzUaPu9xwxi+czFvHrJ41xYg5ui/U8dwr9H3U5SWjKj/+/0kpBxxJXHccSVx1W5b2FeAY8d9TeOuv4kDjqrNAD98cNbS17PfnESCalJ+Px+ktKSiUuMoyA7v0afRSSWFNxFpDoKGSLSoCz6fC73Dbq+5P3237aWPHUFSEhJ5LyXriU/M5ff5q8qs+0pD55f4Xij/+907jvweuZ/NIcBxw2q8tzJTZvQrFNLWvdsh8+3YyOET3vyM7YsW8/0pz9n+tOfA3D2c1fTqnvbkm0GjT2cpdMX8eDQvxAsDjLorCNo26fjDp1HJJYU3EWkMgoZItJg7DV8H+7f+lKNtm3SIo2Hsl+rsLz3EQPKvI9LiOeW+Y+VWRburBp23O1nlry++O2bSl6fdO+5VZbhnAl/Knk94toTGXHtiVVu7/P7OfPJP1a5jUisKbiLyM4w51x9l6FBGTRokJszZ059F0NEpLGpuhdxA6drj4jUk0q/ezXjt4iIiIiIxJRChoiIiIiIxJRChoiIiIiIxJQ6fotIg1JYALNnwAEHQZPQxL7fTIOmzaHv3qXbBQKw9BfIzgQM4vzQux+kNIGMLTBvbun+wWJo1wE6d6t5ObZvgyWLYeDB0dd/Mw3i4r3XycnQf98d+5wiIiK7M4UMEWkwgkFYtAAiB6HZlgGpaV5wCAQgLvStt2g+NG0Gffp777My4cfv4KAh3vvmLWGf/UPHLYaZU6FdR4iPr74cK5fBurXg90dfX1zs/beyACIiIrKnU3MpEWkwfl0EHTtDYlLpst9WQ5t20Lqtd+MPUFgI2VllaybS0qF1G9i4oeJxA8Xe8BlWbgyNn773wgtA5jYvpAAkp8C+B1ZezuwsLxD9MBu+/wa2Z+zgBxUREdnNKWSISIPw2xpISICWrUuXBYq8moyWraFDJ1i7yluel+sFgfKSUiA/z3udsQW++8YLAQt+hL36l9aChHXoXBpcflvrBRzwQk35QBLJ74cu3WH/g6DvAO/4weDOfW4REZHdkZpLiUiDsG6N99+tW7x+Fgt/8movnCutYSgogK2bvX4X4TARKS8XmjTxXkc2lwoLBEqP1aIldOsJSxZBUaFXGxFuelVe+f26dvdCjplXlvgEry9JUvIu/QpERER2GwoZItIgDDyk9PV333idvBf8CPsN9PpkAKxfC2tWeU2ZklNg9Uro3NVbl7kdNm+EbkNCncGjiIur2I+iTTuvH0irNpXXXpTfb81KyM72yliQ74WQhMSd+9wiIiK7IzWXEpEGKVDk/TccMABat/OaT+Xnwd77Qk42fDsD5syEZb964aMmHbsjdegEmzZ4/63xPp298s2Z5Y1i1W+fsp3VRURE9nTmnKvvMjQogwYNcnPmzKnvYoiINDZV9IJp+HTtEZF6Uul3r56diYiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITClkiIiIiIhITDWakGFm8Wb2oplNNbPZZnZiufUnmNm3ZjbTzC4ut66Nma02s751W2qRqq3LK6SgOBj7A2/Kh6eXwMb82B9bREREGrxGEzKAs4EtzrnDgGOBf4dXmFk88DAwCjgCuMTM2kWs+y+QV+clFqnC9xnZ9PrsB46eupC84uLYHXhTPjyzFNbmwrO7V9AoyMnnl8nz6rsYIiIiUo24+i5AHXoTeCvifSDidT9giXMuA8DMpgGHhfZ5AHgS+GsdlVOkWt9nZDP86wXkFgf5LiObUVN/5vPD+pHs91e/893zoTBK7UeCDy7p5QWM/FBoyQ96QePCXtAmqWaFyyqCN1fBUe2ga5Oaf6hqFOTk89iR/8eq75dyyoPnc+SfTojZsUVERCS2dqgmw8wOM7O3zWx9qPnQcjN7xcyG1VYBY8U5l+2cyzKzNLywcWvE6nRge8T7LKCpmY0DNjnnPqu7kkpjsr0owNTNmSzJLltRlv7eN9jbMyv8pL/3TUnAyAp4QSEv6EqCRo1qNKIFjPDyyIARFg4aNanRyCrymlmtyoEXl8HKnOr3qYFwwFg7bwWuOMj7N7/IpEc/iMmxwwoLoKAgpods9KZOncqpp55Ku3bt6Ny5M927d2fs2LFMnz69vosmIiK1rMY1GWb2LyATuA1Y6JwLhpbvA5xtZmc55y6vnWLGhpl1Bt4BHnfOvRKxKhNIi3ifBmwDrgacmR0N7A+8YGYnOufW102JZU/nnGPepnnkFOUwsN1AEvwJJevuXrSGu35eQ6LPR6FzHNAshfeH9KVlYnxJgCgvKxAsEzDCIoPGCxuHkxTYUmFfi0uj3emZVRe4fMAoWR6EZ5ZA0EGRq7g+wQdX9/ECRlYROLztXlwG5/SoUY3G9elnUpBVsVViQmoS7ft1Zu28FQTyiwAoyi3k/ZtfBKi2RmPy5xAte/n9MHyU9zovF76d6b0+aAgkp1RbXKnGVVddRXp6OnfccQf9+/fH5/Oeac2bN4+XXnqJl19+mccff7yeSykiIrXFnItywxBtQ7O2zrkNO7u+vplZW2AycKVz7sty6+KBhcDBQDYwEzjRObc2YpvJwGXOuUVVnWfQoEFuzpw5sS287PZyi3JJiS97Z7poyyJOePN41uesx29eM6Znx4zn1L6n8s7aLZzz7RJyIjptx5txaKs0vjp8b+ztmTtVjngzbi56lEuLX4u6vv1YB7f9VPkB/AbFUb4T/AYtE2BjFY/6m8Z7AaN8Poq3GgWNK+3kStfFJcWXBIwyh05J4MR7zqkyaHz5SeXnPOrY0oBRVBg6ZoKCRixs2LCBtm3b7vT6nWCxPNieRtceEaknlX73VttcysxGmtnTQPvQ+0uibbc7B4yQm4HmwP+Z2eTQz1lmdolzrgi4DvgML2CMjwwY0rCtyVzDLVNu4eS3TuLemfeyJa9iLUBVPlzyIc0ebsrj3/2nZFkgGGDEK0eybNsycopyyCzMJLMwk/M+PJfFWxbz4K/rygQMgCLnmLkli3V5hVWeL9Uf/c82yWcc2LwJ5xS/s0PlL6NlghcoIoUDxoW9qt43WsCA0hqNDflsWbGBj+98nTf/9DQLP/uBYLBmI2NFCxjg1Wh8dNurBHey43v5gAHe629neutkx02cOJGLL76YdevWAfDUU09F3S7GAUNERHYzNWkudTlwPnCrmbXAaza0x3HO/Qn4UxXrPwAqbeTtnBteC8WSevb9+u8Z/vIRFAWLKCguYOKKiTw0+0G+HTeHrk27Vrv/h0s+5A/vnk4gGODGSTcCcPnAK/hyxZfkFOXgKFsrUBgs5Om5T7MpcFbU48X5jK1Fgajrwr48vD9Hfb2Q7IiQkuQz9mvWhK8O68/2N3ahY8GFvbz+F1sKvRqNyICRVE2ncr95zanK8wGJfuZN+oHxFzxMsDhIcWGAmeO/pPfhe3Pp+zfjq6bDekJKIoW5FT9XUloyf5p0Fz6/n6xM2LIJfD5o0x6SatBPvXzACAsHDdVo7LjHH3+c5557jrvuuoutW7cyd+7c+i6SiIjUg5p0/N7knNvmnLsBb4jXg2q5THscM7vEzOaY2ZxNmzbVd3FkB1z8yUVkF2VTUOzdwOYF8tiav5U/f3UDAE0fSsd3r1X4afpQeknAyAt4/QhyA7ncOOlGHv/uP2zO2xz1fIFggHU56zi2XTPirWINY5wZe6VWfXc8uEUaXx7ev6RGIzJgpMTVYHSphEr+7BN8XpC4sFdpjUZNAwZA5yZe06hIPiAljsJzuvDcRY9SlFdIcaEXogqz8/l1ygK+f6P6TsBH3XASCSmJZZYlpSXzpyl302n/HixeCHNmwtJfYMkvMHMKbFhXfZED0StIStYtWVz9MaSs1q1b06xZMx544AE+//xzvv322/oukoiI1IOa1GR8FH7hnLvJzK6qxfLskZxzTwFPgdcutp6LIzWUV5THTxsr9k8IuiCfLfcGFMsqzIq6b1ZhVpmAERYOGjcN+SuFxRUfkTeJb8KxPY7l6F4deW31FrYVBSgIOgxI9vv41/7diff5SIvzRe38nRbnBYRw0Bj+9UL2bZpS84ABcMuAqteHg8a0jXBom5oFDICzusHLK2B1jtdEKhQwuKQXS2cvxBelmVdhTj7fvjyFQWceXuWhj7tjLABfPvAehbkFJQGj8wE9yNgCv62BcMsrF/T6nS/8CVq2quajJkN+HpTvmmYGiUnQp3+NPrlEOO6440pe33vvvfzrX/+qx9KIiEh9qTZkOOfeK/deVwxpEOJ8cfjNT7Gr2J6/fCfuaMoHjLDcQC63Tf0bZ+19Fu/88g45Rd4wrilxKfRp0YfT+51Ogj+BeSP349Ff1/H5xm10TUnk+t4dOKSlN8hZ5kkHV3v+wS3S+GXU/rRKjCcp4gbe4tJwgYrhyOLSKiyrVJIfjm5fcXmCr/I5NuJ8pUFjZQ4k+715N5om4I/3V7yTD4lLjAcgMS056uhSiWnJQChomPHVQ++VBAyA9b9BMEqXDDPYstkbRaqy0aUGDfFqQCKDRjhgHDQEEhIr7idVO+mkk8q8v+oqPZcSEWmMajy6VMkOZicAFwAll1/n3Jga7jsOuBN4BG+uivF4QceAS5xzi8ttfxtwHN7Eedc452ZHrLsGaOecu6mK8zUFXsKbByMBuM45N7PcNhcDl4bOcZdz7kMzuwkYHdqkGdAO+D3eLOGLnHNnVHZOjfCxZxn73pn875f/lal1SI5L5oaD/8wdh92B797KB6xJiUshN1Cxd3BKfAon9DqBF49/iQ+XfsgT3z/u1Xz0O4NL9r+E5PjkWvksu41AEKZshEEtoKk3ZG9xoJib240jZ0vZ8JPQJJEL3/gze48ZVPPDFxYRlxBf8v7n+fDb6orb+f3Qbx9oGyUrRSosLA0aoIARax988AHjx4+nIGISko8//rg2TqXRpXTtEZG6V+l3786EjMV4N+UZ4WXOuR9ruO84oG+o2dXzwDvOuXfN7BjgUufcKRHbHog32/ZRQGfgbefcQWaWDDyNN9zs29WEjDuADOfcI2bWB3jVOXdgxPp2wERgEJAETAMGOecKIrb5EPiXc+4zMxuON4ytQkYDsT1/O6PfGM38TfPwm5+iYBEju43kjd+9SYI/ocqQ8ddD/sqjcx4tEzTCAeOlE17G76thM6NGYtmMRTx+7B04B8FAMTgYdukoTn34QixK/5SaytgKc+dUrM3w+eCwERAXH32/SOGg4ZwCRqz16dOH//73vzRv3rxk2X777Vcbp1LI0LVHROpepd+9NZ6ML8IC59zknS9LiespnWU7Dig/nfChwOfOS0GrzCzOzFrj1Ti8AHwB9K3mHA8D4cAQ7RyDgemhUFFgZkuAfYFvAczsFLyQohm/G6imSU2Zee5Mflj/A0u2LWGf1vvQt2V1/6w8dw+/B6AkaChgVK3H0L7c/dtzzPvgW/K25dDnqH1p07vDLh+3WXPo2AnWrvZCQjiv9N+3ZgEDICEBDh7m9eWI25lvRanU3nvvzfDhw+u7GCIiUsd25nL6npnNBH4OL3DOXbCjB3HObQYI1TA8AJxcbpN0IHLCgiygqXNuCfB5qFakunNsC52jHV6zqWuinGN7xPssoGnE+78CZ1Z3HtnzHdDuAA5od0CF5WkJaVE7f6cleP0bwkHjvm/uU8CogcQmSQw647CYHtMM9uoPHTrD5o1eM6k27SFxB2sj/AoXteKkk05iyJAh9OvXr2TZ+PHj67FEIiJSF3bmsno1cD+wbVdPbmZHAo8D55TvjwFkApE9VdN25pxmtg/wGnCDc25KTc9hZv2BbaFQI43U9usyq93m7uH3MLrnsQztOFQBox6lpnk/snt57LHHuPHGG2nWrFl9F0VEROrQzoSM9c6513f1xKGA8Sgw2jm3Msom04H7zewBoBPgC9d+7MA5+gNvAn+opN/IbOBuM0vC68jeD5gfWnc08MmOnE8ar8M6x/bpvEhD0a5dO/7whz/UdzFERKSO7UzIyDOzT4Ef8Jow45y7eSeO8wjeiE/Phzp9LnbOXWpm9wNvOedmm9lUYCbeiPtXVHUwM3sEmOCcmxux+B94HbofDZ1ju3PuJDO7DljinHvfzB4DpobOcYtzLtxvow9ep3AREdlJycnJjB49mgMOOKCkg/8999xTz6USEZHatjMh44NYnNg5F3V4EefcjRGvbwdur2S7CeUWLQWyy21zElE45x6KeP003mhV5bepMtSIiEj1TjjhhPougoiI1IMahwwzexb4GG/Y2Ozqtq/CWDPbGHmjHyPvOedWxfiYJczsULzal0W1dQ4RkYbiwgsvZMyYMZx66qmkpqbWd3FERKSO1XieDDNriTcx3ii8JkhTgQ+dc0trr3h7Ho1VLiICW7Zs4aOPPuLzzz8nPz+fww47jOOPP56ePXvW1ik1T4auPSJS92IyT0YH59wLwAtmFgccDlxhZr2ccyfuaglFRKTh+O233zj33HM599xzCQQCfP311/znP/9hyZIlvP/++/VdvJgzMx/eaIn74c3PdFHk6IRmdgLwN7y5nsaHmupiZj9QOpT6cufc+aHJaD8Afg0tfyIWA66IiNSlHQkZ/zKzTsAU4FNgonPuq9oploiI7Mmuuuoq1qxZwxFHHMHo0aMZOXIkI0aMqO9i1aaTgSTn3BAzOwR4EDgJwMzi8SaHPQjIAaab2QeEhkx3zg0vd6wDgYeccw/WSclFRGqBr6Ybhr4E9wZeDP33TTObaGa31lLZRERkDzV58mQWLFjAOeecw4IFCzjttNMYOXIkd911V30XrbYcivcADufcLGBQxLp+eCMaZjjnCoFpwGF4tR4pZva5mX0VCicAA4HjzOxrM3vWzHa7GWCCzvG/tVsIBGvW5LrerMuDQLC+SyHSKNU4ZAA45wqA74CfQj9xeE9cREREykhMTGTgwIHsu+++7LvvvgQCAb7//vv6LlZtSae02RNAcahpcbR1WUBTIBd4ADgGuAx4ObTPbODPzrnDgWXAbbEu7PKcfH7JyqOm/TIjBZ3jnG+X8IdvfuHUWYt336DxYwb891d4aXmFoOGc4+fPf+Dli/7NG1c+xcpvf63kICKys3ZkdKnr8Dp+NwO+AD4EbnLOFdVO0UREZE/10EMP8dFHH7Ft2zaOPvpojj/+eO69917i4+Pru2i1JROIrHHwOecClaxLw2sq9QteDYcDfjGzLUB74B3n3LbQtu8A/4pVIRdl5nHKrMWsyC3AgFYJcbx28F4MaekVL/29b8iK8uQ/Lc5H5kkHlwSMd3/bSsDBFxu3c+qsxbx9SB/ifDHue7+tEH7Lg/5Nyy6/ez4URqmdSPDBLQO81z9mwPtrvNm8Vud6QePs7hDnwznHC+c+wo/vfENhTj7mM2Y+9yXH3PJ7Rt98Wmw/g0gjtiM1GX8DNgF/AW51zk1SwBARkWjuvPNOWrduzX333cddd93FkUce2ZADBsB0YAxAqNnTvIh1PwO9zayFmSXgDZwyE7gAr+8GZtYBr8ZjHfCZmQ0O7XsUXguCXVZQHOTwKfNZlJVHXnGQ3OIgq/IKGTVtIZsKvMt5tIARXh4ZMHKLve1yi4MlQSNco7H+jXTWvWIVfta/kV7zwm4rhKeWwFurYNrGsuuiBYzI5eGAEQjVsARcadAIBPl1ynx+fGcWhTne3Lsu6CjKLeDTv79BxupNNS+jiFRpRzp+t8ZrQzoGuMfM1gGfAB/X5vwUIiKy59m0aRNTp07l448/5uabb6Z9+/Yce+yxjBkzhi5dutR38WrDO8BIM5uBN6Tj+WY2Fkh1zj0Vag3wGd7DvfHOubWh+acmmNk0vGfuFzjnAmb2R+DfZlYIrAcuiUUBP1iXQX4wSPnGTYGg48WVm7hurw5V7l8+YIRFBo23D+mDC2RF3b9keXU1EeGAkRvwfiuTN3jrD21T/YcsHzBKPmRp0Jj/w2wKcwsr7Orz+Vj46Q8Mu3hU9ecRkWrVOGSEai2+Cv1gZqOBm4H/AP5aKZ2IiOyR4uPjGTFiRMmIUp9++in33HMPV1xxBcXFxfVcuthzzgXx+lVEWhSx/gO8YWkj9ykExkY51vfA0FiXcV1+IUVR+k/kBx1r8gqq3f/NNVsoqqQPR25xkI/WZ7AiN58m1R2oqpqI8gEDoMjVPGh8tLZiwAgLOFiZQ6e0Vvj8PoKBsv8OzWfEJydUV3oRqaEaN5cys0Fmdq2Z/c/MFgLnAc8D3WutdCIiskeaM2cODz/8MKeccgr9+/fn+eef57zzzmP58uX1XbQGw8wuMbM5ZjZn06bqm/kMaZmGzyr2m0iN83F4q6ZR9ijro2F9SfFHv21I9vt4+5A+9EpNrr7gVSkfMMLCQWP6xqi7lTi7O8RXcmsTb3BgC7qMPQB/fMVno8459jlxcJQdRWRn7EifjPuAeOAuYG/n3JnOuWfVVEpERMr7y1/+QlFREbfeeisLFizg1Vdf5cILL2yoTaXqhXPuKefcIOfcoNatW1e7/aDmqRzdpmmZoJDsM/qkJnN8++bV7j+ybTPeHdKnQtBI9vt4dXBvTurQYsc/RHn5xRUDRlixgx8yqt6/SxM4N0rQiDfYrzkc35F2/btw6sMXEp8UT2JaEolpySQ0SeLi/91EcnrKrn8GEQF2LGT8itf/4ntXbsw7M9vfzP4b26KJiMieqnfv3owZM4YDDzwQK/f0fO7cuVx66aX1VLLG7e1D+nD/gC7s2zSFfmnJ3NqvE18fsXfJyFBpcdFvC8LLyweNmAYMgCPaeIGgPB+QHg/jenp9N6IJLy8fNCICBqF/i4deegx/X/0sZzz5R84efxX/2DCBfqMOiM1nEBEArKZjZJtZC7xajEHAYmAD3nC2++ON6X2bc67RD8swaNAgN2fOnPouhohIvdq6dSu33norc+bMoU+fPrRt25Zt27Yxd+5cBg8ezB133EFNnr5XZsKECfztb3/jmmuu4dxzz6V169YTgWTgN+B851xu5PZmdhveMOwB4Brn3OyIdYcDLzvnOofenwP8GW9uiwnOuWcrK0doJKlHQ8f93Dl3R7n1ycBLQBu8+THOc85tMrNTgH8Cq0Ob3uacm2JmD+FN7BcErgd+xBsyvq9zrl1l5ajra8/EDds4ZdZiXjqoYsBY/0Z61M7fFpdGu9Mz4bafKj/wHfvClA0wdaPXRApKA8bFvSF1B8arWZUDzy+D/csGDBGJqUr/sHak4/dW4PLQzKOHAK2AjcCfnHM5u1xEERFpMFq0aMHjjz9OVlYWs2bNYvPmzbRp04ZHH32UJk2q7RpcI2PHjuW6667j6quvBnjFOTfBzG4CLgUeDm9nZgcCRwAHA52Bt4GDQus6493Mx4fet8J7oHYA3lwWX5jZl865FZUU40ngVLxJ8z4yswNDHbfD/gjMc87dbmZnALcCf8KbyPZG59zbEeXcD6/D98FAL+A159xAYLiZrd+531LtGNm2GdtPHBy1j0e70zOr3jnBV/noUgBHtPX+O3Wj10RqZwIGeDUaN/SHJJ8Chkg92MG/WHDOZQETa6EsIiLSwKSlpTFy5MhaPce0adMAPg29/QS4h4iQgVcz8Hmoqe8qM4szs9Z4NQtP4g0RG56LogcwN/RgDTP7Fu/B2ory5zWzdCDRObc09P4zvHktIkPGocD9EWX7v9DrgcABZnYNXmuAvwBr8WYBT8SbM2O3nosqWsCokfCEeVU5oq33fPSnbV4TqR0NGGHJGvxSpL7sSJ8MERGR3U5mZiZ4TZvACw7lh0pKj1gfuc2/gQecc2sj1v0K7G1mbc0sBS80VFb1ko43m3f541Z27sj1E4Gr8CbmS8Ub/jaA10xqEfAF8EAl520cDm8LV/bZ+YAhIvVKf7kiIrJHS09PB0gD8kL/3VZuk8zQ8rA0oBBvgtleof4aLczsNefcGWZ2LV6TqjV4tRKbKzl1tONWde7I9eOdc9sAzOw9vCZXPrzJ944JbTvNzGaWC0EiInsE1WSIiMgebdiwYQBjQm+PBaaW22Q6cIyZ+cysC+Bzzq1yzvVxzg13zg0HtoYCRhxe86jDgXOBvqH9K3DOZQKFZtbTvCG0jqnk3GXKFtr2JzPrFFp+FF5zrQwg2zlXjFfrUYBXyyEissfZ4ZBhZmvNrMjMfjOzQjPLNLNfzax2G92KiMgep2PHjsTHx9OhQwcSEhJIT0+nd+/eTJwYu659t956K8AZZjYdGILXDAozu9/MBjvnvsO7+Z+JV0NxRWXHcs4F8Go5vgOmAI855zabWTszey3KLpcBL+P1q/jBOfdN6Nyfm1kC8ARe86tpeH0/7gj1DbkI+J+ZTQFSgKeBV0L7zgBm4I14tXgXfjUiIvVmZ5pLfQ3c7pxbbGY9gduAO/GG6FOHcBERKXH44Ydz++2306dPH5YuXcodd9zB3/72N84+++yYdQhv27YtzrnR5Zc7526MeH07cHtlx4gcHjY0DO0d5TbZjNcxu/x+s/BqPsovHxV6WQicFmX958DnUYpyWWVlFBHZk+xMc6lO4ScroRE1ujjnluB1WBMRESmxZs0a+vTpA0DPnj1ZtWoVvXr1Ii5u17sEvvLKKzz00EO7fJwaMrx5LeqUmaWa2eS6Pq+IyK7amW/5dWZ2L15V7lBgfaipVGFMSyYiInu89u3bc9NNNzF06FBmzJhBu3btmDhxIgkJCbt03HHjxjFu3LjYFLIGnHNFeJ2y65RzLhsYXtfnFRHZVTtTk3Eu3oyqo4FVwHlANnBmDMslIiINwAsvvECHDh349NNP6dKlC88//zypqam8+uqr9V00ERGpReb1P9uBHczOBf4KJIUWOedcj1gXbE81aNAgN2fOnPouhojIbuGFF17gH//4B/n5+QCYGcuWLauNUzXqKZ117RGRelLpd+/ONJf6C3ACsHqniyMiIo3CfffdxwcffEDnzp3ruygiIlKHdiZkLAt19BYREalSjx496NWrV30XQ0RE6tjOhIxcM/sEmAs4AOfczbEslIiINAwpKSkce+yx7L///nhz0ME999xTz6USEZHatjMh4+Ny73esU4eIiDQaY8aMKfM+HDRERKRhq3HIMLOLnHPPAH0pDRaGQoaIiJTzzDPPcNFFF7Fo0aKSYOGcU8gQEWkkdqQmI9zRe1HEMkcjH9FDREQqCnf07tu3b8kyM2NHRzQUEZE9U43nyXDOfRZ6uRho6px7HjgGmFcbBRMRkT3XMcccA0CfPn3Yvn075513Hp999hn77LNPPZdMRETqws5MxvcY8EXo9f8Bj8SsNCIi0qBcffXVHH300QD8/e9/55prrqnfAomISJ3YmZARcM4tBHDOLQOCsS2SiIg0FHFxcfTv3x/whrP1+XbmsiMiInuanRldaqWZ3QPMBAYDa2NbJBERaSi6du3KzTffzJAhQ5g9ezYdO3as7yKJiEgd2JlHSucDG4ExwCbggpiWSEREGoznnnuONm3a8PHHH9O6dWvGjx9f30USEZE6sDM1GUOcc4/EuiAiItLwzJw5U/0wREQaoZ2pybgj5qUQEZEG6bbbbqvvIoiISD3YmZoMZ2bv4A1lGwRwzt0c01KJiEiDYGb87ne/o0+fPiWdvu+55556LpWIiNS2nQkZ44GWgB9vMr6NMS2RiIg0GBdccAFbtmyhuLgYM6NNmzb1XSQREakDO9NcKg+4DBgG/BEvaIiIiFSQnJzMk08+yfTp03niiScws/oukoiI1IGdqcm4FjjQOZdtZmnAV8CLsS2WiIg0BA8//DDff/89qampZGVlMWLECM4555z6LpaIiNSynanJCDrnsgGcc1lAfmyLJCIiDYXP5yM1NRWAtLQ0kpKS6rlEIiJSF3amJmOpmT0IfA0cDiyNbZFERKSh6NmzJ9dffz2HH344X3/9NT179qzvIomISB3YmZqMC4BlwMjQfy+OaYlERKTBGD9+PD169GDixIn06NGDp59+ur6LJCIidaDGNRlmdqtz7i7nXMDM/uecW1ebBRMRkT3XXXfdxa233kpcXBynnHIK7du3r+8iiYhIHdqRmowREa9fjnVBRESk4fjqq69KXp911ln1WBIREakPOxIyrJLXIiIiZTjnor4WEZHGYUdChqvktYiISBmR82FobgwRkcZnR0aXGmhmM/BqMfpHvHbOuaG1UjoREdkjfffddwwdOhTnHAsXLix5bWbMmDGjvosnIiK1bEdCxr61VgoREWlQfvrpp/ougoiI1KMahwzn3MraLIiIiDQcXbt2re8iiIhIPdqZeTJEREREREQqpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiIiIxpZAhIiIiItF9vxU25td3KWQPFFffBRCRxiunMIcX5r/Alyu+oFvT7lx24GX0at4rZsdftX0Va7LWsHervWma1DRmxxURaRQmrYdpm8BvcGFPaJtc3yWSPYhChojUi4z8DAY9N4gNuevJLcol3hfPkz88wTunvsvI7iNrfJyfNv5E1/SuZUJEVkEWp797GlNWTyHRn0hBcQE3DL6BOw67EzOrjY8jIrJnuXs+FAYrLk/wwS0DvIAxfRMEnPfz7FIFDdkhai4lIvXivpn38lv2WnKLcgEoChaRG8hl3IfnEXRRLnxRfLL0Ew6aMIihLw4hIz+jZPn5H53P5FWTyQ/ks71gO/mBfB7+9mFeXvByrXwWEZE9TrSAEV4eDhhFrnR5QdALGhvy6qZ8ssdrNCHDzHxm9qSZzTSzyWbWK2Jdu9Cy8M82M7vMzBLN7BUzm2Vmn5tZ7/r8DCINyf9++R8FxQUVlmcWZrIkY0m1+3+y9BN+/87vKQoWsXTbUg59cRgZ+Rlsz9/OR0s/rHDsnKIcHvjmnzErv4hIg1U+YISFg4b6aEgNNKbmUicDSc65IWZ2CPAgcBKAc249MBzAzIYAdwNPA38Esp1zh5hZH+DfwDF1X3SRhic1Pi3q8kAwQGp8Kk0fSierMKvC+rSENF476XV+/87vyQt4tSCFxYUlQeO1k17HZ/6ox96ct7nkdW6gmGlbsog349BWacT7duyZy5aCIgqCjg7JCTu0354sNyOblOap9V0MEaltVVUmFzsvZLRJqrPiyJ6p0dRkAIcCnwI452YBg8pvYF5j7X8Bf3TOFQP9gU9C+ywG+tVZaUUauCsGXkFKfEqZZX7zc0DbA+iQ1iFqwADIKswqEzDCwkHjjPf+QGp8kwr7+c3PUd2OAuDNNZtp8+EcTpv1CyfPXEz7j75jxpbo54tmXV4h+3/5EwMmzmVxVuNoOvDd69P4S6tz+PLBd2N+7N9Ww5yZUFQU80PXGTM7wcy+DdWWX1zFdoeb2eqI99eZ2YKImvQ+oeU/RCx7ri4+g0iJ1oleZ+/y4g2OagcDmtV5kWTP05hCRjqwPeJ9sZmVr8k5AVgQChQAc4HjzXMI0NGskkekIrJDzt/3fMb2P4skfxJpCWmkxqfSo1kP3jj5zWr3zQ9Ev7EvLC5kacZSTtzrJFLiUjC8i2S8L570xHTuOPROlufkc96cpeQUB8kMFJMZKGZLYYBjp/1MTqC42nOvyytk8KR5rM8vZFtRMUMmzdsjgkZhXgGzX5zER7e/yo/vzKK4Bp817LvXp/HS+Y/hgo4P//ZqhaCRuR0WL4AFP8HmjeCitLKozNrVsHihd4w9NWiYWTzwMDAKOAK4xMzaRdmuM3A9EB+x+EDgXOfc8NDPYjNLAohYdn7tfwqRCBf0rBg04g1GtIOhreuvXLJHaUzNpTKByPYZPudcoNw2ZwOPRrwfj1d7MQmYDnwXquEQkV3kMx9PHfsUNw+5mdnrZtMhtQPDOg2r0ehPzZKasS1/G46yd7MpcSmM7jGaJ495kov3u5h/fvNPlm1byhGdj+D6g2+gY1pHbl+4mkCw4l2ww/Hhugwu/n4pWYGKbQXS4nwsHnVAScAIhA4RDhozj9yHPmm756grm5dv4MEhN1KYU0BBdj6JqUk069SS62fcV23zp3DAKMorBKAot4AP//YqAEddfzKrlsPSXyAY+pVtWg/NW8K+B4IZTP4ciqN8a/r90Lsf/LKwdN+8XC9oDBoC8fEV99mN9QOWOOcyAMxsGnAYUJKYQ8HhSeAS4LuIfQcCfw2Fko+cc/8A9gNSzOxzvOv0zaEaeJHYSfBVPrpUot8LGuOXwqYC75H0zgaM9Xkwb5u3f7TaEWmwGlPImI5XU/FGqFZiXpRtBgIzIt4fBExzzl1rZoOAnrVfTJHGpVuzbnRr1m2H9plxzkyGvjikTNAIB4zXT34Dv8/P4A6DefN3FWtFthYGKIryqL3YeYEhWsAAyAoEKwQMAMfuHzReOv8xsjdl4UJ38wXZ+WxetoH3b36JM564jOvTz6QgSm2MPzEen89KAkZYOGgUByB+/5NLQgJ4gSJjC2zZBK3aRA8Y4e0iAwZ4NSB7aNAoX1OeBZSfmOXfwAPOubXlgvRrwH/wHoS9Y2bHAyuBB4BngN7AJ2bWJ8qDMZGdd8uAqteHg8bLK6BfOgzZyYAxfqk3BO6WAjitq4JGI9KYmku9A+Sb2Qy8au1rzWysmV0CYGatgSznytx9/Ar80cxmAn8HrqvrQotIRX1a9mHGOTNpltQMwyoEjKqMadeMVH/Fr76gcxzdpuoJ+zbkF5UJGGEOyAwUc92PK3bgU9SSpVnexTykMK+AZdMXlQSMsOLCAN+/MQ0gasAAKC4oqhAwwopyC3j/pglsX76i4n7FsGF99UUNRslz4aDx/TfV71/fzOwuM5sMvI8XNMLSgG0R23XAq9m4LbR9CzN7LdQP8BHn3GbnXCHwEXAA8AvwkvP8AmwB2tfBRxIpKxw0ogWMu+fDbT9V/Ll7vrc+HDAKgt5TnCVZ8OZK77U0Co2mJsM5FwQuK7d4UcT6TcD+5fbZDBxd3bFDQeUSgC5duuxqUUUEbxSpykaXgtKgMfTFIRzZ5cgaBQyAUW2bcXjrdKZsyiSn2LvLbeL3cXH3NvRMrXq0lF6pSSzNyaewXHMrv0HbxHieGtiD9W+k4wIVy21xabQ7PbPa8u2SnzLgvTUQ54OLekLrXR/9JSElkcLcikMNJ6QksvdJh9K0e9eoYSFuF3qvmc9rcrW7c87dCiV9MhaaWQsgGzgcryYivN1vQJ/wezNb75w7w8yaAvPNrB+QA4zAa6Z7AbAPcHkooKQD6+rmU8meJL84SE6gmJaJZav96uR7qKp5NiIDRlhRRNBQjUaj0GhCRm1yzj0FPAUwaNAgRXSRGNh+XfUXwj4t+7Dy8lWkxKfgs5pVzPrMeH9oX95Ys4VXVm0iye/jwm5tOKZts2r3nTF8AEMnzy8TNMIBY/aIfeiYnMi6KBd2IOoFP6qCYnhrFQxsAX2rrlkpIxwwAg4CxfDMUrioJwmtk+h1eH+WTFlAsLj0gh+XGMegsYdXe9ijb/wdX9z/TpmgkZCSyMAzDuUP/72SaZOswnCXPh+071R9kePiIVCuo7fPDx07Q+++1e+/u3DOFZnZdcBneC0ExoeaRbUAnnHOnVLJftvN7Ga8fn8FwJfOuY/NLAGYEOrb4YAL1FRKyssqKuaIr+ezKreQWUcOoFdqaVPNyr5vavw9tKvKB4ywcNB4axX8oWvdlEXqjUKGiOzRUhN2fN4Gvxlndm7FmZ1b7dB+zRLiygSNYufKBIxdVlAMz4UmulqWDad1qVnQiAwYYfmlQePs567mwSF/IT8rj8LcAhKSE2jZvS0n3HVWtYcec9sZACVBIxwwxj5zJWbGfgPhx+8g3AffOei5F6TXoNgHDYFvZ5YGjciAUYP+/7sV59wHwAfllm0FKgQM51y7iNcvAi+WW18IjK3unKpF33N9sWEb1/y4gp+z8miZGMeNe3Xg+t4dSga+SH/vm0oHoMg86eCSgLEwM4+ioOOQSfMrBI16VVWTKAdsL/S+LPa0P3TZIXUaMsxsHHAn8AjwAvAKkAz8BpzvnMuN2HY0cFP4Ld48FwOADOBlIAGv+nhc5H7lzpcMvAS0weuId16oWVTkNjcAZ+I9i7vHOfdOqJ3sGrw+GQAz8drK/htY5Jw7Y6d/CSKyW0uL81V6cYeyQWN7USD2AWNTARQDOHhzVWnQuHt+9OYJfrwLdbTOIqGg0eKinty+7L/Me+8bNi1dT6f9utHvmAPw+WvWpikcND6+47UyAQOgeQs4bARs3ez1xWjREhIifh1+f+WjS6U0KQ0aweI9N2DUF9Wi75lmbMnipJmLyQ3VLG4qCHDbwjVkFhVz595eWKxqAIrIgFEQqlHNKAzsXkHjzG7w2oqKs4bHmTeJ37ieJX/oRQVFZK3PIK1tM+KTGs/kpo2BuR0Z0HxXT+aFjL7OuZvM7DHge+fcBDO7CShwzj1cyX5/Bpo75242s0dC+71gZrcD26vY7zog3Tl3u5mdAQxxzv0pYn0z4CegF9AEmOuc62pmvYCHnXMnlDvecOCyqkLGoEGD3Jw5c2ry6xCRPVhecTFFQUd6fNlnNeteqfwOuf3yedGDQrxBq0QvYJQPC3HmBY1XV1ZeGL9V/uTQH5o8a1jlI8NUNrpUYloyD2a+WvJ+5ZwldBnYs0bDDO+I3BwvpHTssksBo1FHE1179hxHf72ALzdVbA7axO9j8wkHkeT3YW/PrHT/A5qllAkYYT6geUIcs44cQJP3U6LvDLQfG6P7vtt+qnzdHft6zaIig0Y4YJzfExJ8OOf45M7X+eKf73i1GwYjrjuJ4+44M+bfMVKrKv2fVZ+jS5XMwI03q3bUDtZm1gk4B7gjtOha4CUz8wGdgQ27cI4cvKECm4R+wlf/gXgT700ys4/DM7CKiIQl+/0VAka1KusoWeSiBwzwlr25qurjtkyofHbe/ZrD0KqbhT2Y+Sr/du9W+IkMGABdB/WqlYt/ShPo1LViwJgwYQJdunThoYceqrDPHXfcweDBgxk6dCizZ8+u9NhmdouZvRZ6PTpiFu0pZlZsZv3MrImZvWBmU83sGzMbXNOym9nFZjbHzGaFhp+tbLuxoZEKw++vDZ3rGzO7LbSsiZm9FyrHp2bW2swONbO54c8ge76FVUzeuS4/+mhukRZECRjg3cBkFAYYN2fprhSv5hIquYUML++VBmd0876H/JQJGACTH/vQa4aZU0BhbgGFOQV89eB7fPnAu3VReqkD9dknI3Jc8Whjioddh1erUADgnHOhmbp/BJLwml/tyjlWAwvx/gT+EVq2DviHc+5NMzsUr8nVQTX5UCLSuFlcWqWjulS9YxXr/JU0hwq7sBc8uwS2FJbWaMQb7NMcTuy4R7c/Gjt2LNddV3b08O+//54pU6bwzTffsHr1ak499VS+/fbbCvua2bHAsXjNX3HOfUrowVOohny6c+7nUK34fOfcuWa2L95keJUnl9LjtwOuBgbhXY+mmdnE8PUqYrv9gQsJ/V82sx7AWcDBeM9wp5rZO3ijS33nnLszVPN/q3PuT2Z2DRVHR5Q9VP+0ZNblV5za3gHta9BcqFtKIityCiiM0hIlLc7PEwd0xzbu5PfQjqhung0oDRrfboFTu5QJJp//4+0KI9cV5hYw8f53OPrPv4tdOaXe1GfICM/AnUe5McXDQrUVxwO3RC53zhUB/c3saLy+HUdUcw4qOcexeGOPdw+9/8zMpgNzgEDoXNPMrKOp7k5EaqDK4SGral7Qtyks2l6xDXOizxun/olfo+8HkOQvGzR8NIiAUZlp06YxatQozIwuXboQCAQws9aRfe5CzV4vBW4HLorcP6KGPPzw6BjgdTP7DO+6cUUNizIYL6gUAAVmtgTYFyhJPGbWErgXuAZ4OrR4NTDaOVcc2iYeyHfOPWJm4Y4yXai6pl72UHf078yMaT+TFzHaW4rfx3W925MUZQ6f8mYeOYAhk+ZXCBpN4/xMHb43+zRtArU9XPaO6JXm/ZSTvTl6GXO2ZOKcU5OpBqA+m0tNB8aEXh8LTI2yzQC8jtYldYtm9riZHRl6m0WFwRN36BwZeCGnwDmXjxdCmgG34V0QMLP9gFXlJukTEYmtUzp7QSM+4sIaDhjtatCRMxw0Wid6TaQaaMAAyMzMpGnT0orptLQ0iKipNrNUvFm0LyX0wKicMjXkQCu8fn/H4I0Q9UCUfaKpcqbvUGB4Fq+Zb8ljZedckXNus3keAH4ITbqHc67YzL4CrgI+rmE5ZA8yrFU67w/pw95pyfiA1glx3NG/E3f271yyTXigifLS4ny0SIhn5pED6NYkkYTQ33iZgLGHaBfxeSO17dtJAaOBqM+ajLuA583sYmAzoeH6zOx+4C3n3Gy8yYuWldvvMeBJM/sbXsC4PLTfI8AE59zciG2fCJ1jGlAyJGCoQ/gS59z7odqQWWYWBKYBE/GeQr1kZsfhXaDGxfaji4iU4zMvaPwP+Hm710QqMmAk+KL36YhsF53khz/uVSfFrWvHH3882dnZ7LPPPvTu3ZusrNKmIKHX2yI2HwW0A17He3DUwcxucs7dW0kN+Ra8WbvBCxk3UQkzewZvsJBNeEPPRj6iLV9jPhDojXctSsKrgX/EOXeNmSXhTbyXReg6FuacG2FmffFGNexZWVlkz3V022bMH7V/peszTzq4yv3DQWPIpPmsyy/c4wIGwO8fvoAnT7ybotzSfijxKQmc+vCF9VgqiaV6CxnOuQ3A6CjLb4x4/SbwZrn1i4DhUQ65FG+m1chtc4HTopzjoYjXt+HVXETKAI6r7jOIiOyQ6oJCOGh8nQh908vWYNSk/XMD9uGHH5a8/u6777jxxhu54YYbWLNmDcFgEOfc5vB659z/8OJa5KiA94ZWV6ghx3vANAb4Dm+27gWVlcM5V9L0KtQn4+5QYEgE+gHzI7adDewd2rYb8FooYBjwHvCVc+6+iOP9FVgTmjsjh9BgxiLRtEiI59sR+5BZVEynlBgMo13H+hy1H1d+fgcf/e0V1i1YTbv+nTjujjPpddje9V00iZH6CBljzWxj5I1+jLznnKtmCJadF+oA/giwqLbOISINXE2Cgs9geNvaL8sebODAgRx22GEMGTKEYDDIf/7zHwDMbARwqHOuqgFBotWQ3wM8Exr9qQg4N3S8R6hYQ17CObc+NBz7VLzmx7c45/LNrD9wpXPu8mj7ASfj9SVMDHVOB/grXs3G82Z2Id5gJOdX8TlESI+P2/FR7nYjPYf14+ov/17fxZBaUqfzZDQGGqtcRCQ2JkyYwKJFi7j33nur37gW5skws6uAT5xzS2J97B0sx3A0R5OI7J52y3kyREREqvTKK69EnSejjry3GwSMcC26iMgeRTUZMaanSSIi9aJRD0eja4+I1BPVZIiIiIiISN1QyBARERERkZhSyBARERERkZhSyBARERERkZhSyBARERERkZjS6FIxZmafOucqzGQuNWNmlzjnnqrvcjRW+v3XH/3uZVfsqdce/bvfPej/Q/1riP8PFDJkt2Jmc5xzg+q7HI2Vfv/1R797aYz07373oP8P9a8h/j9QcykREREREYkphQwREREREYkphQzZ3TSo9oh7IP3+649+99IY6d/97kH/H+pfg/t/oD4ZIiIiIiISU6rJEBERERGRmFLIkDpjZg+a2WQzW2Rmq0Kv3zSz7mb2duj9dDN73MzSyu37vpl9UF9lb2jM7CYz+8LMPjezz8xsoJkNNrMFZvaP+i7f7s7M/mJm68wsKfR+spn1reVzdjGzE0KvHzGzLrV5PpFdoe/73ZO++2ND14CaiavvAkjj4Zy7HsDMxgF9nXM3mVkyMBu4yDn3TWj9ecCrwPGh952BVCDBzHo455bVR/kbCjPrD5wIDHPOOTPbH3geeBN40jn3r/os3x7iLOA14AxgQh2dcwTQF/jAOXdNHZ1TZKfo+373o+/+mNI1oAZUkyH17ThgSviCA+Ccex5oZWY9QosuBN4DXgAur/siNjgbgS7ABWbW0Tk3F7gEuAi4ysx+Z2bHm9m3oZ+nzEzfFSFmNhxYCjwJXBGx6k4z+8rMPjGz1qGfryKe2O4T2v/60O91ppndF1p2e+jJ4gwz6xda/76ZfW9md5uZH7gJGGtmJ4afmplZMzP70My+Du07InS8n8zsX2Y2JbRt07r8HYlUQt/39Uvf/TGga0DN6R+P1LceeH+s5S0HuoS+4MYCL+I9NfhD6GmY7CTn3GZCT7OAmWa2COiE9zTmIeAD4N/Acc65g4A1ofXiuQh4xjm3GCgws4NDy//nnBuB9/v7KzAY2A4cC1wNpIcuMqcDQ0M/vc3s+ND+PzvnhgJ5QDdgHHAQ3tOr/YB7gVecc+9HlOVWYKJz7nDgNODZ0N9MOvCqc+4IYG2oDCL1Td/39Ujf/TGja0ANKWRIfVuL98dUXm9gFXAMkAa8AryB9292bF0VriEys15ApnPuAudcF+Bs4AmgRWiTVkCGc24jgHPuTufcqvop7e7FzJoDY4A/mdmnQFPgytDqr0P/nQH0AT4BpuA9lb0TCOJVdc9yzhU5b2i/qcDeof0WR5zqR+fcVudcMfBN6HjR9Auf1zm3FsgEWofW/RD672ogaac+sEhs6fu+Hum7f9fpGrBjFDKkvr0HjDSzweEFZnYRsCnUFvcivPa7o51zo/GeAFwR/VBSQ/sCT1iowxrwC97TluLQ+41AMzNrAWBmj0X+/2nkzgaedc6NCv17PBgYhfelHv4dHQbMB4YD65xzo4C7gHuARcDBZhZnZgYcjvf7B+8CFNbPzFJCVeQHAwtD68t/Z/8cOh9m1hFoDmwJrdP45LK70fd9/dJ3/67TNWAHqOO31CvnXLZ5oyU8bGYt8f5N/gScaWZt8P64/hCx/XQzSzKzoc65GfVT6j2bc+5/ZtYP+MbMsvG+tP4M7B9aHzSzy4GPzKwY72nIt/VV3t3MRcA54TfOuVwzezu0/GQzuwbvSdJ5eL/X10PLioE7nXPzzOwNYHpo/TTgXbyq8EiFeJ0x2wJvOed+DFWB32Jm30dsdw8w3sx+DyQDlzjnAt61S2T3ou/7+qXv/pjQNWAHaDI+EZHdiJl1A15zzh1S32UREZG61ZCuAWouJSIiIiIiMaWaDBERERERiSnVZIiIiIiISEwpZIiIiIiISEwpZIiIiIiISEwpZIiIiIiISEwpZIiIiIiISEwpZIiIiIiISEz9P+SJ2ud/GCBQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 905.669x362.268 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set default aspect ratio for two figures side-by-side\n",
    "aspect_ratio =  .4\n",
    "size_mult    = 2.25\n",
    "mpl.rcParams[\"figure.figsize\"] = [fig_width * size_mult, fig_width * aspect_ratio * size_mult]\n",
    "#https://matplotlib.org/3.1.0/tutorials/intermediate/gridspec.html\n",
    "width_ratios = [1.1, .9]\n",
    "fig, (ax1, ax2) = plt.subplots(1,2, gridspec_kw = {'width_ratios':width_ratios})\n",
    "plot_forcing(ax1, lw, ylab_format_string=\"{:.2f}\")\n",
    "make_legend(ax1, lw.realization)\n",
    "plot_forcing(ax2, sw)\n",
    "make_legend(ax2, sw.realization, x_left = -0.25, band = \"Shortwave\")\n",
    "if save_figs: fig.savefig(figdir.joinpath('Reference-forcing-present-day.pdf'), dpi=600, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agreement among models across experiements - forcing... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uvhiAPuPHM3D69DRHJJkg1cm2P7A7ZrvRzA72G7t7mbtvjueYWO6+wN2L3L1o0KBBSQtaRHoOM6Ng0SIGTJ1KwcKFaj6WpIgr2ZrZBWb2OzPbbmabzew9M3vEzD7bwVPsAfrFXt/dG0I4RkQkYTmFhRSUlpJTWJjuUCRDdDjZmtlPgInAd4ECdx/u7icA9wGfM7P/6sBpyoDJ0fONBV4P6RgREZEuI55Hf+519x2tC939deBbZja4A+d4DJhgZmsAA6aa2bVAvrsv6OgxccQsIiKSdubNo+7ae4HZBOB/AHPd/RUzm36YxNhlFBUVeXl5ebrDEBGRnqXNTv6O1GxvJqhNftvMjgbOSmJQIiIiGa8jfbZV7l7t7t8ELgPOCTkmERGRjNKRZPvH5m/c/Q7g5+GFIyIiknmOmGzd/Q+ttn8SXjgiIiKZJ+6FCMzsKqCEYLYnANx9cjKDEhERySSJrPrzY2AGsCvJsYiIiGSkRJLtX919ZbIDERERyVSJJNs/mNlaYENzgbuXJC8kERGRzJJIsr0V+L9AdXJDERERyUyJJNvt7v7/kh6JiIhIhkok2e43s6XAesAB3P2upEYlIiKSQRJJtkuSHoWIiEgG63CyNbOHgCeB37n7vvBCEhERySzxLB7/v4G+wDwz+62Zfd3MTgopLhERkYwRTzNygbv/HPi5mWUDFwJfM7OT3f1z4YQnIiLS/cWTbH9iZsOAVcBS4E/uvjycsERERDJHh5OtuxebWS9gHFAMTDczgFXufm844YmIiHR/cY1GdvcDZrYOOBroB4yJfomIiEg74hmNPBO4AhgILAOeAO5w9/pwQhMREckM8dRsv0PQV3sfQdOxkqyIiEgHxPPozyBgATARKDOzP5jZjWZWGE5oIiIimSGeAVL1wPLoF2Y2EbgLmAtkhRKdiIhIBoinz7YIuCD6NQp4FXgYuC6c0ERERDJDPH22/w48DdwLrHd3DyckERGRzBJPn+1bwJPu/nLrRGtmZ5nZ/OSGJiIikhniqdneBdwbbU5+E9hB8BjQWcCLwLeTHZyIiEgmiGeA1EfAzWbWDxgLHAt8AHzd3T8OKT4REZFuL+71bN19L/CnEGIRERHJSPH02XaamUXMbJ6ZrTWzlWZ2cqv9V5nZS9H9X42W5ZjZI2a2xsyeN7NRqYxZRESks+Ku2XbSNUCeu48zs7HALOBqCJIqcD9wDvAxwcQZS4BzgWx3P8/MJgA/AP4xxXGLiIgkLKU1W+B8gikfcfcXgKKYfaOBt919l7vXAasJnun9G5BtZhGgP6BpIkVEpFuJO9ma2ftmVm9mW82szsz2mNlb0VrnkfQHdsdsN0YXom9r315gALAPGAlsBH4KzDlMbNPNrNzMyquqquJ4VyIiIuFJpGb7HPD37l5AUBv9PTAJ+H4Hjt1DsDTfweu7e0M7+/oB1cA3gKfd/VTgTOBhM8tr6+TuvsDdi9y9aNCgQR1/RyIiIiFKJNkOc/c3Adz9HaDQ3d8GGg5/GABlwGSAaJ/t6zH7NgCnmNnRZpYLXAisBXbRUuP9CMhBczGLiEg3ksgAqW1m9kNgDXAesD3ahFzXgWMfAyaY2RrAgKlmdi2Q7+4LomvmPk3wR0Cpu79vZvcDpWb2PJAL3KXnekVEpDuxeKc4jjbhTidYjOAvwEPAGOBdd9+R9AgTVFRU5OXl5ekOQ0Sk2/KGBrbfeisNW7aQN2bMwfLadevIHj6cIXPmYNmpfqily7O2ChO5S/8DuAnII+ir/aa7n9iJwEREpCvKyqJu40ZqVq1i35IlLeWRCH2KiyFLPXodlUif7beAqwhqtqMIBkmJiEiGMTOGlpZieXlg1lyI5eVRUFqKWZuVOGlDIsn2XXd/290PNH8lPSoREekSckeOZPCsWdDc5ejO4NmzyRkxIr2BdTOJNCPXmNlTwCuAA7j7XckMSkREuo6BM2aw59FHqVmxgj7jxzNw+vR0h9TtJJJsn0x6FCIi0mWZGQWLFlF1zz0MuuceNR8nIO7RyN2FRiOLiEgadG40spl9xd1/Zmb3EW0+bqZmZBERkfbF04y8OfrvxjACERERyVQdHo3s7k9Hv30TGODuDwOXceiUiyIiItJKIo/+zAGWRb+/G3ggadGIiIhkoESSbYO7vwHg7u8CTckNSUREJLMk8ujPJjP7PwQr8nwGeD+5IYmIiGSWRGq2i4EPCJbKqwJKkhqRiIhIhkkk2d7h7g+4+03uPkfTNYpId1dfWcnWkhLqKyvTHYpkqESakd3MHiMYldwEes5WRLovd2frlCnUrFhB/aZNFC5bphmSJOkSqdmWAs8DHwIfoeduRaQbq54/n5oVKwCoWb6c6gUL0hyRZKJEku1+4EbgswTr2mbmfI8ikvHqKirYcfvthywft2PmTOoqKtIal2SeRJLtN4Ax7n4NcBZwazIDEhFJBXdnW0kJXlt7yPJxXlvLtmnTyNR54yU9EumzbXL3fQDuvtfMapMck4hI+BobyR09mkh+Pnljxhwsrl23juzCQmhshOxEfkWKfFIin6R3zGwW8BxwIfBOckMSEQmfZWczdO7cdIchPUSHm5HNbED02xLgXWBC9N+vhhCXiIhIxoinZruEoCb7E3e/KaR4REREMk48yXa/mb0EnGJmZ8bucPfzkhuWiIhI5ogn2U4CCoD5wM3hhCMiIpJ5Opxs3b0J2AJcEV44IiIimSeR52xFREQkDkq2IiIiIVOyFRERCVmnk62Z/TYZgYiIiGSqZNRsOzyphZlFzGyema01s5VmdnKr/VeZ2UvR/V+NKb8zWrbOzKYlIWYREZGUiTvZmtnvzWyyRRd8dPddcRx+DZDn7uOAO4BZMefNAe4HLgMuAqab2RAzKwbOI1hl6CJgeLwxi4iIpFMiNdt7gcnAejO7x8wK4zj2fGApgLu/ABTF7BsNvO3uu9y9DlgNXABcDrwOPEYwi9UTCcQsIiKSNnEvRODu5UC5mR0FPAi8BfTq4OH9gd0x241mlu3uDW3s2wsMAI4FRgBXAicAj5vZKG9j/Sszmw5MBygsjOdvABERkfAk0ox8gZk9BKwC3gBOiuPwPUC/2OtHE21b+/oB1cCHwNPuXufubwK1wKC2Tu7uC9y9yN2LBg1q8yUiIiIpl0gz8m3Ao0Cxu/+bu2+J49gygiZozGwsQfNwsw0E8y4fbWa5BIserCVoTp5ogQKgL0ECFhER6RYSWc92DjAXyDKz3wCb3P2hDh77GDDBzNYABkw1s2uBfHdfYGYzgacJ/ggodff3gffN7ELgxWj519y9MYG4RURE0sLa6Po8/AFmzxGMKv4dweIEZe7+6eSH1jlFRUVeXl6e7jBERKRnsbYKE2lGbnL3jwB391qCgUwiIiLSjkSS7dtmdh9wjJndAWxKckwiIiIZJZFkeyNBgl0NfEwcM0iJiIj0RIk8Z9sAzAshFhERkYzU4WRrZu8BsaOp6oEc4IC7j052YCIiIpkinmbkUcCngBXAF93974B/JGhOFpEeor6ykq0lJdRXVqY7FJFuo8M1W3c/AGBmJ7n7i9Gy9Wb2d2EFJyJdi7uzdcoUalasoH7TJgqXLSO6JomIHEYiA6Sqzez70eXw/g9QkeSYRKSLqp4/n5oVKwCoWb6c6gUL0hyRSPeQSLL9ErCdYEKL7cDUpEYkIl1SXUUFO26/HZprsmbsmDmTuoqKtMYl0h0kMhr5Y4LpGkWkh3B3tpWU4LW10DzrnDteW8u2adPUnCxyBInMjSwiPU1jI7mjRxPJzydvzJiDxbXr1pFdWAiNjZCtXyci7dH/DhE5IsvOZuhcNWiJJCqRPttDmNlvkxGIiIhIpup0skXTNYqIiBxWp5Otu+9KRiAiIiKZStM1ioiIhEzTNYqIiIRM0zWKiIiELJFHf6rN7PvAi8A4NF2jiIjIYSVjusYpyQxIREQk0yRSs/2Gu9/bvGFm9wF3Ji8kERGRzBLPaORpwFeA0WY2OVqcRTAiWclWRESkHfHUbH8BLAP+FfgBcDSwE/gghLhEREQyRjx9tqcBvwe+BnwaWEqQfC9LflgiIiKZI55k+wPgBnevB+4lGCB1DnBHGIGJiIhkiniakSPu/pqZFQB93f1lADNrCic0ERGRzBBPzbb5tRMJmo8xs15Av2QHJSIikkniqdkuM7MyYDjwOTM7CXgQ+H+hRCYiIpIh4pmu8d/N7HHgA3f/sDnZuvtj4YUnIiLS/cU1qYW7b4j5/h3gnXiON7MI8F/AmcAB4Cvu/nbM/quA7wANQKm7/zRm33HAOmCCu2+M57oSn6YGWP4A7K2Cwae2lO94E/odB+Nvg0gi06FIt+MNDWy/9VYatmwhb8yYg+W169aRPXw4Q+bMwbL1YRA5klT/L7kGyHP3cWY2FpgFXA1gZjnA/QQjnD8Gysxsibtvj+6bD+xPcbw9kmXBR5WwZT28WxZTbjDs7GC/9BBZWdRt3EjNqlXsW7KkpTwSoU9xMWTpwyDSEZ1ePD5O5xM8n4u7vwAUxewbDbzt7rvcvY5g6b4Lovt+DMwDtqYw1h7LDC6/E7J6AdZcGGxPvCvYLz2DmTG0tBTLy2v5wZtheXkUlJZi+jCIdEiqk21/YHfMdqOZZbezby8wwMymAFXu/vSRTm5m082s3MzKq6qqkhVzjzRgKBTfAni0wIPt/kPSGZWkQ+7IkQyeNQs8+mFwZ/Ds2eSMGJHewES6kVQn2z0c+qhQxN0b2tnXD6gGSoAJZrYSOAv4uZm1+Svf3Re4e5G7Fw0aNCjJofc8Z1wNw6PddMPHBNvSMw2cMYM+F18MQJ/x4xk4fXqaIxLpXlKdbMuAyQDRPtvXY/ZtAE4xs6PNLBe4EFjr7he6+0XuXgy8AnzZ3benNuyeySxoNj5tspqPezozo2DRIgZMnUrBwoVqPhaJU6oHSD1GUEtdQ9AbONXMrgXy3X2Bmc0Enib4I6DU3d9PcXzSSv8hQaIVySkspKC0NN1hiHRL5s39MBmmqKjIy8vL0x2GiIj0LG02+6S6GVlERKTHUbIVEREJmZKtiIhIyJRsRUREQqZkKyIiEjIlW5EY9ZWVbC0pob6yMt2hiEgG0XIdIlHuztYpU6hZsYL6TZsoXLZMkzeISFKoZisSVT1/PjUrVgBQs3w51QsWpDkiEckUSrYiQF1FBTtuv/2QlW12zJxJXUVFWuMSkcygZJtm6iNMP3dnW0kJXlt7yMo2XlvLtmnTyNRZ1kQkddRnm0ZdvY+wqQGWPwB7q2DwqS3lO96EfsfB+NsgkgmfoMZGckePJpKfT96YMQeLa9etI7uwEBobIbtzb9QbGth+6600bNnyyWsMH86QOXOwTl5DRLou/e9Oo7b6CI+aMSPNUbWwLPioErash3fLYsoNhp0d7M8Elp3N0Llzw71IVhZ1GzdSs2oV+5YsaSmPROhTXAxZGXIzRaRNakZOk+7QR2gGl98JWb1omVrbgu1MXHIvzCZ9M2NoaSmWl3fIz9zy8igoLe1SLRoiknxKtmnQnfoIBwyF4luA5pA82O4/JJ1RJV9zk/7uhQvZOnVqKD+D3JEjGTxr1iE/88GzZ5MzYkTSryUiXYuakdMhBX2EyXTG1fDmctj8MgwfE2xnmlQ16Q+cMYM9jz5KzYoV9Bk/noHTpyf9GiLS9Wg9W+mQPdthTSmcV5J5tdq6igrePe00fP/+oNZphvXuzYl//Su5I0cm/Xr1lZVU3XMPg+65h5zCwqSfX0TSqs0+ISVb6dHcncpLLqFm1SpoamrZER241NVGiItIl9fmL4yu01Ypkg7drElfRLon/RaRHq2jj/3UrF3L1htuoODhh+kzblwKIhORTKLRyCJH0NTUxObJk6l/6y02T55MU2xzs4hIByjZihzBtuuuo6m6GoCm6mq2XX99egMSkW5HyVbkMD5evZo9v/rVIWV7HnmEj1evTlNEItIdKdmKtKOpqYktV13V5r4tV12l5mQR6TANkBJpT10d2ccfT2OvXuSOGtVSvGEDWYMGQV0d5OWlMUAR6S6UbEXaEcnL46S//CXdYYhIBlAzsoiISMiUbEVEREKmZCsiIhKylCZbM4uY2TwzW2tmK83s5Fb7rzKzl6L7vxotyzGzxWb2vJm9aGafS2XMIiIinZXqAVLXAHnuPs7MxgKzgKshSKrA/cA5wMdAmZktASYBH7r79WZ2DLAeeDzFcXcJTQ2w/AHYWwWDT20p3/Em9DsOxt8GEQ15ExHpclL9q/l8YCmAu79gZkUx+0YDb7v7LgAzWw1cAPwG+G3M6xpSFGuneUMD22+9lYYtWz45yf3w4QyZMweLY5J7y4KPKmHLeni3LKbcYNjZwX4REel6Up1s+wO7Y7YbzSzb3Rva2LcXGODu+wDMrB9B0v12eyc3s+nAdIDCrrBOaFYWdRs3UrNqFfuWLGkpjy7fRlZ82dEMLr8TFl0PDQcABwyyesHEu4L9IiLS9aQ62e4B+sVsR6KJtq19/YBqADMbDjwG/Je7P9Leyd19AbAAgvVskxd2YsyMoaWln1yYPC+PgtLShNZJHTAUim+BZT+OFniw3R0XdE92zV9EpKtK9W+yMuAq4NFon+3rMfs2AKeY2dHAPuBC4MdmNhh4BrjF3Z9NcbydljtyJINnzWL7TTcFBe4Mnj2bnBEjEj7nGVfDm8th88swfEyw3S0lueYvItJVmXvqKoBmFgH+CziDYDX7qcAYIN/dF5jZVcB3CEZJl7r7XDP7D+B/AhtjTjXJ3fcf7lpFRUVeXl4extuIW32ts/DSCg4c6E3/vO30OvNMDOPDCugzEK77GWTHOevfnu2wphTOK+metdpmdRUVn6z59+7NSW+80ak/SERE0qTNJsuUJttU6krJtqkJ5k5spK4mQuufQ698uPlJiPTgJ553zZvXUvMHhsybx1EzZqQxIhGRhLWZbHvwr/jUiUTg8z/Koq2fwed/1LUSbX1lJVtLSqivrEzZNQfOmEGfiy8GoM/48QycPr1T50vHexARORyNPkmRYWfCqEth47KWslET4PjTu87zs+7O1ilTqFmxgvpNmyhctiyhQVzxMjOGzJtHxbnnUvvaa2yKJl5oWWHnhPJyIh1YYSdd70FE5HCUbFNo0nfgvRfgwL6g+XjS3UF5V3l+tnr+fGpWrACgZvlyqhcsSFlzbs7JwWRiTTt3sn/VqkP2eV0d5OZ26DzpfA8iIu3pQg2YmS8SgX/4EQwcFvzb3Hzc/PxsVi9aWppT/PxsXUUFO26/veViZuyYOZO6iorwLw5EIhGGxY5IjjH8iSeIdKCtPd3vQUSkPRog1YW8+vuY52eBS78JZ14T/nXdncpLLqFm1apgNFez6CM4iTbFJvIc7fvXXsueX/3q4Hb/a6/l+F/+Mm3vQUQkTm3+olEzcheStudnGxvJHT2aSH7+J5NiYSE0NkIik0sk8Bzt0F/8gn1PPUVTdTWRgQMZunhxet+DiEgSqGbbxWTK87PNEnmOtmbtWrbecAMFDz9Mn3HjUhyxiEin6DlbSQ89RysiPYies5X0SPZztCIi3Y2SrYTOzChYtIgBU6dSsHChBiqJSI+jESNJ1FUmp+iKcgoLKSgtTXcYIiJp0UN/9Yejq0xOISIiXYuSbSe1rs0eeyK8/yp4Y/QFWtxdRKTHU7LtpPZqswd108XdtbC7iEjy6LdlJzVPtbjoemg4ALR6kqr/UNi3E9Y81M36brWwu4hI0nT1X/ndwoChQe01dqrFAUNh9zbYsw1eWBSUJbvvNszap5kxtLT0kxNS5OVRUFqqEcUiInFQsk2S1lMtXnYHPPzlmNpuGH23Idc+c0eOZPCsWS0TUrgzePbsdmd+EhGRtuk52yQxg8u/1cCJR63hjHdKqF9wD58e+URLs7JD8c2NSe27ba59Wl7eISvdJLP2qQkpREQ6T8k2CZoagibk5XOy6LX7Ld78ywjWlMI7ZU30OrAFgEH2Mqdfk/zb3Vz7pHnazSTXPjUhhYhI52lu5CRwh998PRiRfMjt9EYG7llLfsO7TPjFeI7+9LCQrh9dXm7FCvqMH6/l5ERE0kdzI4elzcXfvYmsplrOfutLXHbb/tASbXB91T5FRLoyJdskaR6RfLCP1iKMfm8mx3z25JT0czZPh5hTWBj6tUREJD5Ktkl0xtXBSGSAYZ+q5e8n1cdd06yvrGRrSQn1lZUhRSkiIqmmZJsEzQmyYXMlE++C0ybDpH/L4/iF8dU03Z2tU6awe+FCtk6dSqb2p4uI9DQaIBWnpgZYdu9ePlzzNsMmnkhkQH92L3qYnTuPpk+kivP/YQtZWcE9jXdyCS2yLiLS7bXZlKlkG6emJucXF71CVdMZh04F5Y0cs3sln/nrpS13Ojq5REdGB9dVVHxytqbevTnxr38ld+TIpL8PEREJRZu/7DWDVDvaW5t2yzObyNnxOpFjTqUp0hsscnDk8Rlvl7Tc5Tgml3B3tpWU4LW1hzwv67W1bJs2TY/yiIh0c0q27Wh3NR8fzjG5xzOq4nbeOGle9MXByOPeB2IGNcUzuURjI7mjRxPJz//kHMeFhdDYCFphR0Sk21Iz8mHs3gaLrnMaDjjQXIPdz4XrP0XegUr+fNqzfDRwPMfWljFx8jOYwa65c2n68EP6XHwxhc8+qxqpiEjPkv5JLcwsYmbzzGytma00s5Nb7b/KzF6K7v9qR44J04ChcNEtcPA2xdRgzYxP197NyNw/8dmzn+K479zNcd/7Hie+/HIwucSiRUq0IiICpL4Z+Rogz93HmdlYYBZwNYCZ5QD3A+cAHwNlZrYEOK+9Y8LQ1ADP3g/vvQBZudD3aCO3dyN1+7Owxv1U9z2LYZZNVu9cRj/7CGeMGAFMOHh88+QSIiIizVKdbM8HlgK4+wtmVhSzbzTwtrvvAjCz1cAFwLjDHJN0zX21e3cE29WbAYJRxx7Jo6b3qZg3MHj2f2qpORER6ZBUT2rRH9gds91oZtnt7NsLDDjCMYcws+lmVm5m5VVVVQkFaBasOZud23qPE7EDnPl2CX211JyIiMQh1cl2D9Av9vru3tDOvn5A9RGOOYS7L3D3IncvGjRoUMJBDhgKxbe2LjUumlbDkGsv0WT/IiISl1Qn2zJgMkC0//X1mH0bgFPM7GgzywUuBNYe4ZjQnHE1DDu7ZXvY2XD2lKM12b+IiMQt1X22jwETzGwNwfDoqWZ2LZDv7gvMbCbwNMEfAaXu/r6ZfeKYVARqBpP+FVbNDRbyKf5aUCYiIhIvPWcrIiKSPOl/zlZERKQnUrIVEREJmZKtiIhIyJRsRUREQqZkKyIiEjIlWxERkZBl7KM/ZlYFbErCqY4FdibhPNIxut+ppfudWrrfqZfqe77T3Se2LszYZJssZlbu7qEufiAtdL9TS/c7tXS/U6+r3HM1I4uIiIRMyVZERCRkSrZHtiDdAfQwut+ppfudWrrfqdcl7rn6bEVEREKmmq2IiEjIlGxFRERCpmTbDjOLmNk8M1trZivN7OR0x5TJzCzHzBab2fNm9qKZfS7dMfUEZnacmW02s1HpjiXTmdmd0d8n68xsWrrjyWTR3yePmNma6O+UtH++lWzbdw2Q5+7jgDuAWekNJ+NdB3zo7hcAk4D/THM8Gc/McoD5wP50x5LpzKwYOA/4LHARMDyd8fQAk4Fsdz8P+DfgB2mOR8n2MM4HlgK4+wtA2h+KznC/Ae6O2W5IVyA9yI+BecDWdAfSA1wOvA48BiwBnkhvOBnvb0C2mUWA/kB9muMhO90BdGH9gd0x241mlu3uSgIhcPd9AGbWD/gt8O30RpTZzGwKUOXuT5vZnemOpwc4FhgBXAmcADxuZqNcj4OEZR8wEthIcO+vTGs0qGZ7OHuAfjHbESXacJnZcGAFsNjdH0l3PBmuBJhgZiuBs4Cfm9mQtEaU2T4Ennb3Ond/E6gFBqU5pkz2DYL7fSpwJvCwmeWlMyAl2/aVEbT7Y2ZjCZqAJCRmNhh4BviWu5emO55M5+4XuvtF7l4MvAJ82d23pzeqjLYamGiBAqAvQQKWcOyipWXyIyAHyEpfOGpGPpzHCP7yXwMYMDXN8WS6u4CjgLvNrLnvdpK7a/COdHvu/oSZXQi8SFDJ+Zq7N6Y5rEx2P1BqZs8DucBd7v5xOgPSDFIiIiIhUzOyiIhIyJRsRUREQqZkKyIiEjIlWxERkZAp2YqIiIRMyVZERCRkSrYiIiIhU7IVkYxkZteY2U/N7A9mdlm645GeTZNaiEhGM7OjgB+7u9aQlbRRzVYkAWb2LTPb1jy5uZlNMbMfpjuujjKzLDN72sxWR5NRIueYaGbTOxnHMWY238yWm9lnomW5ZrbbzL4Z87pVZnZm9PtjzSye+bO/DcyNHjvEzLRWsqSckq1IYr4E/Br4YroDSdBQ4Fh3P9/ddyVyAndf6u4LOhnHvQSJ8BnggmjZBcDTwBUA0T9ohrv7q9H9VwBPHenE0Un//x14yt1fjsa8HdhrZhd1Mm6RuCjZisTJzIqBdwgWXv9azK5xZvasmb1kZs2J4lQzWxOtmT1rZse3c84pZvY7M3vSzNZHtx8zs7fM7Oroa3LM7CEzey5aIy02s/5m9qiZPWNmL5vZTTHne9TMnjCzDdH1a2MtAE6J1ipzzGxxNM4/m9n/jB7ffJ1LzKy3mf3azNaaWbmZjWuuzbd3regxvzGzsuixW1u95/7AOe7+GvAnWpLtZOBnwEAzGwCMA1bFHHoZMPRI9wv4F+BS4J/M7MaY4x8Bvt7uD1gkBFr1RyR+XwF+5u5vmtkBMzs3Wv4xQa1rEPBnM3sKmACsA2YSJJOjgPfbOW8/d7/MzL5IsB7nWKCYIDH8IXrdne4+zcyOAZ4Drgd+7e7/HV26bRXwYPR8A9z9cjM7BVgCLIq51s3R42aY2S3R815vZv2Al4H5wC53b0703wAq3P2LZvb3BEmsOuZ8bV1rOvCeu3/BzEYBf231fscCb0a/Xw+MMjMDLiRYBWpZ9DpnAEujceQAvYmuN324++Xuc4A5bdznN4DPtlEuEhrVbEXiEO3fnAx83cyWAgOAW6K7V3vgA4K1NI8BHgJ2EiSLW4CGw5x+ffTfamCDB6MXdwHNi16fDky2YMH33xH8sbwDuMbMfkHQN5kTc75Xov9ujjlHW0YTJG7cfS9BMsqiJREC/B2wNvqav7j7A63O0da1RgNrosdsBKpaHXNsNH7cvQl4FZgIbHf3AwRNxZ8Fzieo+UKQiJ+Pfn+k+9Wm6NJ2DWam33+SMvqwicTnOuAhd7/M3ScC5xI0aw4CzoFgEA6QT5Bkrwaed/dLgN8A3zrMuY/0aMBG4FfRBd8nRc93O7DW3a+Lblsc52u2gWgTbrRme3r02KZWr2l+fyea2SMdiP0vBE3AmNlJBMk11gfAwJjtPxHUaJv7Y1cDYwDc/aNo2ZXAE4e55hFFa88N0QQvkhJKtiLx+QqwuHnD3WsIapk5QG8zWw48DsyI1rTKgR9YsIj1jcBPzOxoM/vvBK49n6CpdRVBjXETQZPt181sNXAbQY2tV5znXQAcEz3HSuB7BImw9bVPjF7758DsDpz3IWCkmT0H3APUttr/AnBmzPafCGqxTwK4ex1BrfW5mNec4u5vdeDah3M60Vq6SKroOVsRCYWZnQfku/sz0b7cpe5+UqvXzAPmu/v6Nk8STlz/F1ji7s8f8cUiSaKarYiE5V3gTjMrA37JoSO3m32HYLBWSkSb+Psr0UqqqWYrIiISMtVsRUREQqZkKyIiEjIlWxERkZAp2YqIiIRMyVZERCRkSrYiIiIhU7IVEREJmZKtiIhIyP4/BjzT5Hn6UIUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 483.024x340.958 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set default aspect ratio for two figures side-by-side\n",
    "aspect_ratio = 4.8/6.8\n",
    "size_mult    = 1.2\n",
    "mpl.rcParams[\"figure.figsize\"] = [fig_width * size_mult, fig_width * aspect_ratio * size_mult]\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "ax.scatter(xr.ufuncs.fabs(lw.fln.sel(level=toa, expt=isForcing).mean(dim=\"realization\")), \n",
    "                          lw.fln.sel(level=toa, expt=isForcing).std (dim=\"realization\"), \n",
    "                          label=\"LW TOA\", c=col_lw, marker=10)\n",
    "ax.scatter(xr.ufuncs.fabs(lw.fln.sel(level=sfc, expt=isForcing).mean(dim=\"realization\")), \n",
    "                          lw.fln.sel(level=sfc, expt=isForcing).std (dim=\"realization\"), \n",
    "                          label = \"LW SFC\", c=col_lw, marker=11)\n",
    "ax.scatter(xr.ufuncs.fabs(sw.fsn.sel(level=sfc, expt=isForcing).mean(dim=\"realization\")), \n",
    "                           sw.fsn.sel(level=sfc, expt=isForcing).std (dim=\"realization\"), \n",
    "                           label = \"SW SFC\", c=col_sw, marker=11)\n",
    "ax.set(xlabel=\"Abs. mean forcing \" + wm2, \n",
    "       ylabel=\"Std. dev. forcing \" + wm2)\n",
    "l = ax.legend()\n",
    "for i in l.get_texts()[:2]: \n",
    "    plt.setp(i, color=col_lw)\n",
    "plt.setp(l.get_texts()[2], color=col_sw)\n",
    "sb.despine()\n",
    "plt.tight_layout()\n",
    "if save_figs: fig.savefig(figdir.joinpath('Forcing-Mean-and-StdDev.pdf'), dpi=600, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ... and fluxes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>expt</th>\n",
       "      <th>rln</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concat_dim</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LW TOA</th>\n",
       "      <td>PI</td>\n",
       "      <td>0.577901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LW SFC</th>\n",
       "      <td>PI</td>\n",
       "      <td>0.459689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SW TOA</th>\n",
       "      <td>PI</td>\n",
       "      <td>0.691989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           expt       rln\n",
       "concat_dim               \n",
       "LW TOA       PI  0.577901\n",
       "LW SFC       PI  0.459689\n",
       "SW TOA       PI  0.691989"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.concat([lw.rln.sel(expt=\"PI\", level=toa).std (dim=\"realization\"),\n",
    "           lw.rln.sel(expt=\"PI\", level=sfc).std (dim=\"realization\"),\n",
    "           sw.rsn.sel(expt=\"PI\", level=sfc).std (dim=\"realization\")], \n",
    "           dim = [\"LW TOA\", \"LW SFC\", \"SW TOA\"]).to_dataframe() #.to_latex(float_format=\"{:1.3f}\".format, buf=\"pd-flux-stddev.tex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LBL agreement vs GCM error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/7941226/how-to-add-line-based-on-slope-and-intercept-in-matplotlib\n",
    "def abline(ax, slope, intercept):\n",
    "    \"\"\"Plot a line from slope and intercept\"\"\"\n",
    "    x_vals = np.array(ax.get_xlim())\n",
    "    y_vals = intercept + slope * x_vals\n",
    "    ax.plot(x_vals, y_vals, '--')   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 644.031x257.613 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set default aspect ratio for two figures side-by-side\n",
    "aspect_ratio = .4\n",
    "size_mult    = 1.6\n",
    "mpl.rcParams[\"figure.figsize\"] = [fig_width * size_mult, fig_width * aspect_ratio * size_mult]\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "\n",
    "# Plot the errors for each GCM parameterization \n",
    "# \n",
    "# I bet there's a better way to do this - right now it's a loop over realizations so we can plot values by experiement\n",
    "#    against values by experiment and by model\n",
    "#\n",
    "for m in gcm.realization.values: \n",
    "    ax1.scatter(lw.fln.sel(expt=isForcing).isel(level=toa).std(axis=0),\n",
    "                xr.ufuncs.fabs((gcm.sel(realization=m, expt=isForcing) - lw.sel(realization=\"LBLRTM-12-8-p1-f1\", expt=isForcing)).fln).isel(level=toa).values, \n",
    "                label=m[:-6], c=gcm_cols([m]), marker=forcing_marker(m))\n",
    "\n",
    "#\n",
    "# Adapted from make_legend above \n",
    "#\n",
    "gcm_names = np.unique([r[:-6] for r in gcm.realization.values])\n",
    "bottom, top = ax1.get_ylim()\n",
    "y_size = top - bottom\n",
    "left, right = ax1.get_xlim()\n",
    "ys = top -.1 + np.arange(len(gcm_names)) * y_size/22.5\n",
    "for i in range(len(gcm_names)):\n",
    "    ax1.text(.005, ys[i], gcm_names[i], color=gcm_cols([gcm_names[i] + \"-p1-f1\"])[0])\n",
    "\n",
    "abline(ax1, 1, 0)\n",
    "ax1.set_xlim(left=0)\n",
    "sb.despine()\n",
    "ax1.set(xlabel = \"Ref. std. dev \" + wm2, \n",
    "        ylabel = \"Parameterization err. \" + wm2)\n",
    "\n",
    "# Shortwave \n",
    "for m in gcm.realization.values: \n",
    "    ax2.scatter(sw.fsn.sel(expt=isForcing).isel(level=sfc).std(axis=0),\n",
    "                xr.ufuncs.fabs((gcm.sel(realization=m,expt=isForcing) - sw.sel(realization=\"LBLRTM-12-8-p1-f1\",expt=isForcing)).fsn).isel(level=sfc).values, \n",
    "                label=m[:-6], c=gcm_cols([m]), marker=forcing_marker(m))\n",
    "abline(ax2, 1, 0)\n",
    "ax2.set_xlim(left=0)\n",
    "sb.despine()\n",
    "ax2.set(xlabel = \"Ref. std. dev \" + wm2)\n",
    "\n",
    "plt.tight_layout()\n",
    "if save_figs: fig.savefig(figdir.joinpath('LBL-and-GCM-error.pdf'), dpi=600, transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of forcings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanForcing = xr.Dataset({'LW TOA':lw.fln.isel(level=toa).mean(axis=0), \n",
    "                          'LW SFC':lw.fln.isel(level=sfc).mean(axis=0),\n",
    "                          'SW TOA':sw.fsn.isel(level=toa).mean(axis=0), \n",
    "                          'SW SFC':sw.fsn.isel(level=sfc).mean(axis=0)}, \n",
    "                        coords=lw.coords, attrs=lw.attrs)\n",
    "meanForcing = meanForcing.drop(\"realization\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "meanForcing.sel(expt=piForcing ).to_dataframe().to_latex(float_format=\"{:1.3f}\".format, buf=\"pi-forcing.tex\")\n",
    "meanForcing.sel(expt=co2Forcing).to_dataframe().to_latex(float_format=\"{:1.3f}\".format, buf=\"co2-forcing.tex\")\n",
    "meanForcing.sel(expt=scenarios ).to_dataframe().to_latex(float_format=\"{:1.3f}\".format, buf=\"scenarios.tex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fluxes under fixed dynamical heating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# For looking at these fluxes we'll need to be able to average globally\n",
    "#\n",
    "profiles = \"http://aims3.llnl.gov/thredds/dodsC/user_pub_work/input4MIPs/CMIP6/RFMIP/\" + \\\n",
    "           \"UColorado/UColorado-RFMIP-1-2/atmos/fx/multiple/none/v20190401/multiple_input4MIPs_radiation_RFMIP_UColorado-RFMIP-1-2_none.nc\"\n",
    "inputs = xr.open_dataset(profiles)\n",
    "profile_weights = inputs.profile_weight/inputs.profile_weight.sum('site')\n",
    "inputs.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copied from the ingest script (compute-lbl-gcm-global-mean-forcing.py)\n",
    "#   Which maybe I could just import\n",
    "#\n",
    "def compute_forcing(da, band_label):\n",
    "    '''\n",
    "    Compute forcing - up, down, net, absorption\n",
    "    Forcing calculated as\n",
    "     -(X-PD) for experiments with \"PI\" in the title,\n",
    "       X-PD for +4K experiments,\n",
    "       X-PI_CO2 for the CO2 experiments\n",
    "       X-PI for all other experiments\n",
    "    Parameters:\n",
    "        da (xarray Dataset): variables rxd, rxu, rxn, and rxa for up, down, net, and column absorption where x is band_label\n",
    "        band_label (char): 'l' for longwave, 's' for shortwave\n",
    "    '''\n",
    "    piExpts   = [f for f in expt_labels if \"PI\"  in f]\n",
    "    tempExpts = [f for f in expt_labels if \"+4K\" in f]\n",
    "    co2Expts  = [f for f in expt_labels if \"xCO2\" in f]\n",
    "    for v in [\"u\", \"d\", \"n\", \"a\"]:\n",
    "        fVar = \"f\"+band_label+v\n",
    "        rVar = \"r\"+band_label+v\n",
    "        da[fVar] = da[rVar] - da[rVar].sel(expt=\"PI\")\n",
    "        da[fVar].load()\n",
    "        da[fVar].loc[dict(expt=piExpts)  ] = -(da[rVar].loc[dict(expt=piExpts)  ] - da[rVar].sel(expt=\"PD\"))\n",
    "        da[fVar].loc[dict(expt=co2Expts) ] =   da[rVar].loc[dict(expt=co2Expts) ] - da[rVar].sel(expt=\"PI CO2\")\n",
    "        da[fVar].loc[dict(expt=tempExpts)] =   da[rVar].loc[dict(expt=tempExpts)] - da[rVar].sel(expt=\"PD\")\n",
    "    return(da)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# GFDL's file containing fluxes and temperature profiles after 1 and 120 iterations of FDH\n",
    "#\n",
    "fdh_file = 'Stratospheric-adjustment/rfmip-irf-fdh-temperature-profiles.nc'\n",
    "fdh = xr.open_dataset(fdh_file)\n",
    "fdh = fdh.assign_coords(expt=expt_labels).drop([\"profile_weight\", \"plev\", \"tlay\", \"tlev\"])\n",
    "\n",
    "# The FDH calculations are run at lower spectral resolution so the present-day fluxes are biased\n",
    "#    Remove these fluxes, leaving only the magnitude of the adjustment    \n",
    "fdh = (fdh.isel(flux_description=1) - fdh.isel(flux_description=0))\n",
    "# Net flux, absorption\n",
    "for b in ['s', 'l']: \n",
    "    fdh[\"r\"+b+\"n\"] = fdh[\"r\"+b+\"d\"] - fdh[\"r\"+b+\"u\"]\n",
    "    fdh[\"r\"+b+\"a\"] = fdh[\"r\"+b+\"n\"].sel(level=toa) - fdh[\"r\"+b+\"n\"].sel(level=sfc)\n",
    "#    \n",
    "# This compute forcing from FDH-adjusted fluxes, so the baseline for most forcing calculations also includes FDH\n",
    "#\n",
    "fdh = compute_forcing(fdh, \"l\")\n",
    "fdh = compute_forcing(fdh, \"s\")\n",
    "#\n",
    "# Global mean adjustment\n",
    "#\n",
    "adj_fluxes = (fdh*profile_weights).sum(dim='site')\n",
    "\n",
    "#\n",
    "# Global-mean IRF from the same model for comparison \n",
    "#\n",
    "grt_fluxes = lw.sel(realization=\"GFDL-GRTCODE-p1-f1\")\n",
    "for v in [vv for vv in sw.variables if \"fs\" in vv or \"rs\" in vv]:  \n",
    "    grt_fluxes[v] = sw.sel(realization=\"GFDL-GRTCODE-p1-f1\")[v]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table: Ratio of adjustment to LW forcing at PD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "(adj_fluxes/grt_fluxes).fln.isel(level=[toa,sfc]).sel( expt=piForcing).to_dataframe().to_latex(float_format=\"{:1.2f}\".format, buf=\"pd-strat-adj.tex\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-linear CO2 adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 483.024x340.958 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# Include PI CO2 experiment \n",
    "#\n",
    "allCO2Forcing = co2Forcing\n",
    "allCO2Forcing.extend([\"PI CO2\"])\n",
    "\n",
    "aspect_ratio = 4.8/6.8\n",
    "size_mult    = 1.2\n",
    "mpl.rcParams[\"figure.figsize\"] = [fig_width * size_mult, fig_width * aspect_ratio * size_mult]\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(            grt_fluxes.fln.isel(level=toa).sel(expt=allCO2Forcing), \n",
    "            (adj_fluxes/grt_fluxes).fln.isel(level=toa).sel(expt=allCO2Forcing), \n",
    "            color = col_lw)\n",
    "sb.despine()\n",
    "ax.set(xlabel = \"Instantaneous flux change (W/m$^2$)\",\n",
    "       ylabel = \"Relative impact of stratospheric equilibration\")\n",
    "fig.tight_layout()\n",
    "if save_figs: fig.savefig(figdir.joinpath('CO2-adjustment-ratio.pdf'), dpi=600, transparent=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}