pp_FHIST_enssize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse atm variables of the FHIST f09_f09 simulations (RES-CTL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Import the necessary python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import sys\n",
    "import os\n",
    "from getpass import getuser\n",
    "import string\n",
    "import subprocess\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import netCDF4 as netcdf4\n",
    "import xarray as xrµ\n",
    "import pandas\n",
    "import warnings\n",
    "\n",
    "import itertools\n",
    "import numpy as np\n",
    "from iv_utils import *\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 General Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set directories\n",
    "outdir = '/glade/work/ivanderk/'\n",
    "\n",
    "# Define directory where processing is done -- subject to change\n",
    "procdir =  outdir + 'postprocessing/' \n",
    "\n",
    "# go to processing directory \n",
    "os.chdir(procdir)\n",
    "\n",
    "# ignore all runtime warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 User settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set case name\n",
    "case_res   = 'f.FHIST.f09_f09_mg17.CTL'\n",
    "case_nores = 'f.FHIST.f09_f09_mg17.NORES'\n",
    "\n",
    "# set number of ensemble members\n",
    "n_ens = 5\n",
    "\n",
    "# set individual case names for reference\n",
    "case_res_ind   = 'f.FHIST.f09_f09_mg17.CTL.001'\n",
    "case_nores_ind   = 'f.FHIST.f09_f09_mg17.NORES.001'\n",
    "case   = 'f.FHIST.f09_f09_mg17.CTL.001'\n",
    "\n",
    "# run settings -- change this to terms directly? \n",
    "block = 'atm'  # lnd data\n",
    "               # atm data\n",
    "               # rof data\n",
    "        \n",
    "stream = 'h0'  # h0 output block\n",
    "               # h1 output block\n",
    "               # h2 output block\n",
    "               # xtrm calculated (annual)\n",
    "        \n",
    "# define start and end year\n",
    "spstartyear = '1979'   # spin up start year \n",
    "startyear   = '1984'   # start year, spin up excluded\n",
    "endyear     = '2014'   # last year of the simulation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of number ensemble members (detection of forced response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot 5 Members in violinplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_violins_enssize_response_5m(var, ax=False, ylims = False, panel_label = False):\n",
    "    \n",
    "    n_ens = 5\n",
    "    resmask = get_resmask(threshold=0)\n",
    "    # open all ensemble members and calculate temporal and spatial mean\n",
    "    da_res_em = open_da_ens(var,case=case_res, mode='all').where(resmask)    \n",
    "    da_nores_em = open_da_ens(var,case=case_nores, mode='all').where(resmask)\n",
    "    da_res_em_mean = da_res_em.mean(dim=('time','lat','lon'))\n",
    "    da_nores_em_mean = da_nores_em.mean(dim=('time','lat','lon'))\n",
    "\n",
    "    # calculate combinations with 1 ensemble member and calculate average over those ensemble members\n",
    "    mean_1_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 1))).mean(axis=1)\n",
    "    mean_1_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 1))).mean(axis=1)\n",
    "    [meshgrid_1_nores, meshgrid_1_res] = np.meshgrid(mean_1_nores, mean_1_res)\n",
    "    delta_1m = (meshgrid_1_res - meshgrid_1_nores).flatten()\n",
    "\n",
    "    # calculate combinations with 2 ensemble members and calculate average over those ensemble members\n",
    "    mean_2_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 2))).mean(axis=1)\n",
    "    mean_2_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 2))).mean(axis=1)\n",
    "    [meshgrid_2_nores, meshgrid_2_res] = np.meshgrid(mean_2_nores, mean_2_res)\n",
    "    delta_2m = (meshgrid_2_res - meshgrid_2_nores).flatten()\n",
    "    \n",
    "    print('mean_2_nores')\n",
    "    print(mean_2_nores.shape)\n",
    "\n",
    "    # calculate combinations with 3 ensemble members and calculate average over those ensemble members\n",
    "    mean_3_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 3))).mean(axis=1)\n",
    "    mean_3_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 3))).mean(axis=1)\n",
    "    [meshgrid_3_nores, meshgrid_3_res] = np.meshgrid(mean_3_nores, mean_3_res)\n",
    "    delta_3m = (meshgrid_3_res - meshgrid_3_nores).flatten()\n",
    "\n",
    "    # calculate combinations with 4 ensemble members and calculate average over those ensemble members\n",
    "    mean_4_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 4))).mean(axis=1)\n",
    "    mean_4_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 4))).mean(axis=1)\n",
    "    [meshgrid_4_nores, meshgrid_4_res] = np.meshgrid(mean_4_nores, mean_4_res)\n",
    "    delta_4m = (meshgrid_4_res - meshgrid_4_nores).flatten()\n",
    "\n",
    "    # calculate combinations with 3 ensemble members and calculate average over those ensemble members\n",
    "    mean_5_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 5))).mean(axis=1)\n",
    "    mean_5_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 5))).mean(axis=1)\n",
    "    [meshgrid_5_nores, meshgrid_5_res] = np.meshgrid(mean_5_nores, mean_5_res)\n",
    "    delta_5m = (meshgrid_5_res - meshgrid_5_nores).flatten()\n",
    "    \n",
    "    data = np.asarray([delta_1m, delta_2m, delta_3m, delta_4m, delta_5m])\n",
    "    \n",
    "    print(var+' range 1 ens member: '+str(delta_1m.min()) +' - '+ str(delta_1m.max()))\n",
    "    print()\n",
    "    # VIOLIN PLOT \n",
    "    if not ax: \n",
    "        fig , ax = plt.subplots()\n",
    "\n",
    "    # create dummy violinplot to have white left sides\n",
    "    parts1 = ax.violinplot(data, showmeans=False, showmedians=False,\n",
    "            showextrema=False)\n",
    "    for pc in parts1['bodies']:\n",
    "        pc.set_facecolor('palegoldenrod')\n",
    "        pc.set_edgecolor('darkgoldenrod')\n",
    "        pc.set_alpha(1)\n",
    "\n",
    "        # remove the left half of plot\n",
    "        # get the center\n",
    "        m = np.mean(pc.get_paths()[0].vertices[:, 0])\n",
    "        # modify the paths to not go further right than the center\n",
    "        pc.get_paths()[0].vertices[:, 0] = np.clip(pc.get_paths()[0].vertices[:, 0], m, np.inf)\n",
    "        pc.set_color('w')   \n",
    "\n",
    "\n",
    "    # this is the real violinplot\n",
    "    parts2 = ax.violinplot(data, showmeans=False, showmedians=False,\n",
    "            showextrema=False, widths=1)    \n",
    "\n",
    "    for pc in parts2['bodies']:\n",
    "\n",
    "        m = np.mean(pc.get_paths()[0].vertices[:, 0])\n",
    "        # modify the paths to not go further right than the center\n",
    "        pc.get_paths()[0].vertices[:, 0] = np.clip(pc.get_paths()[0].vertices[:, 0], m, np.inf)\n",
    "        pc.set_facecolor('palegoldenrod')\n",
    "        pc.set_edgecolor('tan')\n",
    "        pc.set_alpha(1)\n",
    "\n",
    "    # remove the line in the fifth violinplot    \n",
    "    parts2['bodies'][-1].set_edgecolor('w')\n",
    "\n",
    "    # plot medians and IQRs\n",
    "        \n",
    "    # helper function\n",
    "    def adjacent_values(vals, q1, q3):\n",
    "        upper_adjacent_value = q3 + (q3 - q1) * 1.5\n",
    "        upper_adjacent_value = np.clip(upper_adjacent_value, q3, vals[-1])\n",
    "\n",
    "        lower_adjacent_value = q1 - (q3 - q1) * 1.5\n",
    "        lower_adjacent_value = np.clip(lower_adjacent_value, vals[0], q1)\n",
    "        return lower_adjacent_value, upper_adjacent_value\n",
    "\n",
    "    medians = []\n",
    "    whiskers_min = []\n",
    "    whiskers_max = []\n",
    "    quartiles1 = []\n",
    "    quartiles3 = []\n",
    "    for ensmember in data: \n",
    "        (quartile1, median, quartile3) = np.percentile(ensmember, [25, 50, 75])\n",
    "        whisker = np.array([adjacent_values(ensmember, quartile1,  quartile3)])\n",
    "        whisker_min, whisker_max = whisker[:, 0], whisker[:, 1]\n",
    "\n",
    "        medians = np.append(medians,median)\n",
    "        whiskers_min =  np.append(whiskers_min, whisker_min)\n",
    "        whiskers_max =  np.append(whiskers_max, whisker_max)\n",
    "        quartiles1 =  np.append(quartiles1, quartile1)\n",
    "        quartiles3 =  np.append(quartiles3, quartile3)\n",
    "\n",
    "\n",
    "    inds = np.arange(1, len(medians) + 1.0)\n",
    "    inds = inds + 0.02\n",
    "    ax.scatter(inds, medians, marker='o', color='maroon', s=20, zorder=3)\n",
    "    ax.vlines(inds, quartiles1, quartiles3, color='darkgoldenrod', linestyle='-', lw=4)\n",
    "    #ax.vlines(inds, whiskers_min, whiskers_max, color='goldenrod', linestyle='-', lw=3)\n",
    "\n",
    "    # do axes settings    \n",
    "    ax.set_xticks([1,2,3, 4, 5])\n",
    "    ax.set_xlim((0.5,5.5))\n",
    "    \n",
    "    if ylims != False: \n",
    "        ax.set_ylim(ylims)\n",
    "    if panel_label != False:\n",
    "        ax.text(0, 1.02, panel_label, color='dimgrey', fontsize=12, transform=ax.transAxes, weight = 'bold')\n",
    "    \n",
    "    ax.set_xlabel('Number of ensemble members')\n",
    "    ax.set_ylabel(da_res_em.units); \n",
    " \n",
    "    \n",
    "    if var == ('TSMN'): da_res_em.name = 'TNn'\n",
    "    if var == ('TSMX'): da_res_em.name = 'TXx'\n",
    "    if var == ('TREFHT'): da_res_em.name = 'T$_{2m}$'\n",
    "\n",
    "    ax.set_title('$\\Delta$ '+da_res_em.name, loc='right', size=12)\n",
    "    ax.axhline(0, color='lightgray', linewidth=1)    \n",
    "\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_2_nores\n",
      "(10,)\n",
      "TREFHT range 1 ens member: -0.048706055 - 0.20501709\n",
      "\n",
      "mean_2_nores\n",
      "(10,)\n",
      "DTR range 1 ens member: -0.1451664 - -0.07842159\n",
      "\n",
      "mean_2_nores\n",
      "(10,)\n",
      "TSMX range 1 ens member: -0.2871704 - 0.008636475\n",
      "\n",
      "mean_2_nores\n",
      "(10,)\n",
      "TSMN range 1 ens member: 0.11502075 - 0.44265747\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5 members\n",
    "set_plot_param()\n",
    "fig, axes = plt.subplots(2,2, figsize=(8,6))\n",
    "ylims = [-0.35,0.5]\n",
    "\n",
    "axes = axes.flatten()\n",
    "\n",
    "plot_violins_enssize_response_5m('TREFHT', ax = axes[0], ylims = ylims, panel_label='a')\n",
    "plot_violins_enssize_response_5m( 'DTR'  , ax = axes[1], ylims = ylims, panel_label='b')\n",
    "plot_violins_enssize_response_5m( 'TSMX' , ax = axes[2], ylims = ylims, panel_label='c')\n",
    "plot_violins_enssize_response_5m( 'TSMN' , ax = axes[3], ylims = ylims, panel_label='d')\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig('./plots/ens_size.png',  bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 ensemble members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set parameters for plotting\n",
    "set_plot_param()\n",
    "\n",
    "# 3 members\n",
    "def plot_enssize_response_3m(var):\n",
    "    \n",
    "    n_ens = 3\n",
    "    resmask = get_resmask(threshold=0)\n",
    "    # open all ensemble members and calculate temporal and spatial mean\n",
    "    da_res_em = open_da_ens(var,case=case_res, mode='all').where(resmask)    \n",
    "    da_nores_em = open_da_ens(var,case=case_nores, mode='all').where(resmask)\n",
    "    da_res_em_mean = da_res_em.mean(dim=('time','lat','lon'))\n",
    "    da_nores_em_mean = da_nores_em.mean(dim=('time','lat','lon'))\n",
    "\n",
    "    # calculate combinations with 1 ensemble member and calculate average over those ensemble members\n",
    "    mean_1_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 1))).mean(axis=1)\n",
    "    mean_1_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 1))).mean(axis=1)\n",
    "    [meshgrid_1_nores, meshgrid_1_res] = np.meshgrid(mean_1_nores, mean_1_res)\n",
    "    delta_1m = (meshgrid_1_res - meshgrid_1_nores).flatten()\n",
    "\n",
    "    # calculate combinations with 2 ensemble members and calculate average over those ensemble members\n",
    "    mean_2_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 2))).mean(axis=1)\n",
    "    mean_2_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 2))).mean(axis=1)\n",
    "    [meshgrid_2_nores, meshgrid_2_res] = np.meshgrid(mean_2_nores, mean_2_res)\n",
    "    delta_2m = (meshgrid_2_res - meshgrid_2_nores).flatten()\n",
    "\n",
    "\n",
    "    # calculate combinations with 3 ensemble members and calculate average over those ensemble members\n",
    "    mean_3_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 3))).mean(axis=1)\n",
    "    mean_3_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 3))).mean(axis=1)\n",
    "    [meshgrid_3_nores, meshgrid_3_res] = np.meshgrid(mean_3_nores, mean_3_res)\n",
    "    delta_3m = (meshgrid_3_res - meshgrid_3_nores).flatten()\n",
    "\n",
    "    # plot values per possible combination of ensemble members\n",
    "\n",
    "    fig , ax = plt.subplots()\n",
    "\n",
    "    ax.plot(np.ones(delta_1m.shape)* 1,delta_1m, 'ro', color='goldenrod') \n",
    "    ax.plot(np.ones(delta_2m.shape)* 2,delta_2m, 'ro', color='goldenrod') \n",
    "    ax.plot(np.ones(delta_3m.shape)* 3,delta_3m,'ro', color='goldenrod') \n",
    "    ax.set_xticks([1,2,3])\n",
    "    ax.set_xlim((0.5,3.5))\n",
    "    ax.set_xlabel('Number of ensemble members')\n",
    "    ax.set_ylabel('$\\Delta$ '+da_res_em.name+' ('+da_res_em.units+')'); \n",
    "    ax.set_title(da_res_em.long_name, loc='right', size=12)\n",
    "    ax.axhline(0, color='lightgray', linewidth=1)\n",
    "    \n",
    "# for 5 members   \n",
    "def plot_enssize_response_5m(var, ax = False):\n",
    "    \n",
    "    n_ens = 5\n",
    "    resmask = get_resmask(threshold=0)\n",
    "    # open all ensemble members and calculate temporal and spatial mean\n",
    "    da_res_em = open_da_ens(var,case=case_res, mode='all')#.where(resmask)    \n",
    "    da_nores_em = open_da_ens(var,case=case_nores, mode='all')#.where(resmask)\n",
    "    da_res_em_mean = da_res_em.mean(dim=('time','lat','lon'))\n",
    "    da_nores_em_mean = da_nores_em.mean(dim=('time','lat','lon'))\n",
    "\n",
    "    # calculate combinations with 1 ensemble member and calculate average over those ensemble members\n",
    "    mean_1_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 1))).mean(axis=1)\n",
    "    mean_1_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 1))).mean(axis=1)\n",
    "    [meshgrid_1_nores, meshgrid_1_res] = np.meshgrid(mean_1_nores, mean_1_res)\n",
    "    delta_1m = (meshgrid_1_res - meshgrid_1_nores).flatten()\n",
    "\n",
    "    # calculate combinations with 2 ensemble members and calculate average over those ensemble members\n",
    "    mean_2_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 2))).mean(axis=1)\n",
    "    mean_2_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 2))).mean(axis=1)\n",
    "    [meshgrid_2_nores, meshgrid_2_res] = np.meshgrid(mean_2_nores, mean_2_res)\n",
    "    delta_2m = (meshgrid_2_res - meshgrid_2_nores).flatten()\n",
    "\n",
    "\n",
    "    # calculate combinations with 3 ensemble members and calculate average over those ensemble members\n",
    "    mean_3_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 3))).mean(axis=1)\n",
    "    mean_3_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 3))).mean(axis=1)\n",
    "    [meshgrid_3_nores, meshgrid_3_res] = np.meshgrid(mean_3_nores, mean_3_res)\n",
    "    delta_3m = (meshgrid_3_res - meshgrid_3_nores).flatten()\n",
    "\n",
    "    # calculate combinations with 4 ensemble members and calculate average over those ensemble members\n",
    "    mean_4_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 4))).mean(axis=1)\n",
    "    mean_4_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 4))).mean(axis=1)\n",
    "    [meshgrid_4_nores, meshgrid_4_res] = np.meshgrid(mean_4_nores, mean_4_res)\n",
    "    delta_4m = (meshgrid_4_res - meshgrid_4_nores).flatten()\n",
    "\n",
    "    # calculate combinations with 3 ensemble members and calculate average over those ensemble members\n",
    "    mean_5_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 5))).mean(axis=1)\n",
    "    mean_5_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 5))).mean(axis=1)\n",
    "    [meshgrid_5_nores, meshgrid_5_res] = np.meshgrid(mean_5_nores, mean_5_res)\n",
    "    delta_5m = (meshgrid_5_res - meshgrid_5_nores).flatten()\n",
    "\n",
    "    # plot values per possible combination of ensemble members\n",
    "    if not ax: \n",
    "        fig , ax = plt.subplots()\n",
    "\n",
    "    ax.plot(np.ones(delta_1m.shape)* 1,delta_1m, 'ro', color='goldenrod') \n",
    "    ax.plot(np.ones(delta_2m.shape)* 2,delta_2m, 'ro', color='goldenrod') \n",
    "    ax.plot(np.ones(delta_3m.shape)* 3,delta_3m,'ro', color='goldenrod') \n",
    "    ax.plot(np.ones(delta_4m.shape)* 4,delta_4m,'ro', color='goldenrod') \n",
    "    ax.plot(np.ones(delta_5m.shape)* 5,delta_5m,'ro', color='goldenrod') \n",
    "\n",
    "    ax.set_xticks([1,2,3, 4, 5])\n",
    "    ax.set_xlim((0.5,5.5))\n",
    "    ax.set_xlabel('Number of ensemble members')\n",
    "    ax.set_ylabel('$\\Delta$ '+da_res_em.name+' ('+da_res_em.units+')'); \n",
    "    ax.set_title(da_res_em.long_name, loc='right', size=12)\n",
    "    ax.axhline(0, color='lightgray', linewidth=1)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_enssize_response_3m('TREFHT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_enssize_response_3m('TREFHTMX')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_enssize_response_3m('TREFHTMN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_enssize_response_3m('PRECT')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot 5 Members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eBr7m7itzd2JmZwNnA4wePbrEw3nXwFHjFGhJ3VDgJWWnPhciqdQfeDsn7W1gQBHLZv4eUMR2VgKHAM8AQwjNkjOBY3N34u7TgGkA06ZNay3yOETqmgIvKbshYyZkjasD6nMhkgItwMCctIHA6iKWzfy9uqPtuHsLMD+mLzOz84ClZjbQ3Zs7n32R7kF9vKTs1OdCJJWeBxrNbI9E2hhgYZ5lF8b3ksstc/dVJW4HIFOT1dCpXIt0M6rxkopQnwuRdHH3NWY2G5hsZmcCBwInAB/Ks/itwAwzmwksBS4FZhSzHTMbC7wFvABsD9wAzHX33ObJstGUQVJPVOMlItJzGGFcruXA7YSxuRaa2QgzazGzEQDu/hBwHfAosCj+u6Kj7cT3dgMeIjQ9PgusB06u1AFlpgwK/Upbt04Z1Nw0p1K7FOkS1XiJiPQQ7v4G0KYqyN1fIXSaT6ZdD1xfynbie7cTgrGqaG/KINV6SRqpxktEROqWhq+ReqPAS0RE6lahYWo0fI2klQIvqYjmpjk0zTmcF2btRtOcw9XfQkQqQlMGSb1RHy8pu0xn10y/i0xnV6AmfS70xJNI95X5Lus7LvVCgVcNmdlgYDpwDGG056+7+6wCy14IXEx4kugewlNE6+N7c4HDgE1x8dfcfa/EukcTRo8eATwFjHf3RZU4JkhXZ9e0BYEiUn5pGr5GN3rSETU11taNwAZgKHAq8CMz2y93ITM7FrgEOBrYlfC49pU5i53n7v3jv2TQtQMwG7gMGEwYUfrO8h/Ku9LU2bW9IFBEpJw0tIUUQ4FXjZjZdsBJwGXu3uLuTwD3A6fnWfwMYLq7L3T3N4EpwPgid3UisNDd73b3d4BJwBgz27urx1BImjq7pikIFJHuTTd6UgwFXrWzJ7DZ3Z9PpC0A2tR4xbQFOcsNNbMhibRvmtlKM3vSzI4qtK67rwFeKrAfzOxsM5tvZvNbWlpKOqCMNHV2TVMQKCKVkZaHeXSjJ8VQ4FU7/YHcKTTeBgYUsWzm78yyFxOaH4cD04AHzGz3TuwHd5/m7ge7+8H9+/fPt0iH0jRXY79hR5WULiL1JU3Ne7rRk2Koc33ttAADc9IGEqbZ6GjZzN+rAdz9qcR7t5jZycDxwA9K3E/ZpKWz69olc0tKF5H6kqaHeYaMmZD1MA9oaAtpSzVetfM80GhmeyTSxgAL8yy7ML6XXG6Zu68qsO1WoCHfurFv2e4F9tPthLvg4tNFpL6kqXlv4KhxDBh1IjT0DgkNvRkw6sRU3IRKeqjGq0bcfY2ZzQYmm9mZwIHACcCH8ix+KzDDzGYCS4FLgRkAZjYIGAs8RhhO4rPAEcAFcd17gW+b2UnAg8DlwF/d/e8VOjQRkapp7Dcs741ULZr3mpvmsLppNrRuDgmtm1ndNJu+Ox6s4Eu2Uo1XbRlhXK7lhEllz3X3hWY2wsxazGwEgLs/BFwHPAosiv+uiNvoA1wFrCCMBfZlYJy7/yOuu4Lw9OTVwJuEIO1z1Tk8EZHKGjJmAjT0yU5s6FOT5j091SjFUI1XDbn7G0Cb2yB3f4XQKT6Zdj1wfZ5lVwCHdLCfR4CKDR8hIlJTDYQOFsnXNZCmZk9JL9V4iYhI3Vq1YCps2ZiduGVjTWqZ9FSjFEOBl1REWsbV2drJtdh0Eakraapl0vA1UgwFXlJ2zU1zWPbURVnj6ix76qLaBF+ZTq7FpldQaoJRkW4kTbVMqxc9WFK69EwKvKTsVjw9OW/V/4qnJ9cmQynQ3DSHZX/MCUb/WKNgVKQbSdNMGa0b3yopXXomBV5Sdls2vFlSek+wfP6V0JoTjLZuDOki0mlpmilDpBh6qjGHmQ0FjiEMOjoIeIsw1+HD7v56LfMm9Ut3wiKVk5aZMnpts33eG8xe22xfg9xIWqnGKzKzfczsF8BzwOmE8bFej/+fDiw0s1+Y2b41zGZdaOgzqKR0EZHuoP+I40tKl55JNV7vmgF8GzjV3dfnvmlm2wCfAqYD/6+6WasvA0b+K80vzsyb3mM19Gnb1JhJF5FuIW1zwzY3zWHVgqlsWruExn7DGDJmQipqBns61XhF7j6WMP9hm6Arvr8B+KC7K+jqQLoKn0IjKVZ5hMV8QVd76SJSd9I0tEVz0xyWz5uY9UDP8nkT9UBPCijwynafmY3N94aZXQ+cVuX81KU0FT7Zw1kXky4i9SYtQ7WkaWgLTV+UXgq8shnwSzP7YFaimRMmsD6yJrmqM2kqfESke0tTzU6ahrZI1w2wJCnwSnD3O4AJwG/MbH8AM5sOfAw40t1frmH26saQMROgV07fpV61mbQ2PQqNlK8R9EW6Ik01OwNHjWPAqBPfnRmjoTcDRp1Yk35VugFOLwVeOdz9FuBS4GEzuxc4DDjC3V+tbc7qTG5LXo9v2Ss0Un71R9CH9DTNiHRVmmp2mpvmsLpp9rszY7RuZnXT7B5f+ybZ9FRjgpl9NP75IvAkoabrS8C+mWEk3P13Ncpe3Vi1YGrewUJXLZiqJ2pSINM0k6klyDTNAPp8pO409hsWmxnbpldbe7Vv1f5uZfanpxrTR4FXtuk5r98Evpl43QrsVr3s1Kc03YFKW2n6cRDpqiFjJmTdSEAt+1W1DQDbS6+0tAwsK9kUeCW4+6ha56E7SNMdqLSlwDg9NFNG16lmpzCN45VOCryk7Br7j8wfePUfWYPcSC4FxrVnZvsAU4CPAE8DfyPMlDGAMFPG98zsUeByd3+ujPsdTKjZPwZYCXzd3WcVWPZC4GKgL3APcG5mnMOOtmNmRwM3AiOAp4Dx7r6oXMeRSzU7bTU3zWHZUxfBltDtY9Pa18Jr1KWg1tS5PjKz881s2w6W2dbMzq9WnurVOyueKildqktPnabCDOBOYJi7H+PuX3H3S+P/xwDvi+/ndn/oqhuBDcBQ4FTgR2a2X+5CZnYscAlwNLAroYtFckb3gtsxsx2A2cBlwGBgfjwWqaIVT0/eGnRttWVjSJeaUo3Xu3YGXjSzXwGPAf8AVhPuQPcEjgKOA24t1w7LcfcZg0UnPAgwmPBgwER3/3Vcb1egCViT2Ny17j6lXMfRRmuBJ/UKpUv16anTmoozZbT3/gbg7vivLMxsO+AkYH93bwGeMLP7CTVsl+QsfgYw3d0XxnWnADOBS4rYzonAQne/O647CVhpZnu7+9/LdTxJalJrK99k3e2lS/Uo8IrcfWIcnX488EXgAEKfizeBvwK/IgQ0q8q42+Rd44HAg2a2IFPYZSTuPj8KLAHuJdx9XkL4DBcTBnd9BTgeuMvMDsgZd2yQu28qY97b0UD+X/IqT9Mjeemp03Qwsw+7++PtvH+1u3+jjLvcE9js7s8n0haQf2Do/YD7cpYbamZDCM2H7W1nv/gaAHdfY2YvxfSswMvMzgbOBthrr7149tlnO3NcwGgY/WMA1gNr1gCd3lYXfOCBgm91/tg6KU15kSwKvBLcfSUwNf6rqHLdfbr7GmBSYtlfmlkTcBDwckUPopBe28CWPFNe9tqm+nmRNtS5PjXuM7Pj3L1NG3y8CTwJKGfg1R94OyftbUKtfkfLZv4eUMR2+gMritmPu08DpgFMmzatdf/992//CPJ48e4P0rrxrTbpDX0GMfrTfyl5e13xwqzCz2ftcUpTFXMCL8zajUI3wHuc8s+q5qW7uueeezq1nvp41U6hu882/S3IuYMk++4zS3xKak9gYc5bi8zsVTP7WeyDUTn5gq720qWqGvq8t6R0qZhqT1HWAgzMSRtI6FLR0bKZv1cXsZ1S9tNl+YKu9tJ7Ds1Tm1YKvGqnXHefW5lZH0JN2C2JvhQrgUOAkYRasAFxmbzM7Gwzm29m81taWoo8FKknDQ35m3wLpUtl1GCKsueBRjPbI5E2hrY3acS0MTnLLYtdLTraTta6sXZ/9wL7kYop9POun/1aU1Nj7ZTr7hMAM+sF3EboM3ZeJj02Y86PL5eZ2XnAUjMb6O7NuTvKrfov5YDepT5eabZlQ/6agELpUjnufkt8QOZhM/sjobb6iEqM4RX7Ws0GJpvZmYR+pScAH8qz+K3ADDObCSwlTKM2o8jt3At828xOAh4ELgf+WqmO9b222T5vh/Fe22xfid3VkS0lpku1KPRNMLOTq7i7ct19YmYNhKcjhwInufvGtpvYKhMRVTAKUhV3uhW639J9WDWZ2UfjNGWZKco+Qhjba9/Ee2XfLeHJ6OXA7YSnoxea2QgzazGzEQDu/hBwHfAosCj+u6Kj7cR1VxD6p11NeDhpLPC5ChwLADsedHne4VF2POjySu1SpEtU0ma7iVCIVFy57j6jHwH7AB9z96y5YMxsLGE07BeA7YEbgLnuntvMWTaN/YYXGKBzeKV2KSUpFJe3F69LBVR9ijJ3fwNo8+iqu79C6NKQTLseuL6U7STefwTYu0uZLZJGrpd6o8ArW7Xbwgz4KeGucRWJu0/gOWBfd3/F3R8ys8zdZ2YcrysAzGwkcA7hKerXzSyz7XPcfSah4L4G2AloBh4GKlqzl6a500TSSlOUlY9Grpd6osArW28z+wjtBGDu/rty7awcd59xGo728ns7VarFyxg4ahzrVsyn+aU7wqCpDb0ZMOpEFYwiUhEaQFXqiQKvbNsSqv8LBTJlr/rvjpqb5rC6afa7I9W3bmZ102z67niwCkMRwhRlwE2ZuQ8LLLMtoeb6hurlrP5oTkKpNwq8sq1xdwVWXbRqwdSsZkaA1s3rNDK6yLuqPkVZd9XenIQqbySN9FSjlJ1GRk+7QhW6Gu6jWtx9IvBBwkMvXwR+DTxLmJrsC4SpdT7o7pfWLJN1QnMSpt+yeZfywu2jeWHWKF64fTTL5vXsy1o1Xtn0y1MGDX3eW2AKD42Mng7pGe6jJ/fNqeYUZSK1smzepTS/mBizu3Xz1tdDD72qRrmqLdV4Jbh7vlHjpUStWzaUlC49U3PTHJbPmxiHHmll09rXWD5vIs1Nc2qdNakjDX0GlZReUQ19SkvvAbKCriLSewLVeCWY2eN0cNvv7kdUKTv1a/Pa0tKlR1JfQCmHnQ6+gmV/+CrZI7L3YqeDryi0SuW0biotXXokBV7Zbk783QDcSBhrS0TKTH0BpWx69YYtW7Jf10R6mvElvRR4Jbj7LcnXZnZ9bpp0THOnSTEa+w0rMMPBsBrkRurVqgVT8z7VqJrTtNDcvbnUx0vKTnOnSTGGjJlAQ+++WWma4SAwsw+Y2d21zkc9UM1pug0cfUpJ6T2Baryk7DR3mhSjp18nZtYP+DphntYXgEnADsB3gI8Dqm0vgmpO063vjgfT/OLt5PbB67vjwbXKUs0p8Eows4/mJDXmTiFUzimDujPNnSbF6OHXyY2Esbx+Qxgs9QDCxNK3AGfF4SakA5obNt1WLZhKdtAFsKVHNwUr8Mo2Pef1KsIk1hmaMqhIPXl8JpEiHQsc6O7LzewHwCvAke7+eI3zVVc0N2y65auNbC+9J1DgleDuo2qdh+4gMz5T5g40Mz4TaO40kYT+7r4cwN1fNbMWBV2l09ywaafO9bkUeCWY2c7u/nqt81HvND6TSFHadGVQ14bSqbxJOw2xkUuBV7bngYGZF2Y2291PrGF+6pKeMko73YGmxDKyuzKoa0MnqLyReqPAK1vuL89RtchEvdNTRinXu2/+WQRyhnaQipvo7rfXOhP1TuWN1BuN45Wt59Z9llG/YUeVlC5VltMs02G6VMpNtc5Ad6DyRuqNaryy5fa50HASnbB2ydyS0qW6Gvq8l9aNb+VNl6pS224ZqLyRUqThiXsFXtmWoz4XXaY+F+nWumVDSelSMb1zb+xy6UavYxquQIqVlifuFXgluPuu1dyfmQ0mjB12DLAS+Lq7zyqw7IXAxUBf4B7gXHdfX8x2zOxowmCNI4CngPHuvqhSx6U+FymXr39Xe+lSKdsSvreFAi/d6BWjofe7Q0nkposkpOUJWAVetXUjsAEYSpg25EEzW+DuC5MLmdmxwCXAR4ElwL3AlTGt3e2Y2Q7AbOBM4AFgCnAncFilDkojSYsUZY27K7DqqnxBV3vp0mOlpTVGnesTzOyGnNdfzHl9Txn3tR1wEnCZu7e4+xPA/cDpeRY/A5ju7gvd/U1C8DS+yO2cCCx09ybcmbIAACAASURBVLvd/R3CfHBjzGzvch1LroGjxrHTodfQ2G840EBjv+HsdOg1GlMnJXpts31J6SJpFsqZ4tOl5yrU6lLt1hjVeGUbD5yfeP1tsqcR+ngZ97UnsNndn0+kLQCOzLPsfsB9OcsNNbMhhObD9razX3wNgLuvMbOXYvrfc3dkZmcDZwPstddePPvss6UeVzQaRv8YgPXAmjVAp7fVBR94oOBbnT+2Os4HwN63Fnyr6nnp2dS5vgxUwy7FSsu1osArW25BWMmCsT/wdk7a28CAIpbN/D2giO30B1YUuR/cfRowDWDatGmt+++/f+EjqAMvzCo8C9QepzT1uHxkpOHJnnK7556yVUhXhbsPADCzfYEPA4OBN4AncrsbSGGZ67a7Xc9Sfmm5VhR4Zcsdx6uS43q1kBglPxoIrC5i2czfq4vYTin7KZvu+MPenQwcNU6fRwqY2U8JXQleJfTfHA4MM7PbgC+4u8YWLIKuZylWGq4VBV7ZOhrHq5yPyTwft7+Hu78Q08YA+e50F8b37kost8zdV5nZOx1sZyGhYAe29gnbvcB+yiItj+yKpJmZnUOYHeMwd/9TIv0Q4HbgHODHtcldfdGNntQTBV7ZOhrHa3m5dhT7Ws0GJpvZmYSnEU8APpRn8VuBGWY2E1gKXArMKHI79wLfNrOTgAeBy4G/unub/l3lkpZHdkVS7nTg/GTQBeDufzKzC4Cvo8CrQ81Nc1j2x4ugdSMQbvSW/fEiQDd6kk4KvBKqPY4XYITAbjkhyDs3DgExAngO2NfdX3H3h8zsOuBR3h3H64qOtgPg7iti0PVD4OeEcbw+V8mDSssjuyIpty/wWIH3HgNuq2Je6tby+VduDbq2at3I8vlXKvCSVFLgVSQz+wBhyIZPl2ub7v4G0KZkcPdXCJ3ik2nXA9eXsp3E+48AFRs+IpcGUBUpSm93z9vX0t1Xm1nZhvspZbDmuHynBmw2s12BJmBNYnPXuvuUch1LrnzTX7WXLtXWQP7u0j33oV4FXglm1o9QvX8g8AJhzKsdgO8QhpK4pWaZqyP9hh1F84sz86aLyFZ9OpgyqJzlc1GDNUPXBmxObGaQu28qY/6lbhV6PqTnPjeiwCvbjcAHgd8AxwEHEGqKbgHOcveVNcxb3dCktSJFye1Tmu/9LksMsry/u7cAT5hZZpDlS/KssnXA5rj+FGAmcEkntlUFqlFJs17bbM+WDW/mTe+pFHhlOxY40N2Xm9kPgFeAI9398Rrnq66oj5dIx6rYp7SUwZqhawM2Zywys1bgYeBrhW5akwM2jx49usjDyaUalTTbsumdktJ7Ak0ZlK2/uy8HcPdXgRYFXaVLy7QM6VLoq6avoFRcKYM151u+lAGbVwKHACOBg2J6234HkbtPc/eD3f3g/v37F1qsXQ19BpWULlW2ZV1p6T2Aaryy5Y7bRe5rd/9dLTJWTxr7j8zfub7/yBrkJi22lJguUhwzm0vh2qsngS9T2iDKnR6wOTY/zo/py8zsPGCpmQ109+b2j6RzGhoa8jc0NqipUdJJgVe2ZbQ/jlcrsFtVc1SH3lnxVEnpPUFjv+EFnvTURL7SNe5+VHvvx35ZxQ7WDF0bsDlXJiaqWBS0ZUP+pxcLpUu19SL/DWbPre1X4JVtorvfXutM1L3WzaWl9wBpmZxV6oeZ9XL3LleJljhYM3RhwGYzGwu8RXgqfHvgBmCuu+c2T5aNhq9JO9X25+q5IWd+N9U6A91CQ4GZlQql9wADR41jp0OviTVcDTT2G85Oh16jAR6lDTM7wMymAovLuVnCmFzLCdMRnZt4anGEmbXEgZtx94eAzIDNi+K/3AGb826L0CLwEKHp8VlgPXByGY+jjSFjJtDQu29Wmm5q0qPQ04t6qlEy1CmgDN6z41jeWf77vOk9WRomZ5V0MrMdgVOA8YS5VH8N7Fyu7bc3yHI5B2yOLQZVbTUYOGoc61bMp/mlO0KtekNvBow6Ud+1lGhtzf90aaH0nkCBV7beHQxoqM71RdjUsqik9Ipq6J2/ibMH175JOphZH+BThGDrI8D/EGqa7iO0w/xHzTJXR5qb5tD8z7ve/Z63bqb5n3fRd8eDFXylQOvG/K3MhdJ7AgVe2bYlTIVRKPBS5/oipGkcL9W+SYq9DvyN0H/q8+6+dZRJM9u2VpmqNyuengxbcuZq3LKRFU9PVuCVAuqD15YCr2xr3F2BVRel6YuWqto3kWz/R5gdYyzwkpnNdfee2/7SSflGRW8vXapLDxa1pc71UnZp6uyaLwBsL12kWuIwEP9CmCFjGvCqmV1vZgfXNGMiZaQHi9pSjVc2da4vg8wXatWCqWxau4TGfsMYMmZCj/6iieTj7ouAKcAUMzsc+DzwCGEMQSlCQ59BtG5sO2aXRq5PDz1YlE2BV4K7F5pCQ0qkL5pIadz9CcKk0+cD/04IwqQDOx18Bcv+eBG0Jvp5NfRhp4OvKLxST9BrW9iyPn+61JQCL6mI5qY56ajx0lONUmfc/R3CkAwazLkIqmHPb+Bu/0Hzi22nyRy4mx6WrTX18ZKya26aw/J5E2M/qlY2rX2N5fMm0tw0p+p5KfT0op5qFJHubO2SuSWlS/Uo8CqBmel8FWHVgqlZT7AAtG5ex6oFU6ueFz3VKNK9pelGL03SNKyPZFNTYxHM7ADgDMLUF2WZ1djMBhPGDDsGWAl83d1ntbP8hcDFhKk67iFM07E+jvfjwMeAwcCLhDknfx3X2xVoAtYkNnetu08px3Hkk6YvvJ5qFOne2rvR68nNjb22GZR3SI1e2+ihg1pT4FVApafwAG4ENgBDCRPNPmhmCxJzniXzcixwCfBRYAlwL3BlTGskzOl2JOGx9OOBu8zsAHd/ObGZQe6+qYz5LyhN43ipj5dI95amG7002bLpnZLSpXoUeCVUawoPM9sOOAnY391bCE8y3Q+cTgimcp0BTE9MajsFmAlc4u5rgEmJZX9pZk3AQcDL5chvqVI1YF6+oKu9dBGpK6m60UuTLetKS5eqUeCVrVpTeOwJbHb35xNpCwi1VvnsRwj+kssONbMh7p413o+ZDY3bz605W2RmrcDDwNfcfWW+HZnZ2cDZAKNHjy7ycLKl6Smjxn7DCxTKZWkxFpEaGzJmQt7hJGpxo9drm+0LNO9tX/W8SHop8MpWrSk8+gO5M4S+DRQaRyx3+czfA0gMtBhr7GYCt7j732PySuAQ4BlgCKGJcyZwbL4dufs0wijaTJs2rdPHnpZxvFJV+yYildFAmEk3+boGWlvzF5mF0iupobEfrZvW5k2X2lLgleDuR5nZSMLAhdOAfmZ2JzCLEJQVxczmUrj26kngy8DAnPSBwOoC67TkLJ/5e+vy8YnL2wj9xs7LpMemzPnx5TIzOw9YamYD3b25w4Opc2mqfROR8lu1YGreSbJr0bk+3wj67aVXVMM2QNvAK6RLLSnwylGOKTziHGwFxT5ejWa2h7u/EJPH0LZ5MGNhfP+uxLLLMs2MZtZAeEJyKHC8u2/Mu5Ugc+vVY6ZHSkvtm4iUnzrX59e6MbdRpf10qR4FXu2o1BQe7r7GzGYDk83sTMJTjScAHyqwyq3ADDObCSwFLiX0Q8v4EbAP8DF3z+o5aWZjgbeAF4DtgRuAue6ub5+I1D11rs9Pw0mklwKvIlRoCg8DfgosJ9SmnZt4anEE8Bywr7u/4u4Pmdl1wKO8O47XFXHZkcA5wHrgdTPLbP8cd58J7AZcA+wENBM6159cxuMQEakZ9ePML039zSSbAq8acfc3gLztX+7+CqFDfTLteuD6PMsuop1mQ3fXnG8i0m2lqh9nQ5/spyuT6VWmpsb0UuAlFZGaSbJFpNtLSz/Ohsbt8nakb2jcrup5URNsemnuQSk7zZ0mIj1Rmp5qHDJmAg29+2alqQk2HRR4SdmlaZJsEZGqKTQVWQ2mKBs4ahw7HXpNHCy6gcZ+w9np0GtSUTPY06mpUcpOj3eLSI+UsinK0tIEK9lU4yVlV6gPgfoWiEh3VmgqMk1RJkkKvKTs1LdARHqixv4jS0qXnklNjVJ2qXq8W0SkSt5Z8VRJ6dIzKfCSilDfAhHpcVLWx0vSSU2NIiIi5ZCipxolvRR4iYiIlMHA3T9XUrr0TGpqFBERKYOhh14FQPNLd4TmxYbeDNz9c1vTRUCBl4hIt2dmg4HpwDHASuDr7j6rneUvBC4G+gL3AOe6+/r43nnAeOAA4HZ3H5+z7tHAjcAI4ClgfJxTtkcYeuhVCrSkXWpqFBHp/m4ENgBDgVOBH5nZfvkWNLNjgUuAo4Fdgd2AKxOLLAGuAn6aZ90dgNnAZcBgYD5wZ7kOQqQ7UOAlItKNmdl2wEnAZe7e4u5PAPcDpxdY5QxgursvdPc3gSmEGi4A3H22u88BVuVZ90Rgobvf7e7vAJOAMWa2d9kOSKTOKfASEene9gQ2u/vzibQFQN4ar5i+IGfZoWY2pIh9Za3r7muAlwrty8zONrP5Zja/paWliM3n19w0h6Y5h/PCrN1omnM4zU1zOr0tkUpTHy8Rke6tP/B2TtrbwIAil8/8PYD8tVy5664odl/uPg2YBjBt2rTWDradV3PTHJY9dRFs2QjAprWvhdegsQQllRR4iYjUMTObCxxZ4O0ngS8DA3PSBwKrC6zTkrN85u9Cy7e3bkf76rIVT0/eGnRttWUjK56erMBLUkmBl4hIHXP3o9p7P/bxajSzPdz9hZg8BlhYYJWF8f27Essuc/eOarsy656Rs+/d29lXl23Z8GZJ6ZXW3DRH06VJuxR41UiZH++eCxwGbIqLv+bueyXW7dGPd4v0ZO6+xsxmA5PN7EzgQOAE4EMFVrkVmGFmM4GlwKXAjMybZtZI+O3oDfQ2s/cAm9x9E3Av8G0zOwl4ELgc+Ku7/70iB5cyzU1zWD5vIq2b1wGh2XP5vImAmj3lXepcXzvlfLwb4Dx37x//JYMuPd4tIka4aVsO3E64cVsIYGYjzKzFzEYAuPtDwHXAo8Ci+O+KxLYuBdYRyqTT4t+XxnVXEJ6gvBp4ExgLVHTY9oY+g0pKr6RVC6ZuDboyWjevY9WCqVXPi6SXarxqIPF49/7u3gI8YWaZx7svybPK1se74/pTgJkFls219fHuuO4kYKWZ7d1T7kJFejp3fwPIW+Xi7q8QOsUn064Hri+w/CTCMBGF9vUIULXhIwaM/FeaX5yZN73aNq1dUlK69Eyq8aqNSjze/U0zW2lmT5rZUYXW1ePdItKdrF0yt6T0SmrsN6ykdOmZFHjVRjkf74bQ92s3YDjh0ewHzGz3zuzL3ae5+8HufnD//v3zLdKhTD+HTWtfA1q39nNQ8CUi5RbKmeLTK2nImAk09O6bldbQuy9Dxkyoel4kvdTUWAHVfrzb3Z9KvHeLmZ0MHA/8IM+6He2ry9rr56AOpiJSVg29w4TU+dKrLFO+6alGaY8CrwpIwePdrUBDYt2qPt6tfg4iUjX5gq720its4KhxCrSkXQq8aqCcj3eb2SDCk0OPEYaT+CxwBHBBXLfqj3c39huWt5pf/RxEpNwa+w0vUN4Mr0FuRDqmPl61U67Hu/sAVxGm6VhJaMYc5+7/iOtW/fFu9XMQkWpReSP1RjVeNVKux7tjYHVIB/uq6uPd6ucgItWi8kbqjQIvqQj1cxCRalF5I/VETY0iIiIiVaIaLylo6dKlK6+88souzem4efPmHXr37r2yXHnqirTkJS35gPTkpUz5GFmWzEhNqLypnLTkJS35gBqXOa2trfqnfxX7d+65586vdR7Slpe05CNNeUlLPvSvvv+l6TpSXtKbj1rnRU2NIiIiIlWiwEtERESkShR4SaVNq3UGEtKSl7TkA9KTl7TkQ+pbmq4j5aWttOQDapiXhtbW1lrtW0RERKRHUY2XiIiISJUo8BIRERGpEgVeIiIiIlWiAVSlIszsPGA8cABwu7uPr1E+tgUc+BgwGHgRmOjuv65BXn4OHA1sB7wOXOfuN1c7H4n87AH8H/ALdz+tRnmYCxwGbIpJr7n7XrXIi9QvlTcF86Myp20e5lLjMkc1XlIpS4CrgJ/WOB+NwGLgSOC9wGXAXWa2aw3y8k1gV3cfCHwKuMrMDqpBPjJuBP5Uw/1nnOfu/eM/BV3SGSpv8lOZk19NyxzVeElFuPtsADM7GNilhvlYA0xKJP3SzJqAg4CXq5yXhYmXrfHf7sDT1cwHgJl9DngL+D0wutr7FyknlTcF86MyJ4UUeEmPYmZDgT2BhR0tW6H9O6FJpC/wF+BXNcjDQGAyoQnii9Xefx7fNLNvAf8AvuHuc2ucH5GyqHV5E/OgMqetmpY5amqUHsPM+gAzgVvc/e+1yIO7GzAA+DAwG1hfg2xMAaa7++Ia7DvXxcBuwHDCgIYPmNnutc2SSNelobwBlTl51LzMUY2X9Ahm1gu4DdgAnFfLvLj7ZuAJMzsNOBe4oVr7NrMDCR1/P1itfbbH3Z9KvLzFzE4Gjgd+UKMsiXRZmsobUJmTlIYyRzVe0u2ZWQMwHRgKnOTuG2ucpYxGQn+LajoK2BV4xcxeByYAJ5nZn6ucj0JagYZaZ0Kks1Jc3oDKnHyqXuaoxksqwswaCddXb6C3mb0H2OTum9pfsyJ+BOwDfMzd19Vg/5jZTsBHgV8C6wh3gCcDp1Q5K9OAOxKvJxAKxXOrnA/MbBAwFniM8Gj3Z4EjgAuqnRepbypv2lKZ01ZayhwFXlIplwJXJF6fBlxJ9hM/FWdmI4FzCP0aXjezzFvnuPvMKmallVDQ/JhQ07wIuMDd76tiHnD3tcDazGszawHecfcV1cxH1IcwBMDewGbg78A4d/9HDfIi9U3lTVsqc9pKRZmjSbJFREREqkR9vERERESqRIGXiIiISJUo8BIRERGpEgVeIiIiIlWiwEtERESkShR4iYiIiFSJxvGSqjKzGcCr7n5pDfbdAPwUGAe84O6HVjsPXWVmLwNnuvsjed47Cvi5u+9S7Xx1hZnNJeT75lrnRboflTldozKn/BR49XDxS9UX2M3d18S0M4HT3P2oGmatEg4HPg7skjlWEakulTnS06mpUSAE4F+pdSZKZWa9S1xlJPCyCsDuycwa4uTEkn4qc6TudbbMUY2XAHwbuMjM3N3fSr5hZrsCTUCfzLxnyWpaMxsPnAXMA/4TeIMwXceewBRgW+Br7n5LYrM7mNnDwGHAn4HPu/uiuO29CbPEHwSsAC5z97viezMIc46NBI4ETgCyqr/NbBhhiozDY16udfefmNkXgRuBPnHKiu+4e3KKkcz6XwC+Buwcj+nsRN4yU3B8FdgBmAWc5+6tZjaaMDHugcBG4H/c/bNFHtNaYBTwYWABcBJwCXAGsAw42d3/ksjmIWZ2A/A+YA5wrru/k+dYhsX9HgG0AN919xtyl+tMPtrbtplNAvYjTJtyAvBy3NZJwIUx/Yvu/ttEFnY3s3nAXsBc4D/d/Y24vcOA64F9CdOefMXd58b35gJPEibi/RfgADM7HLgc2BFYCVxag+lapH0qc95dX2VODytzdHcoAPMJF96ETq4/FvgrMIRQMNwBHAKMJhSIPzSz/onlTyUUkDsAzwAzAcxsO+DhuI2dCBO6upntl1j3FOBqYADwRJ683A68CgwD/gO4xsyOdvfpwJeAP7h7/wIF4DhgInAi4Qv0eNxe0r/FYxsDfAY4NqZPAX4LbA/sQiggij2mzxDmmtuBUED8gfDjsAPwC0IBkHRq3O/uhB+bNn1X4l3YA4TCbDhwNHCBmR2bu2yp+Shy258Ebovn4y/AbwjlzXBgMnBTzr4/D3yB8LltAjIF6nDgQcL8aoMJ1+g9ZrZjYt3TgbMJ18SKuO5x7j4A+BDhGpN0UZmDypxi89HdyhzVeEnG5cCTZvb9Tqzb5O4/AzCzO4FvAJPdfT3wWzPbQCgQMxfjg+7+v3H5bwBvm9n7CRfsy5ltAX82s3sIhdnCmHafuz8Z/86644rbOBz4t3g39oyZ3Uz4kvxPEcdxDvBNd/9b3N41wEQzG5m5AwW+Fe/Q3zKzRwl3mw8R7jhHAsPc/VXeLaD/rYhjutfdn477vBcwd781cT7Py8nnD919cXz/akKBm1sQHgLs6O6T4+t/mtlPgM8RCqR8is1HMdt+3N1/E9e9m/DD8i1332xmdwDTzGxQorbjNnd/Ni5/GeGzO4PwI/ord/9VXO5hM5sPHA9kajRmuPvCuO4mYAuwv5m94u5LgaUFjldqS2WOypweWeYo8BIA3P1ZM/sloZr3byWuvizx97q4vdy05N3n4sR+W8zsDcJdx0hgrJklmx4aCXcxbdbNYxjwhruvTqQtAg4u5iDi/r9vZt9JpDUQ7pgyheDriffW8u5xXUS4A51nZm8SmhV+SnHHlHuu2jt3kH0OFhGOO9+xDMvZb2/CHXUhxeajmG3nrrvS3TcnXhO3l9lG7jH1Idz1jgQ+bWafTLzfB3g08Tp5Pa0xs88S7lKnm9mTwFfd/e9tD1dqSWUOoDKnR5Y5Crwk6QpCNW+yEMh0Cu0HNMe/d+7ift6f+SM2BwwGlhAu5sfc/ePtrNvazntLgMFmNiBREI4AXisyX4uBqzvTH8jdXyf0OyG29z9iZv9LccdUqvcn/h5BOO5ciwm1AnuUcb+V3HbuMW0k9JVYTLgzPauddbOuiXjX+xsz60toLvgJoQ+JpI/KHJU5xehWZY4CL9nK3V+M1bvnA/8X01aY2WvAaWZ2E6HT4+5d3NXxsaCYR7hje8rdF8e732+Z2emEPhsQqtVbMlXxHeR/sZn9HvimmU0g9EX4IqHquBg/BqaY2TPuvtDM3gsc4+53d7SimX2a0JfjVeBNwhdzM9ClYyrgv+K5WkvoH3JnnmXmAc1mdjGh/8EGYB+gr7v/qZP7reS2TzOzWwmdYicDv4hNBD8H/hT7cjxCuPM8DHgxnussZjaU0P/nfwh3uS2Ez0FSSGWOypwidasyR53rJddkYLuctLMIT92sIjw58vsu7mMW4U73DcJTN6cCxDvGYwjt9ksIVezXEp5SKtbJwK5x/XuBK9z94WJWdPd74/7uMLNm4FnguCL3ewjwlIWnl+4nPAXTVKZjyjWL0Kn2n/HfVXmOZTOhs+mBhCfEVgI3A+/twn4rue3bgBmE8/Mewg8xsV/JCYTCfgXhbvRrFC67ehGeAFtCuL6OBKwL+ZLKU5mjMqdd3a3MaWhtba8WVURERETKRTVeIiIiIlWiwEtERESkShR4iYiIiFSJAi8RERGRKlHgJSIiIlIlCrxEREREqkSBl4iIiEiVKPASERERqRIFXiIiIiJVosBLREREpEoUeImIiIhUiQIvERERkSpR4CUiIiJSJY3l2pCZ/Rh4zd2nlHPZemZm5wKTgO2Ake6+qrY5EkknM1sI/Je7z+3Euq3AHu7+YjvLtAAfcPd/dj6X6WZmfYG7gCOA37r7p2ucJZFUMrMPAze7+16dWHc8cKa7H97Z/XcYeJnZy8AwYJi7r0ykPwOMAUa5+8vu/qVid1rKsvXKzPoA1wOHufuCKu53LvBzd7+5WvuslHJc4D2BmU0CRrv7aWncXhH7e5lQxjybk55VxnRlH+7evyvr14n/AIYCQ9x9UzV2aGa7Ak1An2rts5K6U/lZSfE7e6a7P5LG7XXE3R8HSg66yqXYpsYm4OTMCzM7AOhbkRx1A2bWSCgA3wMsrHF2Uimeo7rfR2ekNV+1kDgXKmO6wMx6AyOB57tDAFQJPbzM6V3rPKRFGj6jhtbW1nYXiJHozcAJ7n5ITJsKvAlcRbwbNbMZwKvufqmZHQX8HPgucDGwGZjo7j+L6+db9gZgQlz2XGAD8D1gB2Cqu1+Tu258fRThDmWXRH5vBE4HdgfuACYCM4DDgaeAT7v7m3mOdYfEclsIQdOR7r4ltzmjwDH8ALgQeBT4FNAPWAPMc/ePmtn3gROB9wIvABfEyDvzxbgY+CKwE/A8MM7dF5vZ3nHbBwErgMvc/a48+b8auATYCGwCZrj7ee2tH49jLTAK+DCwADgpbucMYBlwsrv/JXF+b4rn933AHOBcd38nvv9vhOtiV+A54Evu/tfEuj8CTiXcbWxH+MzPise8GPiGu99rZvsAfwH6AOuATe4+KPeONLdWLH5O5wEXAI3uPqq9POU5hx8Cvg/sGT+Dr7j7783sc8AEdz84seyFwEfc/VNmti1wNfAZYFvgXuBCd1+X5/p42N1Pz9lvL8J1ehYh4HgI+LK7v517jSfO5ZmEWuv7gQZgPfCSu4+J5+kPwNHxXM8F/tPd3+jM9vKcp5cp33WwlPCdWO7ue8Vz+XtgD2AA8DNCmfCxuM19CNfFWuArwPT4/wXAge7ebGbHxfUOcPcVye9vJ675Yr77RZVfec7j8cBU4P1AM/Bdd5+ar7Y3zzGsIwRbRwLPAIckPrevED7znxBqDVuB3xCac9+K23s/4Vr/MOEm/HZ3Py++9wXga8DOwDzgbHdflCf/r8S8r4lJH3f3P7S3fjyO/yJ8F3aO52lGPI/7Ea7909x9Q+L8OvDfQAuhjJgZt1XS9w44H7gNGEu41p8kXJuv5is/42eTVaOXLIPi53RWPMYzYj6nFMpTnvPX3vf+IeCX7v7DxPILgCvdfXYR5Xry+jghtzbJzIYBPyb83r0BXOvuP0msn/d31sxuI3x31xOu98mEJu4m4BxC95oGwnX/nc5sz92vy8lr5rMs13Uwney4YR9CmXQg8BrwdXe/P743hFCWHAX8nfA9+khXWmKKrfH6IzDQzPaJAcJn44G0Z2dCYTqcEEzcaGbbt7Pse+KylxMKi9MIF9SHgcvNbLci8wqhEP044cfzk8CvCRf3DoRjPr/Ael8FXgV2JNRYTSQUWMXYGRhMuNC/QChAAAa5+0fj338ifLCDgVnA3Wb2nvjefxPu+I8HBsZtrDWz7QgXyixCcHIy4GaWZiPBUwAAIABJREFU2f5W7v4N4HHgPHfvH4OuYtb/DHAp4fysJ/xg/zm+/gWhyTTpVOBYQmC7Z1wXM/sX4KeEL98Qwg/z/fFLkXEy8K/xvGwCXiJ8xu8FrgR+bmbvc/e/AV8C/hCPZVCbM17YOELBum+ReSLmfzDwIOFHdEg87gfjF+9+YC8z2yOxyimE8wpwbTwXBwKjefdazkheH2fnyfP4+O8jwG5Af+CHeZbL4u4PAdcAd8bzlAySPk+4joYRfkhu6OL2cpXlOoiv1wP9YgF4KbA/oaCDEDhcSvhxvwl4G/g0IXgaH5f5FeG6vSF+XtMJgcuKAnkv9ZpvT1fKr+nAOe4+IB7z70rY7ymEH5sBhAA7+blNJ/z4fZPw+e9DCJAmwdYbvV8CiwjB8XDCTSpmNo5Q9p1IKAsfB24vkIcj4v+D4n7/UOT6nyCcn8OAi4BphOvp/fE8nJxYdmfC5zKcENxMM7NMM1Gp37tehB/RkcAIQnDyQ8hffhY45lxjgX8Syteri8hT0ngKf+9nkV0LvG/M94NFluvJ6+OJPPu+nfB7N4zQTH2NmR3d0cHGm8ZXgE/G85QMkj5CuGE6BrjEzD7Wxe0llfM62MpCt6AHgN8SzuWXgZmJbd8IvEO4wfxC/NclpVS53UYoyB8jRH2vdbD8RkLkugn4lYXOrXsRgrh8y17t7pvN7A7Cl/D77r4aWGih4+0HCBd3MX7g7ssAzOxxwp105u71XkIhVSjP7yN0hH+R8CUs1hbgCndfH/fTZgF3Twar3zGzSwnnZAGhtuGi/9/encfJUZX7H/9MZgImJCMSQvglmhBWhWBQELj+onIV8cpFyU9UdsGLgnlEr17CFoGYBAER48qjRqIBTERkGRQQBSV4QSWCEk2UfbKYkIWwDJNhSSb9++NUJzU93TPVM93V1Znv+/Wa12vm1PZ0T/Xpp06dOsfdH4uWL472czywLN9aCPzFzG4mfFCS3MY8JsH2t7r7w9HxbgXM3a+L/v4ZoQUp7rvuvjJa/hXClcRFhKu2H7j7g9F615rZNELlel9U9u38ttF78vPYfn9mZhcChwK3JXhtpVzu7s9F8SWJKe8/gSfc/fro75+a2ecJFcI8M7uNUMHNjBKwNxMSiobotb81dtzLCJXihdG+upwfRZwMzPao83f0Piwxs0/243243t2XRPu7GHjEzE7rx/4KVeQ8iH1W7iHUMacRzs18q+S3CHXJxWb2CeDH0RX/uwkJ+7ui9T4bbbMQ+KW7395D7OWe8z3pT/21iXCBsNhDK3y3lvge3ObuD0S/v1JY50R1WP6Bg/VmNhuYHv19KOEL91zfdmsy/+V8FuEz9E/Yei5PM7NxxVq9ikiy/VfdvY3w/iwhPAyQP/d/BbwNuDa2z4ujz859ZnYH8HEzu5TyP3cvAzfndxqdt/cmeE09We3u34n215kgpriePve3At+LvW8nA7e4+6tRcttbvd7l/IgfNGrtnAQcE7VSP2Jm1xBasH/bj/dihrtvBP5uZj8m1JeV7LdVkfOg4LNyOCHhvcLdtwC/M7PbgRPNbBahIefA6HUtMbNr2XbB0SflJl6/JzTPX5dg/Q3eta9BB+HFlVq3M/o93xy7Nrb85R62LaZw26T7+hrhivA30T9mjrtfkfCY66MTuCQzO4eQYI0mtKQ1EzJ4CFd6TxXZbBxwmJm9ECtrIvw/kkiyfbnv18rY78sJryd/rNPM7HOx5TvElhduS/RF+j+Eq26iY+1K/8SPkSSmvNGE1xO3nHD1BOGD/HVC0/pJQIu7d5jZboTbyg/HPtANQLxfRW/nR+GxlxP+T6N62KY3hf+nwfT/ve1p/30+DyJ3E1podqNr69wqYLSZHQYcC+xg4YnhHYl9ibr7C2b2c8L5dFwvsfe1jiimP/XXcYRk9Qoz+xtwgbv/MeFxi72HW0Xn5bcJielwQmtPPrF7E7Dci/cHGwd8y8y+HitrIHwOkiReSbbv7f3fPfb389GXXl7+XBtJmZ87MxtK6ALzH0D+DsxwM2uM/Q/LFf8/JIkpruTn3t1XRcnFCYQWnRPY1lqTpF7v6fwYDTwXXRzEj31IifWTKqwTDuzn/uIqdh4UGA2sjJKu+L7HRPtuovvr6pfEiZe7LzezVsKtsDP6e+B+2Eh4k/N2L7ViuaKT8BzgnKjJ9l4z+7O7/5aQOBYe91+xv3u8JWnh8dXzCa1tSz30G3uecIJA+MfuRcGTXVH5fe7+/oQvozCOcrdP4k2x38cCq2PH+oq7fyVJfGY2jnBb5n2EW4qdFp5kayhcNybJ/z++XZKY8lYTKrS4sYR+FxCaonc1s4MIV3JfjMqfJXxZHODupVqCe7tlXXjssYTbg2sJFcPW1xzdJhqZYN+F/6dNUaxd3sMy9tfb/vt0HsSsI/QTeRPwWKx8dLTvBYQWnN+7+7lm9k3ChWD+dRxEuA3wU0LC8R8JX0dvevvs95m7/xk4NrrdcTahr0y+z1T8f9TbeV7M5dE6b3X3DVErSf421kpgrJk1FUm+8v+/+QleQrEYytk+iTeY2U6xL92xhHqyL5+7cwh3GQ5z9zXROfNXStc5+WMOJfTBg+51TnybJDHF9fS5h3AuTzez3xP6gOVb55LU6z2dH6uBXcxseCz5Gsu2O1m91bM91TmPxvaXrxP6ur+4Sp4HcauBN5nZoFjyNZbQx3c94f9R+Lr6pdze/WcAb3D3jVa7JwMeISRGlxKuor9QqR1b6BD8KKHlqY3Q0S9/FfQIcFJ02+D9hA6LD5Wx++GEf+B6oMnMLiC0eOVdA8wys38QvlwOJHwIbidcDZ9K1AeDcB+7Pd+UX2Atoa9AXrnbJ/HZqCm2g9CX42dR+Q+BW83sHkJn06GEfjq/L7iyytuJ8IFYDxA1r08oeC1vNLMd3P21qOwR4CNRs/howjkZv1ouVE5MdwLfMbOTCF+AxwH7E95D3H2zmd1EaBndhdBCQ5RE/xD4hpmd7e7rzGwMMMHdf91DbHE/Bc6PbrOsZ1t/nc1m9jjwOjP7T0LyN43Q2hN/n95fUHEAnGJm1wHLCK10N0XJbV/3V6hS50HcGdG+ziX0u4LQJ/MnhKv9W4FTzewhQqtje7TODtE60wj9yx4yM3N37+V4SfT3s1+Ume1A6Kt2u4fO1Pk6B0JXgwOixOBRor5ZZRpO6A/3QnQ+nhtbtojwUMMVZjY9Ou7B0a2p7xPqokfcfamZvR44qqBbQN56wm2cPQlfVJS5fVIzotvVhxG6T0zv4+duOOFL+gULfTqnFyzvUn96eDBjFeGz9APCbfC9SgXZh5hKfu6j5XcSzueZUXn+89ivet3DQ1t/AC43s6mE/lFnEPomQu/fs4XfM3kXW+jeMR74ZAX2V6hS50Hcg4TE8Lyolfb/EvqGvyOqL28BvmzhgZE9COfAsgT7Lamskevd/Sl373eF00/XEyqlZYQvjZ/1uHZ59iHcj24nVPru2wZ0/G/CP+MFwr32ljL3/WtCJ//HCU2Vr9C1+XI24cv+N4Skby4wJPqiOorQzLwaWENodu7WOTzyLeCjZva8mX27D9snsSCK8+no51KA6Nz4NOGq+nlCAnl6qZ24+z8It+7+SPjgHUh4yijvd4T+CmvMLD+G3DcIT4ytJfQB6fGqupyYPAxwewzhqngDodPvMR4bvy567UcCPy9oKTg/2vefoi/QeyhvnJgfse12fivh/PhcFNeLgBGS81WESiLe4pL/QttgZn+JlV9PeDJrDaHz9+f7ub9CFTkP4tz9KUJfrYcILREQrmovjWL+GOEW0bWExD2fdE8lPDX1vagfxynApdb1YYi+6u9nvyenAsuic+YzRF9U7v444cv2HsIT0MU6R/dmBvB2QvJ1B3BLfkF0W+1DhI7IKwj//+OjZbcS6ogboriWAB8sdgB37yB04H7AzF4ws8PL2T6hNYTzaDXh8/4Zd8+3PpT7ufsmoeXoWUJ/47sKlnepP6OyTxOS1g2Eh6b+0Eu85cRU8nMPEJ3LtxDqnAWx8krU6ycSEonVhAua6e5+d7Sst+/Zy4GLov/51Fj5fYTX/lvCU42/6ef+4ip5HmwVXdR/mHCOPkt4cvITsX2fTegqsIZQn/64yG7K0utwEiJxlvJAd9I3VuWBIHUeSBqsyNAnkj1W5YF0t7fzQHM1ioiIiKREiZeIiIhISnSrUURERCQlavESERERSUnNJ4uU7LryyitzO+9czkw9IrXzzDPPPDt9+vSRva8pWaT6RupNX+scJV5S0s4778yZZxabVlAke2bMmNHvEaWldlTfSL3pa52jW40iIiIiKVHiJSIiIpIS3WqUqli76CLanroBcp3Q0EjzXicw6tBLax2WiGyH2lpb2LD4KjZ3rKZp6GhGTJxK8/jJtQ5LpCglXlJxaxddRNuTsZl8cp1b/1byJSKV1NbawrpF08h1vgzA5o5VrFs0DUDJl2SSbjVKxbU9dUNZ5SIifbVh8VVbk668XOfLbFh8VY0iEumZEi+pvFxneeUiIn20uWN1WeUitabESyqvobG8chGRPmoY/PqyykVqTYmXVNzrRh5WVrmISF81NDSUVV5tba0ttLZM4okFe9LaMom21paaxCHZpc71UnGb24uPKVeqXESkr7a89kJZ5dWkjv6ShFq8pOLU50JE0tI0dHRZ5dWkjv6ShBIvqbhBOxSfb61UuYhIXw0dfURZ5dW0uWNVWeUyMCnxkorL5XJllYuI9FXH6oVllVeVHiySBJR4ScXlNr1YVrmISF9lqmuDhtKRBJR4ScVlqc+FiGzfslTfNA0dU1a5DExKvKTiRkycSkPjkC5lDY1DGDFxao0iEpHtVZb6eGUpFskuJV5Scc3jJ7PboZdFV3kNNA0dw26HXqbHqUWk4l5afkdZ5dWUqf5mklkax0uqonn8ZCVaIlJ1uU3Fx+sqVV5NmepvJpmlFi8REZEK0PRFkoRavEREpH41DoXOjuLlKWtoaKDYoDm1nL5ow+Kr2NyxmqahoxkxcaruRGSAEi8REalbgxp3ZEuRxGtQ446px6LpiyQJ3WoUEZG6laVkJ0uzdmj6ouxSi5eIiGBmuwBzgaOAZ4EL3X1BiXW/CJwPDAFuBqa4+6tmtiPgwJHALsCTwDR3/1W14m4aOrrolDy1GMdry+ZXyiqvJnX0zy61eMl2r621hdaWSTyxYE9aWybR1tpS65BEsuhq4DVgFHAy8D0zO6BwJTP7AHAB8D5gD2BPYEa0uAlYCbwHeD1wMXCjme1RraAzNW7glpfLK6+iLLW+SVdq8ZLtmvo5iPTOzHYCjgMmuHs7cL+Z/QI4lZBkxZ0GzHX3pdG2s4D5wAXuvhH4cmzd282sFTgYWFaN2POfY3Ui70pz5maXEi/ZrvXUz2GgV8wiMfsCne7+eKxsMaHlqtABwG0F640ysxHuviG+opmNiva9tNhBzexM4EyAvffeu8/Ba9zA7rI0vpl0pcRLtmvF+n70VC4yQA0DCmexfxEYnmDd/O/Dga2Jl5kNJrSEXevujxY7qLvPAeYAzJkzR00xFdUAxQe3SDsQKaA+XiIi0g40F5Q1Ay8lWDf/+9Z1zWwQcD2hz9jZlQtTkiuVxyq/rTUlXiIi8jjQZGb7xMomUvwW4dJoWXy9tfnbjGbWQHg6chRwnLtvqk7I2+gBGqknutUoIjLAuftGM7sFmGlmnwIOAo4F3llk9euAeWY2H3gGuAiYF1v+PeAtwJHuXvXH+fQAjdQbJV4iIgJgwI+AdYS+WlPcfamZjQX+Aezv7ivc/S4zuxK4l23jeE0HMLNxwFnAq8AaM8vv+yx3n1+NoPUATfZp6qKulHiJiAju/hzQ7dvQ3VcQOtTHy2YDs4usu5yUe2/rAZpsU4tkd+rjJSIi9auhsbxySZWmLupOLV4iKVFzu0gV5DrLK5dUaeqi7pR4SVUoyeiqrbWFtQ+eB1vCA16bO1aFvxm4ze0iFdHQWDzJUotXJmRpLs2sUOKVYZWYtDZathA4HNgcrb7K3ferVty6p9/d+odnbk26ttqyifUPzxyw74lIRajFK9NGTJza5fsAajiXZkYo8cq2+KS1BwF3mNni/BxpebFJa98LrAZuJUxaG59j7Wx3vyaNoPWUUXdbXnu+rHIRSaZp6JgSLSpjahCNFNJcmt0p8cqoSk1am2LIW+mevoikZejoI2h7svtIFUNHH5F+MFKU5tLsSolXdlV60trLzewK4DHgS+6+sNhBKzFpbZbu6etqWGT71r7izpLlow69NOVopBj1+e1Kw0lkV6UmrYXQ92tPYAxhQtpfmtlexQ7q7nPc/RB3P2TYsGHFVunViIlTaWgc0qWsVvf0m4aNK6tcROqLbuNnW77Pb7gAzm3t8zuQp3VSi1d2VWzSWnd/MLbsWjM7ETga+E5lQi0IMkP39F9Z/2BZ5QOFrkBFJA3q89udEq8ymNkowhOGE4GdgRcIt/Xudvc1FT7c1klr3f2JqKy3SWtvjK23NnabsVCOKo8unZl7+nriqRs9dSrbk4bBO5Pb9ELRcqk9zSzQnRKvBMzsLcAs4N+Bh4F/AmsIt/JOBb5pZvcCl7j7PypxzEpNWmtmOwOHAfcRhpM4Hng38IVKxFlKdlpUGgh5ZrHygUlXoPUj5Yu9urTbIdNZ+8dzgC2x0kHsdsj0WoUk0iMlXsnMA64CTs6PjRVnZjsQkqK5wL9V8Lj9nrQWGAxcCrwZ6AQeBSa7+2MVjLOLttYW1v7pPMjFBgv9U60GC1XiVUhPnWZfLS726tqgRtiypevfIhmlxCsBdz+sl+WvAT+Pfip53EpMWrseeEcl4+rNuodmbE26tsptYt1DM2qQeG0ps3z7l6WnTqWkecDX6Pli78NU/mKv7mxYfFXRwYnVgpsRmlmgGz3VmJCZvauX5V9JK5asK9bfoqdySVeWnjqV4qKLvbXFkq5o+WvA29x9QCddoD5EWfe6kcXbLUqVDwRKvJK7zcyKnilmNhs4JeV4RPqkefxkho//yLYrzoZGho//iFoHskd1ThKlWk4GcItKlrz6fPE74aXKBwIlXskZcLuZva1LoZkT+ncVG9h0QBq0wxvKKpd0tbW20Pb0jdua/3OdtD1944AeVyejVOckoSeXM013QLpT4pWQu98ATAV+bWYTAMxsLnAk8B53X1bD8DJl2NijyyqXdPU0Ybdkh+qcZErNQqHZKSSrlHiVwd2vJQzVcLeZ3QocDrzb3f9V28iypWP1wrLKJV0a6bt+qM7pXak5GTVXo2SVnmpMyMzeG/36JPAA4arzM8D+ZrY/gLv/rkbhZYqGKxDpP9U5yehCT+qNEq/k5hb8/TxweezvHGE+xAGvoWkIuc0dRctFJDHVOQnoQk/KkYXBvZV4JeTu42sdQ70olnT1VC5pG0TxcczU8yBLVOcko3HpJKmsTJemmjYBM/t8NGBhT+vsaGafTysmkb5q3vvEsspFskzj0klSPU2Xlia1eCWzO/CUmd1JmPPwMeAlwvQd+wJHAB8kzJkokmlDRh5C25ML6DqVUgNDRh5Sq5CkQHQR94NSA6hG6+wInOXu304vsuzJt1TU+vaRZF9Wbksr8UrA3adFAxaeDpwBHEiYsPZ54G/AncA0d99QsyAzRbeysmzdQzPoPn9lrkZTOkkJuwNP6mIvmebxk3XuSq+ycltaiVdC7v4sYaLsdNsk65LmR8wyDWiYfbrYK08WOkxL9o2YOLVLHy+ozW1pJV4iIhmki71kstJhWrIvK7ellXiJDDgNdL/VmC8XqS89dZhW4iWFsnBbWp1upApKTU6rSWuzoVjS1VO5SHZlpcO0SFJKvKQK1Mcry7I0iXlbawutLZN4YsGetLZM0kTdUrZSHaM1jpdklW41ShWoRSXLtmxqL6u8WtQ3J1vMbBfCaPlHAc8CF7r7ghLrfhE4HxgC3AxMyQ99Uc5+KiErHaZFklLilZCZ3UvPmUPO3d+XVjzZpj5EmZbbVF55lahvTs/M7CPufkuJZYOBS9z94goe8mrgNWAUcBBwh5ktdvelBcf+AHAB8F5gNXArMCMqS7yfSmkeP5mX1z9E21M3QK4TGhoZPv4jOocks5R4JfeTEuVjgM8DQ1OMJdsah0BnkemBGjVXo2yjvjm9+oaZnQh81t3X5QvNbBJwDbCyUgcys52A44AJ7t4O3G9mvwBOZVtClXcaMDefSJnZLGA+cEGZ+6mIttYWXmq9JSRdALlOXmq9hSEjD1HyJZmkxCshd+8yYa2ZjQAuBD4N/AyYWYu4MqmgFaPXchmQsjKYYYYdAHwNWGpm5xBalq4EPgacX1gn9dO+QKe7Px4rWwy8p0RctxWsNyqqE8eWsR/M7EzgTID99tuPJUuW9CH0veGAG7uVrtgI9Gl//fDWX5Zc1LfX1g9ZiSUrcWSIEq8ymVkzcC5wNnA78HZ3f6q2UWWLvlAlCfXN6VnUYjTFzG4AbgIcuIfQmrSmwocbBrxYUPYiYaT83tbN/z68zP3g7nOAOQBz5szJTZgwobyogScW7Emprg37nPR02fvrjycWlJ7XfJ+TWlOMJDuxZCWOarj55pv7tJ0Sr4TMbAjwBeAcYCEwqVp9FuqdvlAliawMZphlUSvSWcAm4BFgf0LrVKUTr3aguaCsmTBNUW/r5n9/qcz9VIQu9KTeaDiJ5FqB/yE09Tuhaf298Z/ahpcdzeMns9uhl9E0dAzQQNPQMex26GX6QpVumsdPZvzk+9nnpKcZP/l+nSMxZnYC8E/gFWB/d38/oUvDTWb2/aj1vVIeB5rMbJ9Y2USg2MXl0mhZfL210fRF5eynIkZMnEpDQf9RXehlR5aGr8kKtXgl9wqhPXtKieU5YM/0wsm2LIwOLFLnLgdOdve78wXu/hMz+w3wXeAfwBsrcSB332hmtwAzzexThKcRjwXeWWT164B5ZjYfeAa4CJjXh/1UhFpOs23kwZew9sHzYEvsqelBgxl58CW1C6rGlHgl5O571DoGERlQJrj7xsLC6AnHj5vZsRU+ngE/AtYBGwhjcy01s7GEJG9/d1/h7neZ2ZXAvWwbx2t6b/upcKxSJ5QYd6fES2SAaRo6pkSfmDE1iEZKKZZ0FSy/raflfTjec0C3b0N3X0HoNB8vmw3MLmc/1dLW2sLaP523dRy6zR2rwt9oIN6s0B2QrpR4JRRd9fUoqqBEMk0PP9QH1TnJrHtoRvfBf3ObWPfQDH3ZSyYp8UpuGdueWS42BHsOzQItdUAjfdeNZajO6VVu0wtllYvUmp5qTO5vwBOEjqTjgMEFPzvULjSR5NpaW2h7+sYuI323PX2jJqjOHtU5ItshJV4JuftBwEeBXYD7gTuBE4Ad3L3T3TtrGV/WtLW20NoyiScW7ElryyR9qWfI+odndn3CCGDLplAumaE6J5mGpuKztZUqF6k1JV5lcPcl7n4uMJ7QsfQY4Bkze3ttI8uWttYW1i2aFnXgzrG5YxXrFk1T8pURW157vqxyqR3VOQk0lGj4K1UuUmNKvPpmH8LcY/8G/BXQN1bMhsVXdem4DZDrfJkNi6+qUUQidU91Tgm5TYUzFPVcLlJr6lyfkJntApwInEaYd+x64N16qqi7zR2ryyoXke5U5ySjKYOk3ijxSm41Ydqg64E/RWV7m9ne+RXc/Xe1CCxrVBFm3SBgS4lyyRDVOQkMHX0EbU/OL1o+oDU0bnuAprBcakqJV3JrgNcBn45+CmnKoIjGicq25r1PLPpF1bz3iTWIRnqgOieBjtULyyofMIolXT2VS2qUeCWkKYOS0xQR2Tbq0EsBuozj1bzXCVvLJRtU5ySjrg0lqMUrs5R4JWRmBwOvuvuS6O+RwDeBCcAfganu3l7DEDNFU0Rk26hDL1WilXGqc5LJVNeGLCU7avHKLHXqSO6bwO6xv68B9gXmECrCK2sRVFZpHC+RflOdk8CIiVOhYXDXwobBtenakKVkp1SypxavmlPildxbgP8FMLOdgQ8CJ7v71YQnjz5Uw9gyReN4iVSE6pykCidUKjbBUgoaBu9cVnlVZSkJlC50qzG5JuC16PfDgTXu/jiAu6+MKsaKih4nnwscBTwLXOjuC0qs+0XgfGAIcDMwxd1fLXc/ldDTOF66/SiSWOp1Tj3asPiqojMx1KS+yb1WXnkVNQ0dU+IW7JjUY5Gu1OKV3FLgY9HvJwD35BeY2RigGqP1XU2oeEcBJwPfM7MDClcysw8AFwDvA/YgPOk0o9z9VIo6u4pURC3qnLqTpfomt7mjrPJqGjFxKg2NQ7qU6enybFDildx5wA/M7DngP4GvxpYdDzxQyYOZ2U7AccDF7t7u7vcDvwBOLbL6acBcd1/q7s8Ds4DT+7CfimgY/PqyykWkqFTrnHpVqhP9QB83sHn8ZHY79LKohauBpqFj2O3Qy3TXIQN0qzG5sdHPvsDj7v5SbNkdwA0VPt6+QGf+1kJkMWHakEIHALcVrDfKzEYQYk66H8zsTOBMgP32248lS5aUH/lbri+5qE/764+3/rLkolRjyUocUk/SrnPqksYNLE1Pl2eTEq/kfuDuzcDDhQvc/bEqHG8Y3W8lvEiYOqS3dfO/Dy9zP7j7HMJTU8yZMyc3YcKE8qIGnliwJ2Fsx0IN7HPS02Xvrz+eWDC+5LJ9TmpNLY6nbnp70UmoB+3wBvb66F9Si2N7dvPNN9c6hEpLu86pS9kaN7CBUnWfSJ4Sr+TS/uS0A80FZc3ASwnWzf/+Upn7qYgsjauTlQ6mIw++hLUPnte1E/CgwYw8+JJU45C6om/rhLLTslMs6eqpXAYiJV7JNZrZv9NDZVjhedMeB5rMbB93fyIqm0jocFtoabTsxth6a919g5m9UsZ+KiJLTf9Nw8YVT7yGjUs1jmxdlUudSLvOEZEUKPFKbkfCkAylKsGKzpvm7hvN7BZgppl9CjgIOBZ4Z5HVrwPmmdl84BngImBeH/ZTEVn5kjsuAAATeUlEQVRKMl5Z/2BZ5dWUnatyqROp1jkikg4lXsltdPe0KzkDfgSsAzYQxuZaamZjgX8A+7v7Cne/y8yuBO5l2zhe03vbTzUDz0ySoUEEpX7Vos4RkSpT4pVh7v4c0C17cfcVhE7z8bLZwOxy9jMwqLOriIhkh8bxSk7f1HWp1L9N/07JPJ2kdUf1jfROLV4JuXvR4Rck67aUWS6SDapzkmtrbclEn1I91ShJqMVLRETqVltrC2sfPC96ejnH5o5VrH3wPNpaW1KPpdQwNZofUeKUeImISN1a//DMopNkr394ZuqxDB19RFnlMjAp8aoQM9N7KSKpUZ0TFJsRoqfyaupYvbCschmY1Mern8zsQMIk1ScCak8WkapSnZNdmztWl1UuA5MSrz4ws5HAScDpwF7Ar4DdaxmTiGy/ql3nmNkuhMFajwKeBS509wU9rP9F4Hy2jRs4xd1fNbMdAQeOBHYBngSmufuvKhVroYbBO5Pb9ELR8rQN2mHnEnOyph+LZJcSr4TMbDDwYULF9+/Ab4ErgdsIj8h9tGbBich2J+U652rgNWAUYXaLO8xscbGBls3sA8AFwHuB1cCtwIyorAlYCbwHWAEcDdxoZge6+7IKxrvVbodMZ+2fzoNcrJ9Xw2B2O2R66Y2qJJcr/vRiqXIZmJR4JbcG+CdhKp5PuPvWy5roKk9EpJJSqXPMbCfgOGCCu7cD95vZL4BTCclUodOAufmkzMxmAfOBC9x9I/Dl2Lq3m1krcDCwrFIxx2VpirLcphfLKpeBSZ0zk/s78BbgMOAgM9OIeHVAj3dLHUurztkX6HT3x2Nli4EDSqx/QLQ8vu4oMxtRuKKZjYr2X3SKMjM708weMrOH2tvb+xR8ljQNHV1WuQxMSrwScvcjgLcTms/nAP8ys9lmdkhNA5MejZg4lYbGIV3KGhqHMGLi1BpFJJJMinXOMKCwSeZFoNQAroXr53/vsn50q3Q+cK27P1psR+4+x90PcfdDhg0bVmyVXrW1trBu0bQu43itWzStJuN4qb6RJHSrsQzuvhyYBcwys0nAJ4B7CBNPS8zaRRfR9tQNYTLqhkaa9zqBUYdemnocWboNIVKuStQ5ZraQ0OeqmAeAzwHNBeXNwEsltmkvWD//+9b1o6Eurif0Gzs7aax9sWHxVeQ6X+5Slut8mQ2Lr0r9c676RpJQ4tVH7n4/oS/E54H/R6gQhSjpenL+toJc59a/a5V8qeKTetfXOidqOSsp6uPVZGb7uPsTUfFEStwejMonAjfG1l3r7hui/TUQnpAcBRzt7puK7qVCsjaEg+ob6Y0Sr35y91eAn0Y/AqGlq0R5LRIvke1Jpescd99oZrcAM83sU4SnGo8F3llik+uAeWY2H3gGuIjwAEDe9wh9045095e7b15ZTUNHR7cZu5eLZJH6eEnl5TrLKxeRWjPCmFzrCAndlNhTi2PNrN3MxgK4+12EYS3uBZZHP9OjdccBZxGStzXRdu1mdnK1Ale/Kqk3avGSymtoLJ5kNTSmH4uI9MrdnwOK3h9z9xWEDvXxstnA7CLrLgdSfeJb/aqk3ijxkop73cjDeGXdH4qWi4hUmvpVST3RrUapuM3ty8sqFxERGSiUeEnFZe0pIxERkaxQ4iUVV2pCWE0UKyIiA50SL6k4TRQrIiJSnBIvqThNFCsiIlKcEi+pOE0UKyIiUpwSL6k4DWgoIiJSnMbxkorTgIYiIiLFKfGSqtCAhiIiIt3pVqOIiIhISpR4iYiIiKREiZeIiIhISpR4iYiIiKREiZeIiIhISpR4iYiIiKREiZeIiIhISpR4iYiIiKREiZeIiIhISpR4iYiIiKREiZeIiIhISpR4iYiIiKREiZeIiIhISppqHYB0Z2a7AHOBo4BngQvdfUEP638ROB8YAtwMTHH3V6NlC4HDgc3R6qvcfb/qRS8iIiKlKPHKpquB14BRwEHAHWa22N2XFq5oZh8ALgDeC6wGbgVmRGV5Z7v7NVWPWkRERHqkW40ZY2Y7AccBF7t7u7vfD/wCOLXEJqcBc919qbs/D8wCTk8lWBERESmLWryyZ1+g090fj5UtBt5TYv0DgNsK1h1lZiPcfUNUdrmZXQE8BnzJ3ReWOriZnQmcCbD33nv37RUAba0tbFh8FZs7VtM0dDQjJk6lefzkPu9PRKQU1TdST5R4Zc8w4MWCsheB4QnXz/8+HNhA6Pv1D8KtyxOAX5rZQe7+VLGdufscYA7AnDlzcn15AW2tLaxbNI1c58sAbO5YxbpF0wBUGYpIRam+kXqjxCtlUWf3Uq1XDwCfA5oLypuBl0ps016wfv73lwDc/cHYsmvN7ETgaOA7yaMuz4bFV22tBPNynS+zYfFVqghFMqiSD/TE1tkH+Dtwk7ufUq3YVd9IvVHilTJ3P6Kn5VEfryYz28fdn4iKJwLdOtZHlkbLb4ytuzZ2m7FQDmgoK+gybe5YXVa5iNRcpR/oye/zz9UMGlTfSP1R4pUx7r7RzG4BZprZpwiV4LHAO0tsch0wz8zmA88AFwHzAMxsZ+Aw4D7CcBLHA+8GvlDN19A0dDSbO1YVLReRbIk90DPB3duB+80s/0BPYTIFsQd6ou1nAfPj65rZCcALwB+AvncWTUD1jdQbPdWYTUZowl8H/JTQjJ+v5MaaWbuZjQVw97uAK4F7geXRz/RoP4OBS4H1hNsHnwMmu/tj1Qx+xMSpNDQO6VLW0DiEEROnVvOwItI3pR7oOaDE+gdEy+PrjjKzEQBm1gzMBM6pQqzdqL6ReqMWrwxy9+eAop0T3H0FoUN9vGw2MLvIuuuBd1Qjxp7k+1XoKSORulDpB3pmEVrEVppZjweuxFPUqm+k3ijxkqpoHj9ZFZ9IBqT5QI+ZHQQcCbwtSWyVeIoaVN9IfVHiJSKyHUvzgR4zOxXYA1gRtXYNAxrNbH93f3u/XojIdkKJl4jIAFbJB3oIrVc3xNadSkjEplQ+cpH6pMRLSnrmmWeenTFjxvL+7KOzs3PXxsbGZysVU39kJZasxAHZiaVCcYyrSDADkwE/IjzQs4GCB3oIgzDv7+4r3P0uM8s/0JMfx2s6gLt3AB1bd2rWDrwS9Tftkeqb6slKLFmJA2pc5+RyOf3op2o/U6ZMeajWMWQtlqzEkaVYshKHfur7J0vnkWLJbhy1jkXDSYiIiIikRImXiIiISEqUeEm1zal1ADFZiSUrcUB2YslKHFLfsnQeKZbushIH1DCWhlyuz0OniIiIiEgZ1OIlIiIikhIlXiIiIiIpUeIlIiIikhINoCpVYWZnA6cDBwI/dffTaxTHjoAT5o/bBXgSmObuv6pBLD8B3gfsBKwBrnT3a9KOIxbPPsDfgZvc/ZQaxbAQOBzYHBWtcvf9ahGL1C/VNyXjUZ3TPYaF1LjOUYuXVMtq4FLCaNi11ASsJEwS/HrgYuBGM9ujBrFcDuzh7s3Ah4FLzezgGsSRdzXw5xoeP+9sdx8W/Sjpkr5QfVOc6pzialrnqMVLqsLdbwEws0OAN9Ywjo3Al2NFt5tZK3AwsCzlWOKTDuein72Ah9OMA8DMTgBeAP4A7J328UUqSfVNyXhU52SQEi8ZUMxsFLAvsLS3dat0fCfcEhkC/BW4swYxNAMzCbcgzkj7+EVcbmZXAI8BX3L3hTWOR6Qial3fRDGozumupnWObjXKgGFmg4H5wLXu/mgtYnB3A4YD7wJuAV6tQRizgLnuvrIGxy50PrAnMIYwoOEvzWyv2oYk0n9ZqG9AdU4RNa9z1OIlA4KZDQKuB14Dzq5lLO7eCdxvZqcAU4Bvp3VsMzuI0PH3bWkdsyfu/mDsz2vN7ETgaOA7NQpJpN+yVN+A6py4LNQ5avGS7Z6ZNQBzgVHAce6+qcYh5TUR+luk6QhgD2CFma0BpgLHmdlfUo6jlBzQUOsgRPoqw/UNqM4pJvU6Ry1eUhVm1kQ4vxqBRjN7HbDZ3Tf3vGVVfA94C3Cku79cg+NjZrsB7wVuB14mXAGeCJyUcihzgBtif08lVIpTUo4DM9sZOAy4j/Bo9/HAu4EvpB2L1DfVN92pzukuK3WOEi+plouA6bG/TwFm0PWJn6ozs3HAWYR+DWvMLL/oLHefn2IoOUJF831CS/Ny4AvufluKMeDuHUBH/m8zawdecff1acYRGUwYAuDNQCfwKDDZ3R+rQSxS31TfdKc6p7tM1DmaJFtEREQkJerjJSIiIpISJV4iIiIiKVHiJSIiIpISJV4iIiIiKVHiJSIiIpISJV4iIiIiKdE4XpIqM5sH/MvdL6rBsRuAHwGTgSfc/dC0Y+gvM1sGfMrd7ymy7AjgJ+7+xrTj6g8zW0iI+5paxyLbH9U5/aM6p/KUeA1w0YdqCLCnu2+Myj4FnOLuR9QwtGqYBLwfeGP+tYpIulTnyECnW40CIQH/71oHUS4zayxzk3HAMlWA2ycza4gmJ5bsU50jda+vdY5avATga8B5Zubu/kJ8gZntAbQCg/PznsWbac3sdODTwCLgk8BzhOk69gVmATsC57r7tbHd7mpmdwOHA38BPuHuy6N9v5kwS/zBwHrgYne/MVo2jzDn2DjgPcCxQJfmbzMbTZgiY1IUy1fd/YdmdgZwNTA4mrLi6+4en2Ikv/1/AecCu0ev6cxYbPkpOM4BdgUWAGe7e87M9iZMjHsQsAn4rbsfn/A1dQDjgXcBi4HjgAuA04C1wInu/tdYmO8ws28D/wdoAaa4+ytFXsvo6LjvBtqBb7j7twvX60scPe3bzL4MHECYNuVYYFm0r+OAL0blZ7j7b2Ih7GVmi4D9gIXAJ939uWh/hwOzgf0J0578t7svjJYtBB4gTMT7duBAM5sEXAKMBJ4FLqrBdC3SM9U527ZXnTPA6hxdHQrAQ4QTb2oftz8M+BswglAx3AC8A9ibUCF+18yGxdY/mVBB7go8AswHMLOdgLujfexGmNDVzeyA2LYnAV8BhgP3F4nlp8C/gNHAR4HLzOx97j4X+AzwR3cfVqICnAxMAz5C+AD9b7S/uGOi1zYR+Djwgah8FvAb4A3AGwkVRNLX9HHCXHO7EiqIPxK+HHYFbiJUAHEnR8fdi/Bl063vSnQV9ktCZTYGeB/wBTP7QOG65caRcN8fAq6P3o+/Ar8m1DdjgJnADwqO/Qngvwj/t81AvkIdA9xBmF9tF8I5erOZjYxteypwJuGcWB9t+0F3Hw68k3COSbaozkF1TtI4trc6Ry1ekncJ8ICZfasP27a6+48BzOxnwJeAme7+KvAbM3uNUCHmT8Y73P330fpfAl40szcRTthl+X0BfzGzmwmV2dKo7DZ3fyD6vcsVV7SPScAx0dXYI2Z2DeFD8tsEr+Ms4HJ3/2e0v8uAaWY2Ln8FClwRXaG/YGb3Eq427yJccY4DRrv7v9hWQR+T4DXd6u4PR8e8FTB3vy72fp5dEOd33X1ltPwrhAq3sCJ8BzDS3WdGfz9tZj8ETiBUSMUkjSPJvv/X3X8dbftzwhfLFe7eaWY3AHPMbOdYa8f17r4kWv9iwv/uNMKX6J3ufme03t1m9hBwNJBv0Zjn7kujbTcDW4AJZrbC3Z8BninxeqW2VOeozhmQdY4SLwHA3ZeY2e2EZt5/lrn52tjvL0f7KyyLX32ujB233cyeI1x1jAMOM7P4rYcmwlVMt22LGA085+4vxcqWA4ckeRHR8b9lZl+PlTUQrpjyleCa2LIOtr2u8whXoIvM7HnCbYUfkew1Fb5XPb130PU9WE543cVey+iC4zYSrqhLSRpHkn0Xbvusu3fG/ibaX34fha9pMOGqdxzwMTP7UGz5YODe2N/x82mjmR1PuEqda2YPAOe4+6PdX67UkuocQHXOgKxzlHhJ3HRCM2+8Esh3Ch0KtEW/797P47wp/0t0O2AXYDXhZL7P3d/fw7a5HpatBnYxs+GxinAssCphXCuBr/SlP5C7ryH0OyG633+Pmf2eZK+pXG+K/T6W8LoLrSS0CuxTweNWc9+Fr2kToa/ESsKV6ad72LbLORFd9f7azIYQbhf8kNCHRLJHdY7qnCS2qzpHiZds5e5PRs27nwf+HpWtN7NVwClm9gNCp8e9+nmoo6OKYhHhiu1Bd18ZXf1eYWanEvpsQGhWb883xfcS/0oz+wNwuZlNJfRFOIPQdJzE94FZZvaIuy81s9cDR7n7z3vb0Mw+RujL8S/gecIHsxPo12sq4bPRe9VB6B/ysyLrLALazOx8Qv+D14C3AEPc/c99PG41932KmV1H6BQ7E7gpukXwE+DPUV+OewhXnocDT0bvdRdmNorQ/+e3hKvcdsL/QTJIdY7qnIS2qzpHneul0Exgp4KyTxOeutlAeHLkD/08xgLCle5zhKduTgaIrhiPIty3X01oYv8q4SmlpE4E9oi2vxWY7u53J9nQ3W+NjneDmbUBS4APJjzuO4AHLTy99AvCUzCtFXpNhRYQOtU+Hf1cWuS1dBI6mx5EeELsWeAa4PX9OG419309MI/w/ryO8EVM1K/kWEJlv55wNXoupeuuQYQnwFYTzq/3ANaPuKT6VOeozunR9lbnNORyPbWiioiIiEilqMVLREREJCVKvERERERSosRLREREJCVKvERERERSosRLREREJCVKvERERERSosRLREREJCVKvERERERS8v8BELispI3up48AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5 members\n",
    "fig, axes = plt.subplots(2,2, figsize=(8,6))\n",
    "\n",
    "axes = axes.flatten()\n",
    "plot_enssize_response_5m('TREFHT', ax = axes[0])\n",
    "plot_enssize_response_5m( 'TSMN', ax = axes[2])\n",
    "plot_enssize_response_5m( 'TSMX', ax = axes[3])\n",
    "plot_enssize_response_5m( 'DTR', ax = axes[1])\n",
    "\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_2_nores\n",
      "(10,)\n",
      "TREFHT range 1 ens member: -0.048706055 - 0.20501709\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2ad6a2f5ad50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "var ='TREFHT'\n",
    "ax=False\n",
    "ylims = False\n",
    "panel_label = False\n",
    "    \n",
    "n_ens = 5\n",
    "resmask = get_resmask(threshold=0)\n",
    "# open all ensemble members and calculate temporal and spatial mean\n",
    "da_res_em = open_da_ens(var,case=case_res, mode='all').where(resmask)    \n",
    "da_nores_em = open_da_ens(var,case=case_nores, mode='all').where(resmask)\n",
    "da_res_em_mean = da_res_em.mean(dim=('time','lat','lon'))\n",
    "da_nores_em_mean = da_nores_em.mean(dim=('time','lat','lon'))\n",
    "\n",
    "# calculate combinations with 1 ensemble member and calculate average over those ensemble members\n",
    "mean_1_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 1))).mean(axis=1)\n",
    "mean_1_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 1))).mean(axis=1)\n",
    "[meshgrid_1_nores, meshgrid_1_res] = np.meshgrid(mean_1_nores, mean_1_res)\n",
    "delta_1m = (meshgrid_1_res - meshgrid_1_nores).flatten()\n",
    "\n",
    "# calculate combinations with 2 ensemble members and calculate average over those ensemble members\n",
    "mean_2_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 2))).mean(axis=1)\n",
    "mean_2_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 2))).mean(axis=1)\n",
    "[meshgrid_2_nores, meshgrid_2_res] = np.meshgrid(mean_2_nores, mean_2_res)\n",
    "delta_2m = (meshgrid_2_res - meshgrid_2_nores).flatten()\n",
    "\n",
    "print('mean_2_nores')\n",
    "print(mean_2_nores.shape)\n",
    "\n",
    "# calculate combinations with 3 ensemble members and calculate average over those ensemble members\n",
    "mean_3_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 3))).mean(axis=1)\n",
    "mean_3_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 3))).mean(axis=1)\n",
    "[meshgrid_3_nores, meshgrid_3_res] = np.meshgrid(mean_3_nores, mean_3_res)\n",
    "delta_3m = (meshgrid_3_res - meshgrid_3_nores).flatten()\n",
    "\n",
    "# calculate combinations with 4 ensemble members and calculate average over those ensemble members\n",
    "mean_4_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 4))).mean(axis=1)\n",
    "mean_4_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 4))).mean(axis=1)\n",
    "[meshgrid_4_nores, meshgrid_4_res] = np.meshgrid(mean_4_nores, mean_4_res)\n",
    "delta_4m = (meshgrid_4_res - meshgrid_4_nores).flatten()\n",
    "\n",
    "# calculate combinations with 3 ensemble members and calculate average over those ensemble members\n",
    "mean_5_nores = np.asarray(list(itertools.combinations(da_nores_em_mean.values, 5))).mean(axis=1)\n",
    "mean_5_res = np.asarray(list(itertools.combinations(da_res_em_mean.values, 5))).mean(axis=1)\n",
    "[meshgrid_5_nores, meshgrid_5_res] = np.meshgrid(mean_5_nores, mean_5_res)\n",
    "delta_5m = (meshgrid_5_res - meshgrid_5_nores).flatten()\n",
    "\n",
    "data = np.asarray([delta_1m, delta_2m, delta_3m, delta_4m, delta_5m])\n",
    "\n",
    "print(var+' range 1 ens member: '+str(delta_1m.min()) +' - '+ str(delta_1m.max()))\n",
    "print()\n",
    "# VIOLIN PLOT \n",
    "if not ax: \n",
    "    fig , ax = plt.subplots()\n",
    "\n",
    "# create dummy violinplot to have white left sides\n",
    "parts1 = ax.violinplot(data, showmeans=False, showmedians=False,\n",
    "        showextrema=False)\n",
    "for pc in parts1['bodies']:\n",
    "    pc.set_facecolor('palegoldenrod')\n",
    "    pc.set_edgecolor('darkgoldenrod')\n",
    "    pc.set_alpha(1)\n",
    "\n",
    "    # remove the left half of plot\n",
    "    # get the center\n",
    "    m = np.mean(pc.get_paths()[0].vertices[:, 0])\n",
    "    # modify the paths to not go further right than the center\n",
    "    pc.get_paths()[0].vertices[:, 0] = np.clip(pc.get_paths()[0].vertices[:, 0], m, np.inf)\n",
    "    pc.set_color('w')   \n",
    "\n",
    "\n",
    "# this is the real violinplot\n",
    "parts2 = ax.violinplot(data, showmeans=False, showmedians=False,\n",
    "        showextrema=False, widths=1)    \n",
    "\n",
    "for pc in parts2['bodies']:\n",
    "\n",
    "    m = np.mean(pc.get_paths()[0].vertices[:, 0])\n",
    "    # modify the paths to not go further right than the center\n",
    "    pc.get_paths()[0].vertices[:, 0] = np.clip(pc.get_paths()[0].vertices[:, 0], m, np.inf)\n",
    "    pc.set_facecolor('palegoldenrod')\n",
    "    pc.set_edgecolor('tan')\n",
    "    pc.set_alpha(1)\n",
    "\n",
    "# remove the line in the fifth violinplot    \n",
    "parts2['bodies'][-1].set_edgecolor('w')\n",
    "\n",
    "# plot medians and IQRs\n",
    "\n",
    "# helper function\n",
    "def adjacent_values(vals, q1, q3):\n",
    "    upper_adjacent_value = q3 + (q3 - q1) * 1.5\n",
    "    upper_adjacent_value = np.clip(upper_adjacent_value, q3, vals[-1])\n",
    "\n",
    "    lower_adjacent_value = q1 - (q3 - q1) * 1.5\n",
    "    lower_adjacent_value = np.clip(lower_adjacent_value, vals[0], q1)\n",
    "    return lower_adjacent_value, upper_adjacent_value\n",
    "\n",
    "medians = []\n",
    "whiskers_min = []\n",
    "whiskers_max = []\n",
    "quartiles1 = []\n",
    "quartiles3 = []\n",
    "for ensmember in data: \n",
    "    (quartile1, median, quartile3) = np.percentile(ensmember, [25, 50, 75])\n",
    "    whisker = np.array([adjacent_values(ensmember, quartile1,  quartile3)])\n",
    "    whisker_min, whisker_max = whisker[:, 0], whisker[:, 1]\n",
    "\n",
    "    medians = np.append(medians,median)\n",
    "    whiskers_min =  np.append(whiskers_min, whisker_min)\n",
    "    whiskers_max =  np.append(whiskers_max, whisker_max)\n",
    "    quartiles1 =  np.append(quartiles1, quartile1)\n",
    "    quartiles3 =  np.append(quartiles3, quartile3)\n",
    "\n",
    "\n",
    "inds = np.arange(1, len(medians) + 1.0)\n",
    "inds = inds + 0.02\n",
    "ax.scatter(inds, medians, marker='o', color='maroon', s=20, zorder=3)\n",
    "ax.vlines(inds, quartiles1, quartiles3, color='darkgoldenrod', linestyle='-', lw=4)\n",
    "#ax.vlines(inds, whiskers_min, whiskers_max, color='goldenrod', linestyle='-', lw=3)\n",
    "\n",
    "# do axes settings    \n",
    "ax.set_xticks([1,2,3, 4, 5])\n",
    "ax.set_xlim((0.5,5.5))\n",
    "\n",
    "if ylims != False: \n",
    "    ax.set_ylim(ylims)\n",
    "if panel_label != False:\n",
    "    ax.text(0, 1.02, panel_label, color='dimgrey', fontsize=12, transform=ax.transAxes, weight = 'bold')\n",
    "\n",
    "ax.set_xlabel('Number of ensemble members')\n",
    "ax.set_ylabel(da_res_em.units); \n",
    "\n",
    "\n",
    "if var == ('TSMN'): da_res_em.name = 'TNn'\n",
    "if var == ('TSMX'): da_res_em.name = 'TXx'\n",
    "if var == ('TREFHT'): da_res_em.name = 'T$_{2m}$'\n",
    "\n",
    "ax.set_title('$\\Delta$ '+da_res_em.name, loc='right', size=12)\n",
    "ax.axhline(0, color='lightgray', linewidth=1)    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_2m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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