fates_parameter_selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Parameter Selection for the Functionally Assembled Terrestrial Ecosystem Simulator (FATES)\n",
    "\n",
    "__Summary__<br>\n",
    "Numerical models that simulate tropical forest ecosystem dynamics, such as the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), have been proposed as a way to improve projections of future climate change. However, parameterizing these complex, process-based models is challenging due to their numerous and interconnected non-linear relationships and relatively long spin-up requirements. This analysis identifies three high-performing parameter sets for use in future FATES experiments by quantitatively evaluating the performance of nearly 600 simulations, which test 300 unique parameter sets in two different background environments, against diverse observations at a tropical forest test site.\n",
    "\n",
    "__Motivation__<br>\n",
    "Greenhouse gas emissions from human activities are warming the Earth with potentially catastrophic implications for human health. Predicting twenty-first century warming is critical to climate change mitigation and adaptation efforts. Unfortunately, the numerical models used to project future climate differ in their predictions of warming even for the same greenhouse gas emissions scenario. This variability in projections across models stems largely from uncertainty about how Earth's vegetation will respond to environmental change. In particular, predictions of tropical forest responses to climate change must be improved, as these forests exert strong control over global climate. Numerical, process-based models of vegetated ecosystem dynamics, such as the Functionally Assembled Terrestrial Ecosystem Simulator (FATES; Fisher et al., 2015, 2018), have been proposed as a way of improving projections of tropical forests and thus future climate. However, parameterizing these complex, process-based models remains challenging due to their numerous interconnected, non-linear relationships and long spin-up requirements.\n",
    "\n",
    "__Goal__<br>\n",
    "The goal of this analysis is to identify parameter sets that allow the FATES model to best match observations of tropical forest structure and functioning at a test site.\n",
    "\n",
    "__Methods__<br>\n",
    "This analysis identifies three high-performing parameter sets for use in future FATES experiments by quantitatively evaluating the performance of nearly 600 simulations, which test nearly 300 unique parameter sets in two different background environments, against diverse observations at a tropical forest test site.\n",
    "\n",
    "_Parameter ensemble simulations_<br>\n",
    "Prior to this analysis, we ran an ensemble of FATES simulations for Barro Colorado Island, Panama. These simulations were initialized with 287 unique parameter sets, which differed in 12 key plant trait parameters that were sampled from observed distributions when possible (following Koven et al., _in review_). Plant structure and functioning are sensitive to atmospheric carbon dioxide concentration, which increased over the observational time period. We therefore tested each parameter set under two carbon dioxide concentrations (i.e., background environments) that approximately bookend the observational time period (367 ppm and 400 ppm carbon dioxide). All simulations were forced with repeating meteorological data for Barro Colorado Island, Panama, from the years 2003 to 2016 (Faybishenko et al., 2018). See Kovenock (2019) for further details of the parameter ensemble simulations.\n",
    "\n",
    "_Parameter set evaluation and selection_<br>\n",
    "This code uses the above ensemble of simulations to quantitatively evaluate the performance of each parameter set against observations of six variables at our tropical forest test site. These variables characterize ecosystem structure (leaf area index, above-ground biomass, basal area) and functioning (gross primary productivity, latent heat flux, sensible heat flux). Observations come from the following sources:  leaf area index from Detto et al. (2018); above-ground biomass from Meakem et al. (2018), Feeley et al. (2007), and Baraloto et al. (2013); basal area from Condit et al. (2017, 2012), Condit (1998), and Hubbell et al. (1999); and gross primary productivity, latent heat flux, and sensible heat flux from Koven et al. (_in review_). (As some of these data sets require permission to use or are not yet publicly available, observational data sets are not included for download here.)\n",
    "\n",
    "We use two performance metrics $-$ error rate and normalized root mean square error (NRMSE) $-$ to quantify each parameter set's performance. The error rate measures how frequently the model output falls within the observed range for each variable. The NRMSE measures the distance between the model output and the observed mean, relative to the observed range for each variable. The expectation is that high-performing parameter sets will result in model output that falls within the range of observations (low error rate) and near the observed mean (low NRMSE). After evaluating a parameter set's performance for each individual variable, we calculate the weighted average performance across all variables for that parameter set. To ensure that our selection of a high-performing parameter set is robust to weighting method, we consider three different weighting approaches:  even weighting, weighting favoring structural variables (leaf area index, basal area, and above-ground biomass), and weighting accounting for correlation between individual variable performance values.\n",
    "\n",
    "Lastly, we identify parameter sets for use in future experiments by assigning an overall rank to each parameter set based on its performance across both performance metrics, three weighted averaging approaches, and two background carbon dioxide concentrations.\n",
    "\n",
    "__Results__<br>\n",
    "This analysis identifies three high-performing parameter sets for use in future FATES simulations. We recommend the highest-performing parameter set for use in primary experiments and the next two highest-performing parameter sets for use in parameter sensitivity tests. These high-performing parameter sets are publicly available through the University of Washington ResearchWorks digital repository at http://hdl.handle.net/1773/43779. The performance of these parameter sets is reported in further detail in the [Results](#Results) section below and in Kovenock (2019)."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis\n",
    "## Step 1: Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4 as nc4\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "\n",
    "from psfxns import annmeans\n",
    "from psfxns import metrics\n",
    "from psfxns import plotmetrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Load and preprocess data\n",
    "\n",
    "This code section returns two multidimensional arrays, one for model output and one for observations, that contain annual mean time series for six variables organized by parameter set and background carbon dioxide concentration. The six ecosystem characteristics we analyze are: leaf area index, above-ground biomass, basal area, gross primary productivity, latent heat flux, and sensible heat flux."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1  Model output\n",
    "\n",
    "This part of the code loads and preprocesses the model output for all FATES simulations in our parameter ensemble. It returns a 4-D array called 'model_data' that contains annual mean timeseries for each variable, parameter set, and background carbon dioxide concentration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return model_data: a 4-D array of annual mean values \n",
    "# with shape (CO2levels, varlist, nens, nyrs).\n",
    "\n",
    "# Model output file path and names\n",
    "model_filepath = 'data/'\n",
    "name1 = 'fates_clm5_fullmodel_bci_parameter_ensemble_1pft_slaprofile_{}_v001.'\n",
    "name2 = 'I2000Clm50FatesGs.Cdf9b02d-Fb178808.2018-07-27.h{}.ensemble.sofar.nc'\n",
    "model_filenames = [name1 + name2,\n",
    "                   name1 + name2]\n",
    "\n",
    "# Variable list\n",
    "varlist = ['TLAI', 'AGB', 'BA', 'GPP', 'FLH', 'FSH']\n",
    "\n",
    "# File type, indexed by varlist:\n",
    "# 0 = monthly mean for entire ecosystem; and\n",
    "# 1 = annual mean by tree size.\n",
    "varfiletype = [0, 1, 1, 0, 0, 0]\n",
    "\n",
    "# Conversion factor, indexed by varlist:\n",
    "varconv = [1, 1, 1, 86400*365, 1, 1]\n",
    "\n",
    "# Background carbon dioxide (CO2) concentrations\n",
    "CO2levels = ['367ppm', '400ppm']\n",
    "\n",
    "# Number of years of model output to analyze\n",
    "nyrs = 50\n",
    "\n",
    "# Number of parameter sets in ensemble\n",
    "nens = nc4.Dataset(\n",
    "    model_filepath + model_filenames[0]\n",
    "    .format(CO2levels[0], varfiletype[0])).variables[varlist[0]].shape[0]\n",
    "\n",
    "model_data = np.zeros([len(CO2levels), len(varlist), nens, nyrs])\n",
    "for c in range(len(CO2levels)):\n",
    "    for v in range(len(varlist)):\n",
    "        filepath = (model_filepath + model_filenames[c]\n",
    "                    .format(CO2levels[c], varfiletype[v]))\n",
    "        model_data[c, v, :, :] = annmeans.annual_mean_model(\n",
    "            filepath, varlist[v], varfiletype[v], nyrs, varconv[v])\n",
    "        filepath = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Observations\n",
    "\n",
    "This code section loads and preprocesses the data for the observations we will use to evaluate the performance of each parameter set in our FATES parameter ensemble."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Leaf area index\n",
    "\n",
    "Leaf area index observations come from Detto et al. (2018) and were made using hemispherical photographs taken approximately monthly from January 2015 to August 2017 at 188 locations at our test site, Barro Colorado Island, Panama. Data was captured from Detto et al. (2018) Figure 7a using GraphClick software.  We calculate annual mean values from the monthly means reported by Detto et al. (2018). (Note that monthly data consists of spatial means across photograph locations, rather than temporal means.) In order to use all the data available, we calculate two time series of annual means $-$ one starting in from January and the second starting from September. We then choose the time series that allows for the best performance in our performance metric calculations in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return obs_data_lai: 2-D array of annual mean leaf area index\n",
    "# with shape (sample_number, nyrs). Sample_number coded as follows: \n",
    "# 0 = sample months starting from January; and\n",
    "# 1 = sample months starting from September.\n",
    "\n",
    "filepath = 'data/LAI_Detto2018Obs.csv'\n",
    "\n",
    "# Monthly spatial means\n",
    "lai_mthts = np.asarray([col[2] for col in (pd.read_csv(filepath)).values])\n",
    "\n",
    "# Specify start months for observations\n",
    "startmonth_list = np.array([1, 9])\n",
    "\n",
    "# Number of annual means per sample\n",
    "nyears_lai = round(len(lai_mthts)/12 - 0.5)\n",
    "\n",
    "obs_data_lai = np.zeros([len(startmonth_list), nyears_lai])\n",
    "for x in range(len(startmonth_list)):\n",
    "    obs_data_lai[x, :] = np.nanmean(np.reshape(\n",
    "        lai_mthts[startmonth_list[x]-1 : 24+startmonth_list[x]-1],\n",
    "        (nyears_lai, 12)), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Above-ground carbon biomass\n",
    "\n",
    "Above-ground carbon biomass estimates were calculated by Meakem et al. (2018) from 1995 census survey data for our test site, Barro Colorado Island. They estimate above-ground biomass using two different methods (the standard and Chave allometric formulations). We use values from these two methods to represent uncertainty in the observational estimate. \n",
    "\n",
    "Alternatively, we can approximate above-ground carbon biomass from estimates of total biomass (rather than just carbon biomass) from census survey data reproted in Baraloto et al. (2013) and Feeley et al. (2007) for the following years:  1985, 1990, 1995, 2000, and 2005. This alternative method yields similar results and can be implemented by setting use_alt_agb_obs to 1 in the code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return obs_data_agb: a vector of above-ground carbon biomass\n",
    "# (KgC/m2) indexed by allometric formulation as follows:\n",
    "# 0 = standard formulation; and\n",
    "# 1 = Chave formulation.\n",
    "\n",
    "# Specify whether to use (0) Meakem et al. (2018) estimate; or\n",
    "# (1) Baraloto et al. (2013) and Feeley et al. (2007) estimates.\n",
    "use_alt_agb_obs = 0;\n",
    "\n",
    "filepath = 'data/BCI_biomass.csv'\n",
    "\n",
    "if use_alt_agb_obs == 0:\n",
    "    # Above-ground carbon biomass from Meakem et al. 2018 (MgC/ha)\n",
    "    cbiomass_obs_Mgha = np.asarray(\n",
    "        [col[2] for col in (pd.read_csv(filepath)).values])[-2:,]\n",
    "    # Convert from MgC/ha to KgC/m2\n",
    "    ha_to_m2 = 1/10000\n",
    "    Mg_to_kg = 1000\n",
    "    obs_data_agb = cbiomass_obs_Mgha * ha_to_m2 * Mg_to_kg\n",
    "    \n",
    "elif use_alt_agb_obs == 1:\n",
    "    # Total aboveground biomass (Mg biomass/ha) from \n",
    "    # Baraloto et al. (2013) and Feeley et al. (2007)\n",
    "    agb_biomass_obs = np.asarray(\n",
    "        [col[1] for col in (pd.read_csv(filepath)).values])[:-2,]\n",
    "    # Estimate of carbon biomass from total biomass\n",
    "    # using a conversion factor of 0.47 gC/g biomass\n",
    "    # following Meakem et al. 2018\n",
    "    obs_data_agb_v2 = agb_biomass_obs * 0.47\n",
    "    obs_data_agb = obs_data_agb_v2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basal area\n",
    "\n",
    "We use estimates of the median basal area for our test site Barro Colorado Island, Panama, from census surveys conducted in 1999, 2001, 2006, and 2011 by Condit (1998), Condit et al. (2012, 2017), and Hubbell et al. (1999)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return obs_data_ba: vector containing basal area (m^2/ha)\n",
    "# indexed by census year in chronological order.\n",
    "\n",
    "filepath = 'data/census_bmks_bci_171208.nc'\n",
    "\n",
    "# Load basal area median values for the last 5 census dates\n",
    "# Data structured as follows:\n",
    "# [census number, tree diameter size class,\n",
    "# distribution percentiles (0.05,0.5,0.95)]\n",
    "basalarea_bysize = nc4.Dataset(\n",
    "    filepath).variables['basal_area_by_size_census'][-5:, :, 1]\n",
    "\n",
    "# Sum across tree size classes\n",
    "obs_data_ba = np.nansum(basalarea_bysize, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gross primary productivity, latent heat flux, and sensible heat flux\n",
    "\n",
    "Estimates of gross primary productivity, latent heat flux, and sensible heat flux were calculated from fluxtower eddy covariance measurements made from July 2012 to August 2017 at Barro Colorado Island by Koven et al. (_in review_). To use all available data in our analysis, we calculate two versions of the annual means time series, one beginning in July and the second beginning in September. We then choose the time series that allows for the best performance in our performance metric calculations in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return obs_data_flux: 3-D array of annual mean \n",
    "# fluxtower estimates with shape (sample_number, variable, years).\n",
    "# Sample_number is indexed as follows: 0 = sample months \n",
    "# starting from July; and 1 = sample months starting from September.\n",
    "# Variable is indexed as follows: 0 = gross primary productivity;\n",
    "# 1 = latent heat flux; and 2 = sensible heat flux.\n",
    "\n",
    "# Load observations\n",
    "GPP_data = np.load('data/fluxdata_GPP.npy')\n",
    "LH_data  = np.load('data/fluxdata_LH.npy')\n",
    "SH_data  = np.load('data/fluxdata_SH.npy')\n",
    "fluxdata_mask= np.load('data/fluxdata_mask.npy')\n",
    "\n",
    "# Apply mask to arrays\n",
    "GPP_monthyear = np.ma.masked_array(GPP_data, mask=fluxdata_mask)\n",
    "LH_monthyear = np.ma.masked_array(LH_data, mask=fluxdata_mask)\n",
    "SH_monthyear = np.ma.masked_array(SH_data, mask=fluxdata_mask)\n",
    "\n",
    "# Specify start months for observations\n",
    "startmonth_list = np.array([7, 9])\n",
    "\n",
    "# Number of years\n",
    "nyrs_obsflux = len(annmeans.annual_mean_fluxobs(\n",
    "    GPP_monthyear, startmonth_list[0]))\n",
    "\n",
    "obs_data_flux = np.zeros([len(startmonth_list), 3, nyrs_obsflux])\n",
    "for x in range(len(startmonth_list)):\n",
    "    obs_data_flux[x, 0, :] = annmeans.annual_mean_fluxobs(\n",
    "        GPP_monthyear, startmonth_list[x])\n",
    "    obs_data_flux[x, 1, :] = annmeans.annual_mean_fluxobs(\n",
    "        LH_monthyear, startmonth_list[x])\n",
    "    obs_data_flux[x, 2, :] = annmeans.annual_mean_fluxobs(\n",
    "        SH_monthyear, startmonth_list[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we return one list that contains the observed annual mean time series for all variables (i.e., a list of the observed data arrays returned in the code above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return obs_data_list: a list containing all six observed\n",
    "# variable arrays.\n",
    "obs_data_list = [obs_data_lai, obs_data_agb, obs_data_ba,\n",
    "                 obs_data_flux[:, 0, :], \n",
    "                 obs_data_flux[:, 1, :],\n",
    "                 obs_data_flux[:, 2, :]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Quanitfy performance of each parameter set\n",
    "\n",
    "In this section we evaluate the model performance for each parameter set and background carbon dioxide concentration against observations. As we would like to identify parameter sets that robsutly perform well regardless of performance metric we use two metrics to evaluate performance:  error rate and normalized root mean square error (NRMSE). We calculate both metrics for each variable. Then, we take a weighted average across all variables for each metric, parameter set, and background carbon dioxide concentration combination."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance Metric #1: Error Rate\n",
    "\n",
    "The error rate measures the percent of model annual means that fall within the observed range for each variable and ensemble member. To account for relatively small sample sizes and potential measurment error within the observations we extend the observed range by 10% in both directions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return error_rate_array: a 3-D array containing error rates\n",
    "# indexed by (CO2levels, varlist, nens)\n",
    "\n",
    "# Degradation level for the observational range \n",
    "# (as fraction, not percent)\n",
    "dg = 0.10\n",
    "\n",
    "error_rate_array = np.zeros([len(CO2levels), len(varlist), nens])\n",
    "for i in range(len(CO2levels)):\n",
    "        for j in range(len(varlist)):\n",
    "            error_rate_array[i, j, :] = metrics.error_rate(\n",
    "                model_data[i, j, :, :], obs_data_list[j], dg)     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance Metric #2:  Normalized root mean square error (NRMSE)\n",
    "\n",
    "The normalized root mean square error (NRMSE) measures the distance between the model output and the observed mean value, relative to the spread in the observations. The NRMSE is calculated as follows:\n",
    "\n",
    "\\begin{equation}\\Large\n",
    "NRMSE = \\frac{ \\sqrt{ \\sum_{k=1}^n \\frac{(x_{model,k} - \\bar{X}_{obs})^2}{n}}} {x_{obs,max} - x_{obs,min}}\n",
    "\\end{equation}\n",
    "\n",
    "where $NRMSE$ is the normalized root mean square error for a single variable (e.g. leaf area index) and parameter set, $n$ is the number of years of model output, $x_{model,k}$ is the model annual mean for year $k$, $\\bar{X}_{obs}$ is the overall annual mean of the observed values, and $x_{obs,max}$ and $x_{obs,min}$ are the maximum and minimum observed annual mean values, respectively. When mulitple annual mean time series were sampled for an observed variable (e.g. observations for leaf area index spanned a partial year), we calculate the difference between the observed mean and model output using the time series that minimizes this difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return nrsmse_array: a 3-D array containing the NRMSE \n",
    "# indexed by [CO2level, varlist, nens]    \n",
    "\n",
    "nrmse_array = np.zeros([len(CO2levels), len(varlist), nens])\n",
    "for i in range(len(CO2levels)):\n",
    "        for j in range(len(varlist)):\n",
    "            nrmse_array[i, j, :] = metrics.nrmse(\n",
    "                model_data[i, j, :, :], obs_data_list[j])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weighted average permformance metrics across variables\n",
    "\n",
    "We calculate weighted average performance metrics across variables for both error rate and NRMSE. We consider three different weighting approaches to ensure that our selection of high-performing parameter sets is robust to weighting method. The weighting approaches we use are:\n",
    "\n",
    "1. Even:  All variables are evenly weighted.\n",
    "\n",
    "2. Structure:  This weighting favors structural ecosystem properties (leaf area index, above-ground biomass, and basal area). It reflects the likelihood that structural variables at our test site include less measurment uncertainty than flux variables.\n",
    "\n",
    "3. Correlation: This weighting scheme is informed by correlations between individual variable performance metrics. The ability of a parameter set to mach observations of flux variables (gross primary productivity, sensible heat, and latent heat) was correlated with ability to match observations of leaf area index, as well as other flux variables. As leaf area index observations likely include smaller measurement uncertainty, we choose to give a greater weighting to leaf area index at the expense of flux estimates. We also reduced the weightings of basal area and above-ground biomass performance to account for their correlation with one another."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weighted average error rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Even weighting across all variables\n",
    "er_wavg_even = np.average(error_rate_array, axis=1)\n",
    "\n",
    "# Weighted average favoring structural properties\n",
    "ws = 0.3\n",
    "wf = (1-3*ws)/3\n",
    "w_strct = [ws, ws, ws, wf, wf, wf]\n",
    "er_wavg_strct = np.average(error_rate_array, axis=1, weights=w_strct)\n",
    "\n",
    "# Weighting considering correlations between performance metrics\n",
    "w1 = 0.4\n",
    "w2 = 0.25\n",
    "w3 = 0.1/3\n",
    "w_corr = [w1, w2, w2, w3, w3, w3]\n",
    "er_wavg_corr = np.average(error_rate_array, axis=1, weights=w_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Weighted average NRMSE\n",
    "\n",
    "We quantify the distance of model output from the mean observations in multivariate space by calculating the weighted Euclidean distance as follows:\n",
    "\n",
    "\\begin{equation}\n",
    "NRMSE_{avg} = \\sqrt{ \\sum_{i=1}^m \\omega_{i} \\cdot NRMSE_{i}^2}\n",
    "\\end{equation}\n",
    "\n",
    "where $NRMSE_{avg}$ is the weighted average NRMSE across variables, $m$ is the number of variables, and $NRMSE_{i}$ and $\\omega_{i}$ are the individual variable NRMSE and weighting, respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Even weighting across all variables\n",
    "nvars = nrmse_array.shape[1]\n",
    "w = 1/nvars\n",
    "w_even = np.matlib.repmat(w,nvars,1)\n",
    "nrmse_wavg_even = metrics.avg_nrmse(nrmse_array, w_even)\n",
    "\n",
    "# Weighted average favoring structural properties\n",
    "nrmse_wavg_strct = metrics.avg_nrmse(nrmse_array, w_strct)\n",
    "\n",
    "# Weighting considering correlations between performance metrics\n",
    "nrmse_wavg_corr = metrics.avg_nrmse(nrmse_array, w_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, we return one array that contains the weighted average values for all combinations of weighting approach and performance metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_avg_array = np.transpose(np.stack([\n",
    "    er_wavg_even, er_wavg_strct, er_wavg_corr,\n",
    "    nrmse_wavg_even, nrmse_wavg_strct, nrmse_wavg_corr]),\n",
    "    (1,0,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Rank parameter sets by performance\n",
    "Here we assign an overall rank to each parameter set based on its performance across two performance metrics (error rate and NRMSE), three weighted averaging approaches (even, structure, and correlated), and two background carbon dioxide concentrations (367 ppm and 400 ppm). High-performing parameter sets (i.e., parameter sets that robustly performed well at our test site) are indicated by a low numbered rank."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highest-performing parameter set numbers:  [ 86 260 151]\n"
     ]
    }
   ],
   "source": [
    "# Assign each simulation a rank for \n",
    "# each weighted average performance metric\n",
    "rank_array = stats.mstats.rankdata(all_avg_array, axis=2)\n",
    "\n",
    "# Sum ranks across performance metrics and\n",
    "# background carbon dioxide concentrations\n",
    "sum_rank_array = np.nansum(np.nansum(rank_array, axis=0), axis=0)\n",
    "\n",
    "# Sort parameter set indexes by their summed rank\n",
    "# (best to worst performance)\n",
    "sum_rank_index = np.argsort(sum_rank_array)\n",
    "\n",
    "#Print for highest-performing parameter set numbers\n",
    "highperform_num = np.transpose(sum_rank_index)[:10,] + 1\n",
    "print(\"Highest-performing parameter set numbers: \", highperform_num[0:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Result:__ Parameter sets 86, 260, and 151 resulted in the highest model performance (in descending order) at our test site.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Plot performance metrics for each parameter set\n",
    "\n",
    "In this section we visualize the model performance for each parameter set to gain insight into how the highest-performing parameter sets performed in individual variables and in comparison to all other parameter sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return error_heatdata and nrmse_heatdata: two 3-D arrays\n",
    "# containing error rates and NRMSE values, respectively,\n",
    "# indexed by (CO2level, variables, nens). Variables indexed \n",
    "# as follows: \n",
    "# 0-5, variables in order of varlist; and \n",
    "# 6-8 = weighted averages across variables using even,\n",
    "#    structure, and correlation weights, respectively\n",
    "\n",
    "error_heatdata = np.concatenate(\n",
    "    [error_rate_array, all_avg_array[:, :3, :]], axis=1)\n",
    "nrmse_heatdata = np.concatenate(\n",
    "    [nrmse_array, all_avg_array[:, 3:, :]], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for simulations run at 367 ppm carbon dioxide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a0b60de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set carbon dioxide case to 367 ppm\n",
    "casenum = 0\n",
    "\n",
    "# Plot Error Rate and NRMSE\n",
    "fig1 = plt.figure(figsize=(12, 12))\n",
    "\n",
    "plotnum = 1\n",
    "plotmetrics.heatmap_subplot(error_heatdata, casenum, 0, 100, \n",
    "                            plotnum, 'A.  Error Rate (%)', highperform_num)\n",
    "\n",
    "plotnum = plotnum + 1\n",
    "plotmetrics.heatmap_subplot(nrmse_heatdata, casenum, 0, 10,\n",
    "                            plotnum, 'B.  NRMSE', highperform_num)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 1.__ FATES model performance as measured by (A) error rate and (B) normalized root mean square error (NRMSE) for simulations run with 367 ppm atmospheric carbon dioxide. Performance metrics are shown for leaf area index (LAI), above-ground biomass (AGB), basal area (BA), gross primary productivity (GPP), latent heat flux (LH), sensible heat flux (SH), and weighted averages across variables using three weighting approaches:  even (Av$_E$), favoring structural variables (Av$_S$), and considering correlations between individual performance metrics (Av$_C$). Top panels highlight the performance of the 10 highest-perfoming parameter sets. Bottom panels show the performance of all 287 parameter sets we tested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot for simulations run at 400 ppm carbon dioxide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AQL2oVwCAbNQaIniQpGNUmYlpf0nfkjTd9iW2P9ykfAAA1ES9AgDkpNYsgtdKuk2VGyueExFLbN8t6d8lHdykfAAA1ES9AgDkpNYQwSMl/V7SGEk/sv0nSdtLequkh5qQDQCAelCvAADZ6PUMVkSslnS97Wcj4s2SZHuupL9Ierek2c2J2LvH1o3VcY+/JnWMmn664w2pIxSaNX9O8UqJHTZlRuoIhd798F9SR6jpqof3TB2h0E5b5T/R24e3vzF1hEJLO0enjlDo5yXuqxXq1dpo16Prtkwdo6Z/GrkmdYRCT3/6lakjFDp6zq6pIxT65ksvSR2hpvVR6/3/POy229OpIxRauN2Y1BEKbda+PnWEQg/3Y5ta98Hq9tYen98cEZdKurQfxwIAoJGoVwCA5ArfIoiIx3p8zsXCAIAsUa8AADmodR+s/2t7Qo3nD7X9psbEAgCgPtQrAEBOag0RnCvp17ZflHSXpIWSRkiaJmmGpN9K+mLDEwIAUBv1CgCQjVqTXPxK0q9sT5N0oKTJkpZL+omkEyIi/6thAQCbPOoVACAnhZNcRMQjkh5pQhYAAPqNegUAyEH+82ACAAAAQIugwQIAAACAktBgAQAAAEBJChss21+1Pc52u+3rbb9g+/g6tjvf9vO27+ux7Azbz9ieU/14Q3X5RNs32F5p+9sD+y8BAAYj6hUAIAf1nMH654hYLulNkp6WNF3SJ+rY7oeSDt/I8rMjYkb14zfVZS9K+g9J/1bHfgEA2BjqFQAguXoarPbqv2+QdHFELK5nxxHxe0n1rrsqIm5WpXABANAf1CsAQHL1NFi/tv2QpJmSrrc9SQMrLB+zfW91SMb4AewHAICeqFcAgOTqabBOl/QKSTMjYr2k1ZKO6OfxvitpZ0kzJC2QdGZfd2D7BNuzbc9eu4Q3EAEAf5VtvVq5ZF0/YwAAWk09DdZtEbEkIjqlyvAISVf352AR8VxEdEZEl6QfSNq/H/s4NyJmRsTM4eNH9CcGAGDTlG29GjN+WH9iAABa0NDenrC9taRtJI20vY8kV58aJ2lUfw5me3JELKg+PErSfbXWBwCgCPUKAJCTXhssSYdJeq+kqZLO6rF8uaTPFO3Y9sWSDpG0he2nVRm6cYjtGZJC0hOSPthj/SdUKYbDbB+pymxQD9T/XwEADFLUKwBANnptsCLiQkkX2n5rRFzW1x1HxLEbWXxejfV36OsxAACgXgEAclLPNVi32D7P9tWSZHt32//a4FwAAPQV9QoAkFw9DdYFkmZJmlJ9PE/SyQ1LBABA/1CvAADJ1dNgbRERP5PUJUkR0SGps6GpAADoO+oVACC5ehqsVbYnqnKhr2wfIGlZQ1MBANB31CsAQHK1ZhHsdqqkKyTtbPsWSZMkHd3QVAAA9B31CgCQXGGDFRF32X61pF1UubfIwxGxvuHJAADoA+oVACAHvQ4RtL1f9eaN3ePY95X0BUln2p7QpHwAANREvQIA5KTWGazvS3qdJNk+WNKXJX1c0gxJ5yqDYRcdD3Vq0YFLUseobX7qAJuGWfPnpI7Q8n50XP5fw2W/eUnqCIXO+vM/pY5Q6LnnNksdoQ6/L3Nn2der51aP1dlzXps6Rk1vO+g7qSMU6mpPnaDYrJf1egu1bMzvqOcKkXSuXLF36giFOrvqmcYgrRWrh6eOUGiF8s/YH7V+w9oiYnH183dIOrd6A8fLbOf/lxoAYLCgXgEAslGr/W6z3d2AvVbS73o8l/dbHwCAwYR6BQDIRq3Cc7Gkm2y/IGmNpD9Iku2XiGlvAQD5oF4BALLRa4MVEV+wfb2kyZKujYioPjVElbHtAAAkR70CAOSk5tCJiPjjRpbNa1wcAAD6jnoFAMhF/lOgAAAAAECLoMECAAAAgJLUbLBst9n+bbPCAADQH9QrAEAuajZYEdEpabXtVrhrJQBgkKJeAQByUc/9QV6UNNf2dZJWdS+MiBMblgoAgL6jXgEAkqunwbqq+lEa26dIer+kkDRX0vskfU/Sq/W3e5a8NyLmlHlcAMAmjXoFAEiusMGKiAttj5S0XUQ8PNAD2t5G0omSdo+INbZ/JumY6tOfiIhLB3oMAMDgQ70CAOSgcBZB22+WNEfSNdXHM2xfMcDjDpU00vZQSaMkzR/g/gAAgxz1CgCQg3qmaT9D0v6SlkpSdRjEjv09YEQ8I+nrkp6StEDSsoi4tvr0F2zfa/ts28M3tr3tE2zPtj17vdb2NwYAYNNzhjKtV50rVm1sFQDAJqieBqsjIpZtsCz6e0Db4yW9RZWiN0XSaNvHS/q0pF0l7SdpgqRPbmz7iDg3ImZGxMx2bbSmAQAGp2zrVdvY0f2NAQBoMfU0WPfZPk5Sm+1pts+RdOsAjvk6SY9HxMKIWC/pckmvjIgFUbFW0gWqvAsJAEC9qFcAgOTqabA+LmkPSWsl/VSVWZNOGsAxn5J0gO1Rti3ptZIetD1ZkqrLjpR03wCOAQAYfKhXAIDk6pmm/Y0R8VlJn+1eYPttkn7enwNGxO22L5V0l6QOSXdLOlfS1bYnSbIqFyl/qD/7BwAMWtQrAEBy9TRYn9Y/FqeNLatbRJwu6fQNFh/a3/0BACDqFQAgA702WLZfL+kNkrax/a0eT41T5Z08AACSo14BAHJS6wzWfEmzJR0h6c4ey1dIOqWRoQAA6APqFQAgG702WBFxj6R7bP+0ut52EfFw05IBAFAH6hUAICf1zCJ4uCoX8V4jSbZn2L6ioakAAOg76hUAILl6GqwzVLnHx1JJiog5knZoXCQAAPrlDFGvAACJ1dNgdUTEsoYnAQBgYKhXAIDk6pmm/T7bx0lqsz1N0omSbm1srDpNb5e+NzV1igJzUgcodNiUGakjFPJ+L00dodA1v/px6gg1te02LXWEQgsfmpg6QqGusZ2pIxRye1fqCKlkW6+2GLlK/7Lnbalj1PRiROoIhbb7zyy+nTX95O3516ujx92bOkJNW7YvTx2h0KKLt00dodBONz6bOkKhjq02Sx2h0CP92KaeM1gfl7SHpLWSLpa0XNLJ/TgWAACNRL0CACRXeAYrIlZL+mz1AwCALFGvAAA5KGywbM+U9BlVLhT+6/oRsVfjYgEA0DfUKwBADuq5BusiSZ+QNFfSoB3YDwDIHvUKAJBcPQ3WwojgPiIAgNxRrwAAydXTYJ1u+38kXa/KhcOSpIi4vGGpAADoO+oVACC5ehqs90naVVK7/jbkIiRRsAAAOaFeAQCSq6fB2jsi8r+pAwBgsKNeAQCSq+c+WH+0vXvDkwAAMDDUKwBAcvWcwXqVpPfYflyVMe2WFEx7CwDIDPUKAJBcPQ3W4WUf1PZJkj6gSvH7QUR8w/YZ1WULq6t9JiJ+U/axAQCbLOoVACC5wgYrIp6UJNtbShox0APa3lOVwrS/pHWSrrF9VfXpsyPi6wM9BgBg8KFeAQByUHgNlu0jbD8i6XFJN0l6QtLVAzjmbpL+GBGrI6Kjus+jBrA/AACoVwCALNQzycV/STpA0ryI2FHSayXdMoBj3ifpYNsTbY+S9AZJ21af+5jte22fb3v8xja2fYLt2bZnr1+2ZgAxAACbmGzr1aol6wYQAwDQSuppsNZHxCJJQ2wPiYgbJM3o7wEj4kFJX5F0naRrJN0jqUPSdyXtXN33Akln9rL9uRExMyJmtm82sr8xAACbnmzr1ejxw/obAwDQYuppsJbaHiPp95Iusv1NVQpMv0XEeRHxsog4WNJiSY9ExHMR0RkRXZJ+oMqYdwAA6kW9AgAkV0+D9RZJqyWdoso7eI9KevNADlq9AFm2t5P0fyRdbHtyj1WOUmVoBgAA9aJeAQCSqzmLoO02Sb+KiNdJ6pJ0YUnHvcz2REnrJX00IpbY/rHtGZJClQuTP1jSsQAAmzjqFQAgFzUbrIjotL3a9mYRsaysg0bEQRtZ9q6y9g8AGFyoVwCAXNRzo+EXJc21fZ2kVd0LI+LEhqUCAKDvqFcAgOTqabCuqn4AAJAz6hUAILnCBisiyhrHDgBAw1CvAAA5KGywbE+T9CVJu0sa0b08InZqYC4AAPqEegUAyEE907RfoMpNFTskvUbSjyT9uJGhAADoB+oVACC5ehqskRFxvSRHxJMRcYakQxsbCwCAPqNeAQCSq2sWQdtDJD1i+2OSnpG0ZWNj1Wf6iGWatduVqWPUdNiUGakjFLrs6T+mjlDorVNTJyiW+/d68ZVOHaHQsNn1vOeTVkxdVbxSYpuPWZM6QqEnG7PbbOvVpKGr9eHxd6eOUdMFy/ZKHaHQ0B23Tx2h0MOrn08dodBX17w2dYSajp5wR+oIhZYdmv/r7NLdtkodoVDn6K7UEYrd3PdN6vlr5mRJoySdKGlfScdLek/fDwUAQENRrwAAydU8g2V7kip3qh8aEU9Lel9TUgEA0AfUKwBALno9g2X7/ZLul3SOpIdsH9G0VAAA1Il6BQDISa0zWCdL2iMiFtreSdJFkq5oTiwAAOpGvQIAZKPWNVjrImKhJEXEY5KGNycSAAB9Qr0CAGSj1hmsqba/1dvjiDixcbEAAKgb9QoAkI1aDdYnNnh8ZyODAADQT9QrAEA2em2wIuLCZgYBAKA/qFcAgJzkf1dPAAAAAGgRNFgAAAAAUJKGNVi2t7V9g+0Hbd9v+6Tq8jNsP2N7TvXjDdXlw2xfYHuu7XtsH9KobAAAdKNeAQDKVGuSC0nSBjMzdVsmaXZE/KrGph2STouIu2yPlXSn7euqz50dEV/fYP0PSFJEvNT2lpKutr1fRHQV/zcAAIMd9QoAkIN6zmCNkDRD0iPVj70kTZD0r7a/0dtGEbEgIu6qfr5C0oOStqlxnN0lXV9d/3lJSyXNrCMfAAAS9QoAkIF6GqyXSDo0Is6JiHMkvU7SbpKOkvTP9RzE9g6S9pF0e3XRx2zfa/t82+Ory+6R9BbbQ23vKGlfSdtuZF8n2J5te/bCRZ31HB4AMDhkW68WLeLkFgAMFvU0WNtIGt3j8WhJUyKiU9Laoo1tj5F0maSTI2K5pO9K2lmVdxkXSDqzuur5kp6WNFvSNyTdqsqwjb8TEedGxMyImDlpYlsd8QEAg0S29WriROaUAoDBovAaLElflTTH9o2SLOlgSV+0PVrSb2ttaLtdlWJ1UURcLkkR8VyP538g6crq8g5Jp/R47lZVhngAAFAP6hUAILnCBisizrP9G0n7q1KwPhMR86tPf6K37Wxb0nmSHoyIs3osnxwRC6oPj5J0X3X5KEmOiFW2/0lSR0Q80J//FABg8KFeAQByUM8ZLKkylHBhdf2X2H5JRPy+YJsDJb1L0lzbc6rLPiPpWNszJIWkJyR9sPrclpJm2e6S9Ex1WwAA+oJ6BQBIqp5p2r8i6R2S7pfUfZVuSKpZsCLiZlXeQdzQb3pZ/wlJuxTlAQBgY6hXAIAc1HMG60hJu0RE4QXCAAAkRL0CACRXz7RGj0lqb3QQAAAGiHoFAEiunjNYq1WZlel69ZjmNiJObFgqAAD6jnoFAEiungbriuoHAAA5o14BAJKrZ5r2C5sRBACAgaBeAQBy0GuDZftnEfF223NVmYXp70TEXg1NBgBAHahXAICc1DqDdVL13zc1IwgAAP1EvQIAZKPXBqv77vUR8aQk2R5Xa/0U5t07SodNmZE6Rk2z5s8pXim5EakDFHriC69IHaHQKUflfenHQ2uy+vXdqLtesT51hELru+qZfDWtZ5/dPHWEpmqFevXnNRP0lgfemTpGTV+adlnqCIXOf/vhqSMUevOY2akjFJrSviR1hJomta1KHaHQtMnPp45Q6IlhE1JHKNTW1lW8Uguq50bDH5T0n5LW6G9DL0LSTg3MBQBAn1CvAAA5qOcdvn+TtEdEvNDoMAAADAD1CgCQXD1jXR5V5d4iAADkjHoFAEiunjNYn5Z0q+3bxY0bAQD5ol4BAJKrp8H6vqTfSZoradO8Eg0AsCmgXgEAkqunweqIiFMbngQAgIGhXgEAkqvnGqwbbJ9ge7LtCd0fDU8GAEDfUK8AAMnVcwbruOq/n+6xjGlvAQC5oV4BAJKr2WDZHiLp+Ii4pUl5AADoM+oVACAXNYcIRkSXpK/3Z8e2z7f9vO37eiz7mu2HbN9r+xe2N68u38HkcJ9PAAAgAElEQVT2Gttzqh/f688xAQCDE/UKAJCLeq7Butb2W227j/v+oaTDN1h2naQ9I2IvSfP098M4Ho2IGdWPD/XxWAAAUK8AAMnVcw3WqZJGS+q0vUaSJUVEjKu1UUT83vYOGyy7tsfDP0o6uk9pAQDoHfUKAJBc4RmsiBgbEUMioj0ixlUf1yxWdfoXSVf3eLyj7btt32T7oN42qs4QNdv27PV/u48kAGCQy7ledSxbU0IMAEArqOcMlmwfIeng6sMbI+LKgRzU9mcldUi6qLpogaTtImKR7X0l/dL2HhGxfMNtI+JcSedK0jhPiIHkAABsWnKtV2Omb029AoBBovAMlu0vSzpJ0gPVj5Oqy/rF9nskvUnSOyMiJCki1kbEournd0p6VNL0/h4DADD4UK8AADmo5wzWGyTNqM7QJNsXSrpb0qf6ejDbh0v6pKRXR8TqHssnSVocEZ22d5I0TdJjfd0/AGBQo14BAJKrZxZBSdq8x+eb1bOB7Ysl3SZpF9tP2/5XSd+WNFbSdRtMb3uwpHtt3yPpUkkfiojFdWYDAKAb9QoAkFQ9Z7C+JOlu2zeoMiPTwfr76Wo3KiKO3cji83pZ9zJJl9WRBQCA3lCvAADJ9dpg2T4wIm6RdLmkGyXtp0rB+mREPNuceAAA1Ea9AgDkpNYZrG9J2lfSbRHxMklXNCcSAAB9Qr0CAGSjVoO13vYFkqba/taGT0bEiY2LBQBA3ahXAIBs1Gqw3iTpdZIOlXRnc+IAANBn1CsAQDZ6bbAi4gXbP5c0JSIubGImAADqRr0CAOSk5jTtEdEp6c1NygIAQL9QrwAAuahnmvZbbX9b0v9KWtW9MCLualgqAAD6jnoFAEiungbrldV//7PHslBlrHtS0/darVmz5qSO0fIOmzIjdYRCD8//buoILe+N/5z/93njdx5CX+2909OpIxR6qjG7zbZebTdisb69y8WpY9S017ARqSMU2uYrt6aOUOigD/85dYRCo9yZOkJNs1btkjpCoXWf2zp1hELb3pj/e0seWk8rktYD/dim8H8VEa/px34BAGgq6hUAIAc1r8GSJNtb2T7P9tXVx7vb/tfGRwMAoH7UKwBADgobLEk/lDRL0pTq43mSTm5UIAAA+umHol4BABKrp8HaIiJ+JqlLkiKiQ1Leg3cBAIMR9QoAkFw9DdYq2xNVuVBYtg+QtKyhqQAA6DvqFQAguXqm7jhN0hWSdrZ9i6RJko5uaCoAAPqOegUASK6eWQTvtP1qSbtIsqSHI2J9w5MBANAH1CsAQA56HSJoe5rtX9m+T9KPJS2NiPsoVgCAnFCvAAA5qXUN1vmSrpT0Vkl3STqnKYkAAOgb6hUAIBu1GqyxEfGDiHg4Ir4maYe+7Nj2+bafr76j2L3sDNvP2J5T/XhDdXm77Qttz7X9oO1P9+t/AwAYjAZUryRqFgCgPLWuwRphex9VxrFL0siejyPiroJ9/1DStyX9aIPlZ0fE1zdY9jZJwyPipbZHSXrA9sUR8UQd/wcAwOA20HolUbMAACWp1WAtkHRWj8fP9ngckg6tteOI+L3tHerMEZJG2x4qaaSkdZKW17ktAGBwG1C9kqhZAIDy9NpgRcRrGnTMj9l+t6TZkk6LiCWSLpX0FlWK5ChJp0TE4gYdHwCwCWlgvZKoWQCAPqrnRsNl+q6knSXNUKUwnVldvr+kTklTJO0o6TTbO21sB7ZPsD3b9uyFizqbEBkAMEgNqGb1rFdLF3c1KTIAILWmNlgR8VxEdEZEl6QfqFKkJOk4SddExPqIeF7SLZJm9rKPcyNiZkTMnDSxrTnBAQCDzkBrVs96tfmEZr+fCQBIpamv+LYn93h4lKTu2ZqeknSoK0ZLOkDSQ83MBgBAT9QsAEB/1Jrk4q9sbyNp+57rR8TvC7a5WNIhkraw/bSk0yUdYnuGKhcIPyHpg9XVvyPpAlWKlyVdEBH39uU/AgBAf+pVdTtqFgCgFIUNlu2vSHqHpAdUGXMuVYpNzYIVEcduZPF5vay7UpVpbwEA6Jf+1iuJmgUAKE89Z7COlLRLRKxtdBgAAAaAegUASK6ea7Aek9Te6CAAAAwQ9QoAkFyvZ7Bsn6PK0IrVkubYvl7SX98VjIgTGx8PAIDaqFcAgJzUGiI4u/rvnZKuaEIWAAD6g3oFAMhGrw1WRFzYzCAAAPQH9QoAkJN6ZhGcq8rQi56WqfKO4ecjYlEjggEA0BfUKwBADuqZRfBqVaa7/Wn18TGq3PdjmaQfSnpzQ5IBANA31CsAQHL1NFgHRsSBPR7PtX1LRBxo+/hGBavHvHtH6bApM1JG2CTMmj8ndYRNwrz1q1JHqOn5741OHaHQ0kWjUkcoNPLO/DP+ZeWWqSOkkm29enzhVjr2+6emjFDoP957ceoIhR751stTRyi0Pm5PHaHQc13DUkeo6YCRj6WOUOgnn1qaOkKh+e/dN3WEQrGungnNE/vAJX3epJ7/1Rjbf31Fs72/pDHVhx19PiIAAI1BvQIAJFfPGaz3Szrf9hhVhlosl/R+26MlfamR4QAA6APqFQAgucIGKyLukPRS25tJckT0PCf6s4YlAwCgD6hXAIAc1LrR8PER8RPbp26wXJIUEWc1OBsAAIWoVwCAnNQ6g9V9RfzYZgQBAKCfqFcAgGzUutHw96v/fq55cQAA6BvqFQAgJ7WGCH6r1oYRcWL5cQAA6BvqFQAgJ7WGCN7Z4/PPSTq9wVkAAOgP6hUAIBu1hghe2P257ZN7PgYAIBfUKwBATuq9fXI0NAUAAOWgXgEAkqrnRsOlsj1C0u8lDa8e/9KION32H/S3GaC2lPSniDiy2fkAAOhGzQIA9FWtSS5W6G/vBI6yvbz7KUkREeP6ecy1kg6NiJW22yXdbPvqiDiox7Evk/Srfu4fADCINLBeSdQsAEAf1boGqyH3E4mIkLSy+rC9+vHXIR22x0o6VNL7GnF8AMCmpVH1qrpvahYAoE/qvQarVLbbbM+R9Lyk6yLi9h5PHyXp+ohY3su2J9iebXv2eq1tRlwAwCDW35rVs151rF7VrLgAgMSSNFgR0RkRMyRNlbS/7T17PH2spItrbHtuRMyMiJntGt7oqACAQa6/NatnvRo6anQzogIAMpCkweoWEUsl3SjpcEmyPVHS/pKuShgLAIB/QM0CANSj6Q2W7Um2N69+PlLS6yQ9VH36bZKujIgXm50LAIANUbMAAH3V9GnaJU2WdKHtNlUavJ9FxJXV546R9OUEmQAA2BhqFgCgT5reYEXEvZL26eW5Q5qbBgCA3lGzAAB9lfQaLAAAAADYlNBgAQAAAEBJaLAAAAAAoCQ0WAAAAABQEhosAAAAACgJDRYAAAAAlIQGCwAAAABKkuJGw4PKrPlzUkcodNiUGakjFGqFr+P09tGpI9Q04U3zUkcotPisA1JHKDRkfeoExVohI/JzzNglqSMUuuDE21NHKLTVkfn/Ai7t6kwdoabfrtotdYRC6y7eKnWEQrtd+3jqCIVi/LjUEQo91Y9tOIMFAAAAACWhwQIAAACAktBgAQAAAEBJaLAAAAAAoCQ0WAAAAABQEhosAAAAACgJDRYAAAAAlIQGCwAAAABKQoMFAAAAACVpWINle1vbN9h+0Pb9tk+qLn9b9XGX7Zk91t/f9pzqxz22j2pUNgAAulGvAABlGtrAfXdIOi0i7rI9VtKdtq+TdJ+k/yPp+xusf5+kmRHRYXuypHts/zoiOhqYEQAA6hUAoDQNa7AiYoGkBdXPV9h+UNI2EXGdJNnecP3VPR6OkBSNygYAQDfqFQCgTE25Bsv2DpL2kXR7wXovt32/pLmSPrSxdwNtn2B7tu3Z67W2EXEBAINUo+pVx+pVjYgLAMhQwxss22MkXSbp5IhYXmvdiLg9IvaQtJ+kT9sesZF1zo2ImRExs13DGxMaADDoNLJeDR01ujGhAQDZaWiDZbtdlWJ1UURcXu92EfGgpFWS9mxUNgAAulGvAABlaeQsgpZ0nqQHI+KsOtbf0fbQ6ufbS9pF0hONygcAgES9AgCUq5GzCB4o6V2S5tqeU132GUnDJZ0jaZKkq2zPiYjDJL1K0qdsr5fUJekjEfFCA/MBACBRrwAAJWrkLII3S3IvT/9iI+v/WNKPG5UHAICNoV4BAMrUlFkEAQAAAGAwoMECAAAAgJLQYAEAAABASWiwAAAAAKAkNFgAAAAAUBIaLAAAAAAoCQ0WAAAAAJTEEZE6Q7/ZXijpydQ5AAB9tn1ETEodolmoVwDQsvpcr1q6wQIAAACAnDBEEAAAAABKQoMFAAAAACWhwQIAAACAktBgAQAAAEBJaLAAAAAAoCQ0WAAAAABQEhosAAAAACgJDRYAAAAAlIQGCwAAAABKQoMFAAAAACWhwQIAAACAktBgAQAAAEBJaLAAAAAAoCQ0WAAAAABQEhosAAAAACgJDRYAAAAAlGRo6gADscWEtthh2/bUMWqad++o1BHQJKN3j9QRalr1gFNHKNQ5cXTqCIUi/y+juvJ+WZQkrV3w9AsRMSl1jmYZP2FIbDM175L7yOKtUkcoNPyZVakjFFq748jUEQrtOOaF1BFqWtGZ/9dwxQOcoxgsVmhJn+tV3q/2BXbYtl1/mrVt6hg1HTZlRuoIaJL9Lu5MHaGmO2a0pY5QaOkbX5E6QqHO4akTFFs9Of8u8OH/PPXJ1BmaaZupQ3XpVVukjlHTGy8+JXWEQjt96rbUEQo99sX86/75B5yXOkJNv125R+oIhf6w14jUEdAkv41L+1yvWrrBmnfvqOwbmFnz56SOUCj3r6EkDZ26TeoIhT6/5VWpI9T0xqlvTB2h0NrN828M2tblfaZSkjZ7tCt1BGxgwfpx+vz816eOUdMvjzkrdYRCp34q/zdh3rPnH1NHKLRV25rUEWrasn156giFlh1/SOoIhSZc91jqCIVi64mpIxTrx5/yLd1gtYJWaF5aQcfTz6SOUCj/73X+X8Otzsk/I9AfK9eO0M2PvCR1jJru+uWeqSMU2ka3po5Q6KLLD00dodCKt+R99uXRlXmf7ZWkzX6SfyOd97iaqueeT52gIRhACgAAAAAl4QwWUJKRN+V9gfiaVz+XOkKhtt2mpY5QKB7/S+oIm4a8RyiVbmhbp7aYuCJ1jJpWjMz7rEareHHrjtQRCr1xs7wvX7jWL00dodAdyv+6ZqTT0g3W9L1Wa9asvF8k8h82hrL8x3a/Th2hps9o/9QRCi18Rf7DQuJV+Wdc1QKTXOhzP0mdoKl2GbVYf5jx09Qxatp1/kdSR9gknHTQtakjFBrh9akj1DRqyLrUEeqQ/0yHSIchggAAAABQkpY+g8UsguXI/WsotcbXcd76vN8RXHzl9NQRCi1dnv+4sZF35n9vu1HP5T/T4WDz8OoJetXd70wdo6ZbDzs7dYRC79WrUkco9NIR+Q8j7oq8319/w9h7U0codO3rP5Y6QqHhV9+ROkKhts03Sx2h2JK+b+KI1i3EM/ceEdwHa+BaoXlpha+jh+d9g6RYuzZ1hEIvfDD/KZiXvzr/JnDsmPwz3vPmL9wZETNT52iWGXsPi+t/k/d9lX++Mu9ZDiVp1+HzU0co9MjarVNHKPRCx9jUEWq6bfFOqSMUeu0WD6WOUGj7YQtTRyjU5vxvK3LEzvf1uV7l/RYGAAAAALQQhgg2GGeHBo/lR+2TOkJNS966KnWEQmOuTp2g2GY35H9h87AVzAaXm0fWTNTr5747dYyabtr74tQRCh2xzX6pIxSadkfeoxkkadKwvGe0PGO7K1JHKPTxU09MHaHQqF/NTh2h0NBtp6SOUIf7+rxFSzdYzCKInNx61vdSR6jpjfu/MXWEQgvePCZ1hEItMJpBS3ZhcEJuJg9fps9M+03qGDXt+uuPpo5QaLr+lDpCofWZX98kSe/ePO+v43Wr879meNQvbk8dYZPQ8WT+1yz2R/6vAgAAAADQIlr6DFYrDBFEOVphqOXrX39s6gg1dT39YOoIhSY+sGXqCIWG3vXn1BE2CfNSB2iy59aN09lP/FPqGDW9bp/7U0co9FTqAHU4e8pNqSMUOnfZnqkj1DR72Q6pI9RhaeoAyBhnsAAAAACgJC19BgvlaIWzQxetmJg6QqFdzn8kdYQC+f+6/+re/N/zGbvvS1NHKDRsRQvcfuPc1AGaq81dGjss71slvHnC3akjFDrp/ONSRyh07Zr8z3JPae/HjX2a6LNTHkgdodA5dxyaOkKhJ1ZOSB2h0BC3QL16dd83yf8vLjQcwyzL0pE6QMubrjtTRwAa4sWVw/XwzTumjlHTp9bkf++h6Z+/NXWEQqd8N/8mcJ89Hk8doaazV41LHaHQuNc/mjpCHfK/b1xn6gANkv/bxQAAAADQIjiDBQBAgw17ZpV2+OxtqWPUtOC0V6aOsEnY9TvLU0co9NAZW6WOUJNbYNhY/ufYkBJnsAAAAACgJDRYAAAAAFAShgg2WCvM0NcKk1y0wtcxdy//5IdTRyj0/EH5TxQy7sH21BEKtbfCLII/uDR1gqZaN2W0nvjoK1LHqOl/33l26giFPnnmy1NHKHTWleenjlBo7JCu1BFquv3FKakjFPr3b+Q/mclmDzl1hEJdw/LPqG/2vV61dIM1fo/1OurShalj1NQKzUsr4Os4cJsr7+s/JGnCvbuljlBo5U75j7xfvSWDE3Kz9fgl+vTRl6WOUdM7f3BK6giFpir/WQQ/9udjUkco9M9b5X3j+bkrtkkdodBLTv5j6ghokrn92IYqDAAAAAAlaekzWEvub9cvdp+UOgaagCGCA/fqD56QOkKhpTvn/5I09edPpI5QKP9zbIPPc4vG62s/Ojp1jJriZfnPftcKvrRz3mcqJWlKW943vb5p2OLUEQr9SNumjoCM5f/XTA2jdw/td3Hetyi7Y0Zb6gibhFYYIvjRR+aljlDTiF//KXWEQqOPOSB1hEJLX5l/UV09qQVed76TOkBzbTVxiT7x7ryvO/vqRXk3gK3i/z1+ZOoIhc7Y4YrUEWp6et2E1BGAAWGIIAAAAACUpKXPYK16wJwhGiR2uzP/H9UjRq9OHaGm/3tq/jcRHbI+dYJi21z6WOoIhTZPHaAO96YO0GTPvThOZz302tQxanpx57yHjbWKk7f7beoIhaYOXZM6Qk37jHwidYRCv9MeqSMgY5zBAgAAAICS5H9aAJD04L753x/pMOV9ndjkFpjeuBXk/5OIHLU/+qImH5n31NgLTsv/LHcr+OaRR6WOUOjJM/L+88/O/15+U3V/6gjIWN6/YUAVswgOXCtMFPLCCXnfiFWStr4i/yGCLWFB6gDNFdOHad1/b586Rk3tL76QOkKxM1MHKHbJNRekjlBofkfeDczVK/dMHaHQLOZrRQ0MEQQAAACAknAGCy3hNf/ygdQRCt1w/g9SR6hp6NRtUkcoNO7J/Ge5iBdfTB0BLaijc4gWrRqVOkZNw4fmfduTVnHu0vzPvrxj3D2pI9S0WVvek0ZVcAYLvWvpBmv6Xqs1a1beQ8d2vuRDqSMUesmpf0wdoVDuzYsk3bl2XeoINT34qampIxSauMOS1BEKPXHkLqkjFGof3wJN4DtSB2iu7Uct0rl7/SR1jJqWd41IHaHQmS0wc9urRz+UOkKhZzuHp45Q00tH/CV1hEI/09apIyBjDBEEAAAAgJK09Bmsh56epFeemvcZopdckv/ZIZTjzPmHpY5Q07SP3Z46QqEXPpj/JBe73r4sdYRCnSPbU0co9GjqAE22qGOMLnjhValj1LRs/cjUEeqwOHWAQpOG5H8/sfmdeQ9XvXr53qkjFGqFYfcdTz+TOsKg1fQGy/a2kn4kaWtJXZLOjYhv2j5D0gckLayu+pmI+E2tfQ1ZvEpjaWAGhVaYAW/I3rkPF8h/+N2kH96VOkKhrnV5DwWVJKcOsIkos14tXzFK1/8h7z8a21fkP6hluxa43cTr/vDx1BEKnT7z16kj1LS8I//hqjQvqCXFGawOSadFxF22x0q60/Z11efOjoivJ8gEAMCGqFcAgD5reoMVEQtUvQNKRKyw/aCkfp1ndXu7hm6d9ynaq/50VeoIhVrh7FAr3Adrrz/tljpCTZOPTJ2g2OP/8bLUEQqNWJT/+aGhq/O+x40k6Xs/T52gUJn1avzYVTrqNXkP0/3a1nenjlDosDPyr1dzD/l+6giFbn5xdOoINQ0fkv8t3Ttfk3+9arsh/1EhHp73hCuSpH7MG5X0GizbO0jaR9Ltkg6U9DHb75Y0W5V3Df9hTJPtEySdIEkjNCr7U7St0Ly0gh2vfn/qCIUef/3/pI5Q02HK/2dx0t1dqSMUGnfjI6kjIIGB1qv2seN17YV5X2M4+QP5X1/YCq5evUXqCIV2bl9YvFJCk4fl/7N4bws0L60g1uZ/zWJ/JBtwbXuMpMsknRwRyyV9V9LOkmao8o7hRu/XHhHnRsTMiJjZrhboegEALa2MetU2Mu8zBgCA8iQ5g2W7XZVidVFEXC5JEfFcj+d/IOnKwv20wBDB3M+wtYoP7X9T6ghogrWb5X+RvUflPfuWJKkt/6+jXkgdoD5l1asxE1frwOPzfsf7v+89OHWEQjsr/+Hio1tgFsHNhuR9U/fhmecDijS9Ctu2pPMkPRgRZ/VYPrnHakdJuq/Z2QAA6Ea9AgD0R4ozWAdKepekuba734r6jKRjbc+QFJKekPTBoh3F+vWcIRokPjkx/+te5q1flTpCTYuvnJ46QqGVf8p/Aokth+SfUdECk1y0htLq1ZpnR+mer+V9HeSwt69MHWGTsPewRakjFHqyI+97nh00Kv+af4Umpo6AjKWYRfBmbfw2LTXvIQLk7o4Xt0sdoaYDJz+WOkKhWUPyvzgcg0eZ9WrEVms0/dT7Bx6qgf5wyx6pI2wSnuvM/0bfz3ZuljpCTbu3PVe8EpCxFhioDwAAAACtIek07cCm5J1j8x4W8qNdtk0dodCoE1pgaNvadakToAWt7himOc/lPSnTxw6/JnWEQleftnnqCIXWtcB717sPy/sM0XWrdk0dARgQGiy0hFa4n9iQvfO+0bD0YOoAhSZdmPcsa5LUsY4GC303Yuh67bZF3n/UfvueQ1JHKNQKswh+/qk3p45Q6LBJmQ9XXTItdYQ6LE4dABnL/20WAAAAAGgRnMECStJ1T/5niHK3qd7RHeh8dIiWvT3vmduGva8F7vHWAubdtGPqCIV2e/OzqSPU1BUtMFsrUENLN1jT91qtWbPyHi7QCkPbWsE5T96SOkKh3GcR/MYjr00dodCiRWNSRyg05p4RqSMUWrN1C1zL9olLUydoqkm7rNAHf3lj6hg1/ejZV6aOUGjF51MnKHbmcRekjlBohPO+ke+k8Xnf9kSSPqEDUkdAxhgiCAAAAAAlaekzWPPuHcUZokHi49sfmDpCy5ugeakjFJqQOgCaJv+7spXr+ftG6DvT8r7Z94LT8s4nSZP1QuoIhb595JGpIxR68oy8//yz8z8LP1V5TxSCtDiDBQAAAAAlyfstjAIdW47Wc8fkPWZ8q3NuTR1hk7Dd7aNTRyj0ia2vTR2hpsN/fWrqCIWGP9+WOkKhqb9bnTpCoY7RLfDSfs3gugZr1O7SjJ+mTlHbsSPz/55cfOaU1BEKHX/ZdakjFHrp8GdSR6hpaVf+17p+QYygQu9aoAr3bujzq2hgSjBrft4ThbSKeXlfM6yJOyxJHaHQ0gn5z2L23Kr8M7avzH94zWCzbPFoXfmzvN8Q3Oe9F6eOUOiRb708dYRCewy7PXWEQqu72lNHqGmc859RdtU1O6WOUGj+s+NTRygU61pgMN0H+v7mUwv8rwAAAACgNbT0GaxW8NFH8p9YoBUmCmmFs2wrMn9HcMKb8v9ZXPalV6SOUGjYsvzPDrWvyj/jYBNt0vrN8v6+TBq6PHWEQtNOzP/s0FZHZj6cQdL8zrzvMzVr5Z6pIxQafXj+U/VMSx1gE/FUP7ahwWqw3GeNahWt0ATudmfev0673Zk6QbFVX+xKHaHQuBsfSR0BLWjkmBc141V5v8nx/hvelzpCoemanTpCobNeOCh1hEIzRvfnT8bmOWLsPakjFPqd8h7yi7QYIggAAAAAJcn7LfcCnS8ZrsXfzPsMUSsMy2oFrTBEcM9vfiR1hJredXz+M1s9t3/+7/lEW96vOZL04vi8h/9Ikr6XOkBzjWpbr73G5T1z210jt0sdYZPwzvF/TB2h0C1rXpI6ArBJa+kGq+3Pa2lgkI3LP/K11BFqaoVp2kcuzb8x2PzWvIfWIE+LXhytix7eL3WMmtqGdqaOsEmYOrQjdYRCB4x8NHWEmpZ1DU8dARiQ/N8uBgAAAIAW0dJnsDB4tMIkF7p+auoENR3xivxnubjrv16WOkKhWJX/jYaRnyErhmjk9WNSx6hph+P+nDpCoVWpA9ThG4sOSB2h0PQRC1JHqGm/EYwUQGujwcL/Z+++47Ou7v//P0/2JiRACHtGEMSFIFrrLg7c1mqddeCug4/FWe1U2+rPqlWLW+vGASoaRdwoKCoge8+wIXteOb8/EvulCu9zJbmu6/U6uZ73282bGDIehpCT9/uc9zle8OEZLEB3ow8XqZVjE6UTnLJTU6QTyEOmQwMSx2yVzgj07TL9z2AVYbN0gtMpHfTfzOqcWCedEGhKxR7SCURtwiWCREREREREEcIZLPLCkedcJJ3glDRN/11L7bo8N0c6wamhiksEqeWSEhtRkFUunRHokv6fSSc4vYou0glOS+oKpBOc7ty0v3RCoBU78qQTnDqBm6zR7nEGi4iIiIiIKEI4g0Ve+OA/j0sneG/k+MulE5yqu+jfpr3niyulE9qHtdIBsdUQSsCmSt2bXHRP3iadEAb9M1jHZq6XTnA6tPca6YRAM7p2lU5wehg8S4x2z+8LrIx0mKF7SVcEsl/NlU4g8kbIg/0jbAfdPyQDgE3x4A/SZrcAACAASURBVFt7nF1gZSbXYUQX3TujTSvdUzohDI3SAU6J0H+jSLuQ5QIr8hu/gomIiIiIiCLEg9ucu1c0YCvenfSsdEYgH7bG9sGhl46VTnD6+N8TpBMClfXXf1fVJljpBKeSIzpJJzhVddX/ecS30gGxlZdUgbPyv5DOCHTOR/q/zxbha+kEp4d3DJFOcDo1e7Z0QqDcRB9OPCPaPa8vsBbPyeAFDKnxXHm+dEKgrOFbpBOcEl/Uv3NUXrH+w1h9EG+fxRVlnXHO1EulMwJ16lYqnUAxsi6ke6lzZ15gkee4RJCIiIiIiChCxGawjDErAZQDCAFosNYON8bkAXgJQB8AKwGcYa3dvrv3kbNnCEe8rPsux7S9MqUT2gXty+8AoKShQjoh0DNj9J/ZseKvo6QTnBqT9O8cVd1Z/3JQ/E06IHyRGK+6ZpXipkPejn5sG9xdfKJ0gpP+OW7gpGz95/nV2ETphEDFFfqXWRIFkV4ieLi1dud1SzcC+MBae5cx5sbm/x6/uzcum5/IC5g4ccxJ50onOJ37nynSCYHyP+8oneBU+rD+Z4fyipdJJ7QL86UDWq5N49XWukw8s+rAaDe2yZVHvSed4FSMHOkEp9fL9D+6sG/6SumEQMdlfS+d4PT8VeOkE5wKPte/7Leuc7p0glvxxBa/ibYlgicBeLr5108DOFmwhYiIaHc4XhER0S4Za2XuGBtjVgDYDsAC+Le1doIxZoe1Nnen19lurd3tbfcck2dHmiNjUEtERJE01U6cZa0dLt0RjngZr0rGHSSd4FR4z3TpBKeEoYOkE5xW3SG9gCmYMfpXM/Q4bZ50AsVIa8Yryb9hB1tr1xtjugB43xizMJw3MsaMBTAWANKQEc0+IiIigOMVERG1gNgSQWvt+uZ/bwLwOoARADYaYwoBoPnfm3bxdhOstcOttcOTkRrLZCIiikMcr4iIqCVEZrCMMZkAEqy15c2//gWAPwKYDOB8AHc1/3uSRB9RayTsPVg6IVDj7AXSCU4mVf8PobauTjqhfdC/AghA5MarpEGJyH9C90Yza9+SLmgflt6cJp3gdO2eH0gnBJpT0UM6wWmldACpJrVEsADA68aYHxqet9a+a4z5CsDLxpiLAKwG8EuhPiIiIoDjFRERtZDIBZa1djmAvXfx8q0AdD8FTCKK138nnRAG3Y2ju+nfOnjz+ftJJzh1nbxcOqF9KJEOCE+kxqv6UCLWV3aIZFrEdTtqjXSC253SAW5fHPIv6QSnbY3SBcHqlZ/TBQArket+JYpbureRcSgaVoXiYv5QGw98+Dymf1wgneCwUTrAKaVC/7qxjcf3k05wqir04KDhP0kHxFavtG34V9EL0hmBTnvmeukEp97QfxF456ZDpBOcTu34tXRCoO0NPOOU/KbtHCwiIiIiIiJveT2DRfHDhyWCt27aSzoh0FfQv+Ri6176Z14y1utvDKXrnwmMN/U2EesacqQzAtX14gYukfDrvC+lE5y0L8FL8GUXHKLd8PoCa/GcDC+WjlHb8c85PvS96QvpBIqRpdIBMbbh+3TcM2CIdEagrHH6d/H0wa0nnCed4MSDhtuuB3jQMO0elwgSERERERFFiO5bGA4NXTKx8cyDpDMCFTwwXTrBKTFH97IVAJiy8BPpBKeShgrphEAHTx4nneCUWKn/nk+vYv3LqBoydC//AQC8OVG6IKay9mzEQS/q/trplPymdILT5HvypROcHnz7MekEpzqr+3vt5sYM6QSnC/55mXSCU+4C/Uva63P0N+Lulo9XXl9gJW2qVH8B48OzQ1x+FxmFSVnSCYFSt+j/obu2T610gtPmffQvo0op17+8Jt5srcjCU9N/Jp0RaNKx90snOE2G/gusgkT9P1pta2yQTgi0PqR/vBp4jf5n7Sgy5rfibXTfwiAiIiIiIvKI/tssnuPsUGT48HkseWOwdEKgXnfonu0FgMSB+s+Yaly9TjqBPJSRXosD9lomnRHozK8vlk5w6onvpROcnizdQzrBac+0tdIJgd4r070rL5GL1xdYPhw07AMfLl42Xq37WTsAmDPiIemEQMf3OF46wWnBLR2lE5w6dEyRTnDqlFUpneB2hHRAbHVLKcXtPd6Szgg0pF+6dILTaOgfrzonlUknOA1MLpVOCLQsbZN0gtP3PfeTTnBqWKP7Qro94xJBIiIiIiKiCPF6BsuHc7CeWv2ZdIKTHxtxSBe4jX5A99cioH9pW95nvaQTnLYf1iid4FS+JFc6gX5kaUUXnPDJldIZgR49+GnpBKdlz+0rneC0pr5cOsHpyZpC6YRAM7b1kU5we0b/WFBaPUA6wak+5MFcz5iWv4kH/1dERERERER+8HoGq7ZHJpaOO1A6I9AF+m/Ie8GHWbZhM8+STghUePIC6QSnUJr+8zD6PKn/vlRdB/3btK+QDoixTukVuHAf3RvNXDrjXOkEp/5nfyud4DRm5VzpBKc1DR2kEwLVNur/8XTWvvrHAt1/yu2b/q/gAEk1QN73+n8go7bTvhQUACau0n245DEPXi+d4NTlS/1LLpK31UgnOCXWJEsn0I80wqAqpHuDlIRV+je58MF9m46UTnC6ssuH0gmBju+g/6bqLOjf5ILk6L/8JiIiIiIi8oTXM1iJWyqR98QX0hlEAICrex8snRBoIGZIJ7QL+hffAZzX16eyIQWztuleMz785wulE5y2SgeEYUS2/gWwr5fqnn2ZtUP335UmG6QDSDGvL7AofvjwDNZz5fnSCYFu/fA06QSnzBX6vyX1fm6VdEL7sEY6ILaSE0IoyNB9PtIRHfVfYL0A/VvKHpKh+0BpANicul46IdCxObOlE5xuwwHSCaQYlwgSERERERFFiP7bxRR1PswO+eDsbN2LV+7/PFE6wWnTIfXSCU5rf9lbOsEpudyDhYyPSgfEVqKxyEmqlc4ItKq2k3SC06YrDpJOcNoc+kY6walfUpV0QqDpNfpnKvFBD+kCpxWb86QTnFJSQtIJbqdMbPGb8AKLvNih74FVn0sneG/zSP079GUu07/7XY9XuESQWq66IRnfb9d9uGu3zAzpBKcuD+ne6h4Ayq/XvxvjmpDuH2p7Juu+YQkAOHKtdIFTX+hv9EFrDrnhEkEiIiIiIqII4QwWeUH7Dn0+4C6CkdEgHUBeSlhaj/QTde86Nuca3TvLAUB36J/B+r/HLpJOcBp58hzphEAba7KlE8JQIh1Ainl9gVU0rArFxbqfH/Jh+Z0PNl6tf939dzc9JJ0QyIevxfU36P9zTq7Q/3xTle6VaE1ua/madp91HlKNS9/4Xjoj0HWfDJVOaBd+f+Fz0glOg1I2SicEKq4YIp3gNBU+XASSFC4RJCIiIiIiihCvZ7BCaERFY410BsVAwQP6l4X0HTJWOiFQEWZKJzjlLtX94DUA1HbQf18qsZpHDWuTbELombRNOiNQ+ooU6YR2YfKWfaUTnDK7fCmdEKi0Qf+GK0RB9P+kQERERERE5AmvZ7CWzcnCaT0OlM6gGPDhrK5jjx0snRBI/ybtQNq2OukEp6ypS6UTnPKlA8KwUDogxtbV5uLG5adJZwQae9YU6QSnd/6cK53g9Eivd6UTnF4q7yOdEKikpoN0QhgqpANIMa8vsFCUDDyi/KA3D85J8IEPGzSct+g96YRA9y05UjrBqe59/efHZHfaUzrBqSbPg8UJE6QDYisnuQa/KJgvnRHopdX7Syc45WCZdILTZauPkU5wurjgE+mEQAd0XSmd4HQDeIOfds+DUZiIiIiIiMgPMZ/BMsbsAeClnV7UD8DvAeQCuATA5uaX32ytDV6vsLhe/QyRD0vbfJgd8uHzOHL85dIJgfKe/UI6walkXCfpBKeqzvrvS6V4sJW8LyI1Zm0uz8ZDn+meRZ50zP3SCU4+zBr8tvB96QSnlfW6v9e+sHWkdILThuv0b2ZSMLNKOsGprkOydILb2y0/VsRYKzcQG2MSAawDMBLAbwBUWGv/Ee7b55g8O9LoHrAofiTsrfwZrNkLpBOcTGqqdIKTrdP/nJgPpja+MstaO1y6oyXaMmbl7FFgD/z3WdHMa7PV03pLJzj1/LP+HWU3TRokneB0VdFH0gmBZpb3lU5wWjmiWjqBYmSqndji8Ur6VuyRAJZZa1cJdxAREblwzCIiIifpTS7OBPDCTv99lTHmPABfAxhnrd3+4zcwxowFMBYA0sBzEkgPH2aItLO1tdIJREFaNGb9eLyyR6yLWWhrJI3TP4Plg663SBe43XPHUdIJgYzRv8y5B+ZJJ5BiYjNYxpgUACcCeKX5RQ8D6A9gHwAlAO7Z1dtZaydYa4dba4cnQ/9yIiIi8l9rxiyOV0RE8UlyieCxAL6x1m4EAGvtRmttyFrbCOBRACME24iIiHbGMYuIiMIiuUTwLOy01MIYU2itLWn+z1MAfO96B0XDqlBcrHt3OR926KPI0L7ToQ9fi1vGjpJOaBequhrpBLc/tHxXJmFtGrMKhlbjukm6lxH/9hX+/YuEYc/qP0b77o5fSicEmlqh/7zBYuRIJ5BiIhdYxpgMAEcDuHSnF//NGLMPAAtg5Y9+b5fKG4GPqqX36SBqss+dV0gnBDpgxmzpBKctT0gXuCXWSBe4JXnQ6JNIjFl1Ngmr6/Oj1hgJpxyj/yiH726SLnCbu6ObdILTtBTdOx0em+W8xy6uGAdJJ5BiIhdY1toqAPk/etm5Ei1ERERBOGYREVFLSO8i2CYl32fgzv7DpDMoBswBe0knOBV+8pNNL1VZ/UCldIJTQUf9S2tCpWXSCU4dpQPCMF86IMZ2NGRg8sa9pTMCzVveXTrBqQhfSyc4nd1thnSC08b6DtIJgf607njphDDoHvNJFtfXERERERERRYjXM1gUGdo3Z2iiv3Fxve4ZomPevF46wS27XrrAKWt2mnSCU0q5/jNkMOEl6YKYCjUalNXq/tq5YsSH0glOb405UjrBaVDKTOkEpyEp66UTAv0sc5F0gtNlk8+RTnDavl73TCUAIKlRusDtopZvyuT1BRZ3EYwMHxrPW7RGOsHp7w/q3uRi4MmrpROcln3TUzrBKaFBusCtrK90Af1YUfp2vDtE90Xle9V50glOaW/pv3hZdm9n6QSn9fW6FxIfnal7x00AyHg6VzrBqcukWdIJTom9ekgnOLXmpycuESQiIiIiIooQr2ewKH6cnb1VOsHp7Jsekk4I5MNMpb1X/50swIMzpjxIjDdVFvi2TveQe+vck6QTnLpjnnSC04Fp66QTnEKpuhvfryqSTnDKfFX/ZiYeLBZHw/KV0glRofu7vcPiORle/NBIbcc/5/gw4Hrdh19S5CyTDoixFaWdcc57l0lnBEqo4qKWSPjLhqOlE5xyk6ulEwItKOsqnRCGDdIBpBi/mxIREREREUWI1zNYPvBjhz6KBz7MApZOGSCd0C5s3OjBzlEXtHxXJq8lWiTm1ElXBJp+/L+kE5wO3XGDdILTs4V/l05wWt+g+8e/txJ1nxkHAJ9+oH+8WrVV92Ym3vgldxFUx4cfan24COz/ou6lNQDwzmn3SCcEWvLgSOkEp/xG/Qc3Jibo31J2r366n68AWrcrk88GZG3Giwc9LJ0R6NPqQukEp8FHLZFOcJpSqX8bz/3SdP8NPDxL/1HkPVK2SSc4bSvIkk5wSk3QfzzLb1vxNlwiSEREREREFCHGWh/2GNm1HJNnRxr9hw4SEdH/mmonzrLWDpfuiJX0rj1t/3N0H/Zdr/9mN3r9cbp0gtPiR0ZIJzgdu/8c6YRAy8o6SSe4HblWuoBipDXjFZcIRpkPSwR94MPytj8drfuZkvuW6L8ZsW2p/oNOs1bon/iv7urBjbMbdf99ibQenbfgziuekM4I9Oclx0sntAu3H/aGdIJTn+Qt0gmB8jpXSSc43YADpRNIMf0/KRAREREREXnC6xksnoMVP5af+m/pBO/d/1K+dIJTZhf9J+T2mLhSOqFdiLdzsLbUZ+HpDQdLZwTasFr/DHKOB185p2fp3kACAMobG6QTAs2o9eEcLKLd4wwWERERERFRhHg9g0Xx49BLx0onOK35pe47gide+5V0gtNXd+0vneBka2qkE8hDtVvTsPypIumMQPufr38L9HLpgDBM2LGndIJTVqLu72OHpC+VTgiD/nOwSI7XF1iZe1oc8EJIOiPQV/skSie0Cx//e4J0gtNz5bqX4PmwycWWX+g/Y6o2Zw/pBKeazvqXWuJO6YDYyi8ow6+vLZbOCFRSp/+Aat173zW5KHeedILT17W6t4ys5wIr8hy/gomIiIiIiCLE6xmsyvmGM0RxgpuZtF0eFksnOOl/xJ4iZYF0QIxtX5eNN245Sjoj0NY99f9I0AP6z8E68LFx0glOex+9UDoh0MaqbOkEpxSskk4gxfR/NyXyRPF6nsnWZgcOky5wSpirfxczL1RIB8RWQ4bBpv113xCs7VovndAuXHPGJOkEp9GZi6QTAr1VMUQ6wektdJROIMW4RJCIiIiIiChCvJ7BKhpWheJizhrEg+V3j5JOCIPur8UlD46UTnAydfo3Z8gesbd0glNVoZVOcLtROiC2+udvxIvn3SudEeikz6+QTmgXEoz+v3/10P29du90/cvvOINFQby+wFpc0wGjF4yRzghUvP4t6QQnHy4C+0/0YD3RudIBwQb9a5t0gtOiS3TvxAgApXvpX0ZlkvXvxhhvdoQyMKlM9/faaYc8IJ3gdAl+Jp3gVBFKk05wSlN+Ebiwtpt0gtPWi/Xf+O3y0QbpBKeGAv27l+KziS1+Ey4RJCIiIiIiihCvZ7Aat6Sg/NEe0hmBRr+o+46lL+xXc6UTnLTPBC5+XP9dokH3l0onOJlK3Qd0AoBNS5VOcNK/ACiy6m0i1tfmSmcE+t2aE6UTwqB/Jv4/yw+QTnBa1LVAOiHQWfkzpBOcKnpJF7gljdT95wwAdTm6l6sCAD5r+ZtwBouIiIiIiChCvJ7BSthWiewXv5TOCKR9625A/8yLL7ZdqHs9dqeCrdIJTptH6H8Gy3pwsy2lXPfzFQAA/ZPSEVVanoF3P95XOiPQ06c+JJ3g9EfsJ53gdEDX1dIJTntkbJROCNQ5sVI6wan377+QTiDFvL7A8gEvXuJH7Yk7pBMCHVG4XDrB6dsS3UuoACDz8yXSCeSjJItQXoN0RaAXtuq+SdSkVjrAqaFR93lnAJCVqHupcwI8uElEFIBLBImIiIiIiCIkqjNYxpgnAIwBsMlaO7T5ZXkAXgLQB8BKAGdYa7cbYwyAfwI4DkAVgAustd9Esy8WuEQwMnz4PPZ/8UDphEALTtZ99xwAttysf1K9rNdg6QQnL5YIPiMd8L9iMl4p3xr7vE6teJI7xm6D/g0kxhcWSyc4LanXvRz74S2HSieEQf+RHSQn2j/NPAXgQfzvUHojgA+stXcZY25s/u/xAI4FMLD5n5EAHm7+t9d8uHihyPjj8a9IJwS6LeV06QSnTP2PLqDb5Hjb/y5uPIVojlf1BkkbUyJfHUG3LT9FOiEMa6UDnDaEMqUTnPITdD/jdE7+dOkEJx8u9klOVJcIWms/wU/3VD0JwNPNv34awMk7vfwZ2+RLALnGmMJo9hEREQEcr4iIKHIk1uMUWGtLAMBaW2KM6dL88u4A1uz0emubX1ay8xsbY8YCGAsAvbonofhr3UvHOIMVP87O1r1L3/N36b/zu+B3us+1A4C1p/WWTnCqLtS9FA1A0zyQfhEbrzp3S8Y9pz0Nza6ZdrZ0glORBzNYA5MqpBOctC9ue79ygHQCUZtoeuBhV5sf/+SnBGvtBAATACDH5FlewMQH/jlHwjrpAKeBv9XfSJGxTDqgbVo8XqX27WGv+eysaHe1Sdr6ZOmEduGCJWdKJzj1z9kinRBoWVkn6YQw6L/YJzkSuwhu/GEpRfO/NzW/fC2Anju9Xg8A62PcRkRE9AOOV0RE1GISM1iTAZwP4K7mf0/a6eVXGWNeRNPDwqU/LM0g8mEXQe2OH3G8dIJTyQm9pBOcCl/Xf56YF/z47h6x8SqhxiBrQWo0W9vsovOnSCc4vfMH/Wfl3dRH/+exX3KZdEKgadn9pBOcnjtU/5ia8PG30glxK9rbtL8A4DAAnYwxawHcjqaB6mVjzEUAVgP4ZfOrT0HTlrdL0bTt7W9c779oWBWKi3X/4M2lbaTFlsP1X7xs31f/VvKhtP7SCU5VXT14But30gH/K9rj1cDOGzD5qr9FoTxyLlqiewkjACT8z6NvOg1PrZJOcNrRKF0QrGtSqXSCEy9eKEhUL7Cstbv7bn3kLl7XArgymj1ERES7wvGKiIgiRdMmF0S75cNMYMLeug+gzb1X/51f+0xP9ysJa0zWPzuUnLWrPRhIUlljmvqd0ZYu7Sqd4FTkwQzWEbPPlU5wOrRwqXRCoF92nCmdQNQmEptcEBERERERtUtez2AtnpPhxcwGtZ0Pm1z0m7qfdEKggUcukE5wSjm9u3SCU86HS6QT2oVF0gExVh5Kxcc7iqQzAl006lPpBKdPkSad4DRt72elE5w+rtG9Wcgr20dIJzjZUcOkE5zMF7OlE+KW1xdY3OQifhx7rP6Hr5e/84R0QqDDj7lEOsGptG+idIJTVec9pBOcqgqlC8Jwm3RAbHVLLsXt3XTvLnf0pHHSCU4DMUM6wem1Cv0Hph+RsVI6IdCOzNXSCU5zvtC/XJzkcIkgERERERFRhHg9g8UlgvGjcbb+5W3avxZT8JV0glO3d6ULKFbibaHl2rpc3LTmJOmMYB3qpQvahdV1naQTnB6p0T3NPadU/3JxYIN0ACnm9QUWxY/zFunfOUq7294/XTrBKXup/iWCPV5ZKZ3QPqyVDoit2u1pWPyy7uWldk/959D5YHDaeukEp65JO6QTAp3Y4RvpBKfbcIB0AinWoiWCxphMY4z+n4CIiCiucbwiIiIpgTNYxpgEAGcCOBvAAQBqAaQaYzaj6ST7CdZasZUe3OQifpydvVU6wenQS8dKJwQa+Kb+h8M3XHuQdIJTyQm9pROcqrt6cA7W7ZF9d9rHq25dtuKOq5+R+vBhGf/NqdIJ7cIR6fqXjr1Z2Us6IdDHOwZJJ4ShQjqAFHMtEfwQwFQANwH43lrbCADGmDwAhwO4yxjzurX2P9HN3LW5pZ3Q952LJT502IrwtXRCu+DDhWp6qu7tUH3Y76jHpHXSCU71XXVvbwwAdatSpBOcFkf+Xaoer2ptMpbXdpH40GFL+i5LOqFdOGXBr6UTnI4tnCedEKgypP97GFEQ1wXWUdbanzz1aq3dBuBVAK8aY5KjUkZERBQ+jldERKRC4AXWD4OVMWaYtXZO0OtISF1VhaKLOEMUD8wBe0knOL07SffhkiPHXy6d4JRaFpJOcMr6RP/+d/G4e5H28WpTeTYe+OwoqQ8flozhpdIJ7YL22SEAOK/Dt9IJgYam69/Y6n74sIyRpLiewSqw1m4E8BSA/Zpfdre1dnwM2oj+y341VzrBSfsyxlx8IZ3QLui/BIxP2ser1NVVKLpc91EJ628YJZ3QLrz0L90X0gDw7bk9pRMCldWlSSeEQf9ukSTHtYvgI8aYLwD0MsZcbow5CMAxMegiIiJqCY5XRESkgmuJ4CkAYIxZAKASwKkA+hhjPgbwpfSdQe4iGD+WPDhSOsHpT0dPlE4IdOuHp0knOCWW699VO2uN/h36qrp6sKXJTZH9+6J9vOoytAZXvrFIMsHp8wr9Xzff/U26wO23174qneA0MEX3ToeZRv+h1+Oh/+cSkuNaIjgdwBIAGQDmAngRwBEAjgL4lUVERDpwvCIiIi1cM1gHGWMGADgMwEUAhgEYAOAfAD6Nep3DkiV5OPbYs6QzHBZIB7QLvzviLekEp/+c8QvphEBFs2dKJzg1HrKvdIJTyvKN0gluSfpnApdF+P1pH6+qGlPwbVUf6YxA66r1H0EA7JAOcOqcVCad4PRNdV/phEBf7OgnneCUsE+hdIJT43fzpRPilnOzKWvtUmPMNmvtVQBgjPkOwLMADo12nIutqYVduFw6g2Lg9T07Syc4Fa9/QToh0EHXXyad4JS6vUE6wSm5qlo6gXZD83hVFUrB7NLu0hmB+mbqP9B9s3RAGJbXFkgnOJ2erXunQx8uUp/8brt0AikW7m6+O28t9Kq19muAJ+gSEZE6HK+IiEhUWBdY1tqanX79p+jltEzRXpV4t3iGdIb3fNiI44FVn0snOA2bebF0QqDCF7+UTnBa9rz+r8UOV+mfTe2UUSWd4HZEdN6t1vGqX2opXug/RToj0Pd1+je5uBkjpBOceqfon2ebW9dROiHQrMo+0glh0P/3heS4NrmYAOABa+1PDiEyxmQC+BWAWmvtc1HqC7R4Tob6i4Pi9bp3OfTFlWdfKZ3gNOflp6QTAg28W/9Bw0lL9O/Q17gpQzrBabsHu8FFmvbxal5pZwyacoXEhw7bV8fcJ53QLiyt7Sqd4HRs1vfSCYGO7zBbOsFpXu4h0glOoR08PFyKawbrIQC3GWP2AvA9mpY/pwEYCCAHwBMARAYrIiKinXC8IiIiFYy17judxpgsAMMBFAKoBrDAWit+oEeOybMjzZHSGURE1EJT7cRZ1trhkX6/HK9ar2TcQdIJToX3TJdOcEoYOkg6wWnVHeE+gi/DGP2z8D1O071RCEVOa8arcJ/BqgDwUWuiookHDccPLrVsu+NHHC+d4LTgdz2kE5yyl+nfAr26UP8PJxgfnYO5tY5XXYbW4PI3lkpnBLr+HR4XFgnPvvO4dIJTVRg31yW9XzlAOsHpZehfCkpyEqQDiIiIiIiI2gvdc8QOPmxyQZHhw5/zmlt0L69JelD/mR0D/l4rneCUOCfSR+TGp3j7LK7dmo+bnzlPOiPQoCNWSic4haQDwnDV6hOkE5wOzNV9hugRmQulE5w4g0VBWnyBZYxJAJBlrRU/cj+nfwAAIABJREFUBa77XpX465szpTMC3dxX/5ayXH4XGSPHj3K/kqDcvyyQTnBa/Ij+vy9p+X2kE5yyM2rcryTtuOh/CE3jVZe8Ulx51pvSGYEeX6r7JhEA/GpupXSC06GZs6QTnFbWd5JOCPTI5sOkE5z2+Vb/99ltdZnSCU6pCQ3SCU5TW/G0cFhLBI0xzxtjcpq3up0PYJEx5oaWfzgiIqLo4XhFRETSwt1F8Dtr7T7GmLMB7A9gPIBZ1tph0Q4M4sOuTERE9FNR3EWQ41UrcRfByOAugm3HXQRJk9aMV+FucpFsjEkGcDKASdba+hbXERERRR/HKyIiEhXuLYx/A1gJYDaAT4wxvQEEHg9tjHkCwBgAm6y1Q5tf9ncAJwCoQ9Mzzr+x1u4wxvQBsADAD2eVfGmtvaxF/yfUrj2w6nPpBKeiZN1rnUeOv1w6wakm30gnOPV4ZaV0QvuwNmrvWeV4Vd8/DRvuHdya/5+YKcpfLJ3gVH6PdIHbpa+/JZ3gNDJ1g3RCoBm1+jeQeBj6t5InOeFeYL1prb3/h/8wxqwGcKHjbZ4C8CCAZ3Z62fsAbrLWNhhj7gZwE5qWbwDAMmut/q3iWogbSETGHk/+n3SC03WnTJZOCDTq2q+kE5y+2dJTOsFp2+h06QSnDRtypRPcLojae1Y5XiUmNKJDuu6H4q/r/p50gtOl46+STnDaUL9ZOsHpKzRKJwQamKz/c5j44c+lE5xWbs2TTnBKTNT9tQigaT1EC4W7RPDVnf/DNj249WLQG1hrPwGw7Ucve89a+8N2IV8C0H+qKBER+YTjFRERiQqcwTLGDAIwBEAHY8ypO/1WDoC0Nn7sCwG8tNN/9zXGfAugDMCt1tpPd9M0FsBYAEhDRhsTos+H85tOma//TtGi3zwsneC9kXeeKJ3gtHmk/jtZebP1n8/etVL/A+KrI/z+tI9XiR07Yt28gjZmRNe/snRvwgEA3e/Wv8nFAZetkE5w6pZYJ50Q6NOa7tIJTgvn619xkb04UTrBqTFFuiA6XEsE90DTuvRcNK1F/0E5gEta+0GNMbcAaADwXPOLSgD0stZuNcbsD+ANY8yQXZ1dYq2dAGAC0LQrU2sb6P95fc/O0glOr0N/o3a5+EI6wSn3WekC8pj68WrA9V+2NiMmvvdhF0Hov8C69QTdB0oD3EUwEgZeOUM6gWJkfiveJvBvmLV2EoBJxphR1tqI/HRmjDkfTYPgkc1LN2CtrQVQ2/zrWcaYZQCKAHwdiY9JRETtG8crIiLSIty1LluNMR8YY74HAGPMMGPMrS39YMaYY9D0kPCJ1tqqnV7e2RiT2PzrfgAGAlje0vdPRERxj+MVkYMxVvU/RL4Ld474UQA3oGn7W1hr5xhjngfw5929gTHmBQCHAehkjFkL4HY07cKUCuB9Ywzw/7a3/TmAPxpjGgCEAFxmrd22y3dMcYm7MbZdv9culU5wSivRv1687zORfnooTkXv06hyvGrolImtp45qy/9X1FXuUy2d0C7c+9YT0gne29bY1scmo++W4/SPqalT9O8ebFJTpRPcWrEBbLgXWBnW2pnNg8wPGnb3ygBgrT1rFy9+fDev+yp+tPMTERFRK3C8IiIiUeFeYG0xxvQHYAHAGHM6mh70JYoJH3Zj1G4g+EBuJAT+pE4aqByvkrZUIv9R3RvN1OXo3+TCB9ePcR27Jo+bXLRdDw9mh3xga2ulE6Ii3L9hV6JpJ6RBxph1AFYAODtqVURERK2jcrwyaalI7DdQOiNQfbZ0Qfuw7s/G/UrCftFzoXRCoJUV+dIJTlxQS0HCusCy1i4HcJQxJhNAgrW2PLpZRERELcfxioiIpLkOGj4BwBxr7armF40DcJoxZhWAa6y1+k/To3bBh00u+k3VvSzk18P0L2eY9Pwh0glOvZ5f5X4lclsT2XenfbyqKUzAopuzJBOcOnTYKp3QLkzb/zHpBKeJ5UXSCYEuzf9EOsHpWnBJLe2ea5v2vwDYDADGmDEAzkHTifaTATwS3TQiIqKwcbwiIiIVXEsE7U7nf5wK4HFr7SwAs4wxV0Q3za2+IBMl5+i+g1B4r/5T530wesEY6QSn5Ufp3prXh41Cki/X/2Bzfc9O0glODZm6H2AHEPEZLCgfr9JT6rF377XSGYG+/2yAdIJTZ+mAMEyv0V85OnORdEKgj6v7SScQtYlrFDbGmCwAVQCOBPDQTr8nfkhBSlkI3T/YLp0RqFE6oJ0wx22WTnBTvmDWh7Mm8hbq300oebX+r8XkZA8usCJP9XjV0JiALdW6lwgm1ujfnMEHi2oLpROchqRskk4IFLKuBVZEurlG4fsAfAegDMACa+3XAGCM2RcKtr0lIiJqxvGKiIhUCLzAstY+YYwpBtAFwOydfmsDgN9EMywctroGjbMXSGdQDJz8re6lNT5YddP+0glOyWXSBW49F0sXhKE+/k7r0j5e1dcmYc0K3UvHLvvl+9IJTtP+mCmd4LR32mrpBKdk5ZOVfVL0rxQAukoHkGLOdSTW2nUA1v3oZbwbSDF1We469ytRoJxl+p9vqu6ifNQn1TSPVwnJjcjpqnvH+AlTj5ROcBqAL6UTnDIT9C911r4Ar8YmSycQtYn2v2NERERERETe8PpJ6KJhVSgu1n0+kg87t/nAh8+j9rO6Oi6skE5wqs3Nlk5w2jCmt3SCU1WhBzOBt0sHxNbgzK34ZPjT0hmB9l50jXRCu/DMloOlE5xu66p7OejWBt0bwhC5OC+wjDEJaDq8cWgMelpk8ZwML37wprbTfvHig+2D9A9YjSnSBW7dX+NBw5EQjUfZNI9Xi9Z2wRE3/lY6I1CvC7gUOxLGFUyVTnBKMbpvwuQmVrlfiUgx5xJBa20jgNnGmF4x6CEiImoVjldERKRBuEsECwHMM8bMBFD5wwuttSdGpYqIIi732S+kE5waxo6STnALhaQLKJjK8aohx2LTUfWSCU4F9fo3FsiRDghDyOqeHQKADSHdj+AvreUOfeS3cC+w/hDVCiIHLgWND50meHARKB1ALirHq9SVVRh4wSzpjEAl4w6STnDKwTLpBKfrx1woneC06g7dj+Abo3/X2x6YJ51AioX1N8xa+7ExpjeAgdbaqcaYDACJ0U0jIiJqGY5XREQkLawLLGPMJQDGAsgD0B9AdwCPABA9NMOkpSKx30DJBKfQgiXSCRQj2jfiGDn+cukEp7Tt+pffZX3nwUYASR5cT6yIzrvVOl6lDTYY+GyqZILTRR2ek05wevyevtIJTv2fjNIXdwQ9UfCBdEKg6TXdpBOcbv39OdIJTp3n6F9zUdPBg/HqqYktfpNwF+FeCeBgAGUAYK1dAqBLiz8aERFRdHG8IiIiUeEuwq211taZ5m09jTFJAMQXyNqaWs4QxQnts0M+qM+ULnCrKtD9XAAAZH0r/q3PrV7/XcsoUjlelVal4+1vhklnBHo7tLd0glMRZkonOP2l8CPpBKdtyhcLZCbUSic49frjdOmEdiFdOiBKwv1p5mNjzM0A0o0xRwO4AsCb0csi8s8Bt+pegld9rP6DhjPf1X9Wl22I64sXH6gcrxKTQ8jrViqdEejcfvovXt5BrnSC04p63Tv0AcD7leqOivsfo7O4gQT5LdzvAjcC2AxgLoBLAUyx1t4StSoiIqLW4XhFRESiwp3Butpa+08Aj/7wAmPMNc0vE1M0rArFxbqXjnF78cjw4fP41fqHpRMC+fA53OLBOVgbT+ovneBU1VX/OTxR3Exd5XjVP30LJg57QjLB6Yj3r5VOcCrC19IJThO2HCqd4DSuy1TphEDvVg6WTiBqk3AvsM4H8OPB6YJdvCymFs/J8OKHRooPJy8ZLZ3gsFE6wKng0y3SCU52xRrphHZhcfTetcrxak1dR4xbdYpkgtOQAfp3yNR9VHOT9IQ66QSnNQ26j2xeV9tROoGoTQIvsIwxZwH4NYC+xpjJO/1WNoCt0QwjIiIKF8crIiLSwjWDNR1ACYBOAO7Z6eXlAOZEK4rIR9WH6p8h0o67glIbqB6v7MpE1Fyse9Zg5RkF0glOPVEineD02lfDpROcuv5M94YrJbUdpBPCUC4dQIoFXmBZa1cBWAVglDGmN4CB1tqpxph0NO2sKPrVlTQoEflP6J5G3nrwdumEduGBVZ9LJzid/Yf/k04IlPfEF9IJTj48g5W+rVE6wak6X/8uZnik5Qc3BtE+XmX0r8E+z+u+gbBsCo8Li4Qpx9wnneA0t65QOiHQmqQ86QSiNglrFDbGXAJgIoB/N7+oB4A3ohVFRETUGhyviIhIWribXFwJYASAGQBgrV1ijBG/1dWwMMQZojhRlKz/lNwEH56+Vi5nlf5PYvoM3bMQAKB7IVrUqRyvyjZm4YN7D5bOCDTu5knSCU6v3iL+R+nUO0n/genz68TP3g6UYHT3EbmEu46k1lr7321xjDFJAPjVT0RE2nC8IiIiUeHeZvnYGHMzgHRjzNEArgDwZtAbGGOeADAGwCZr7dDml90B4BI0HQIJADdba6c0/95NAC4CEALwW2ttsSuq45B6nDJxs+vVRF2Wq3/bWx+2uvehMfcD3dt3D75W/13VSd/oP78p6fy+0glOaen6t4nGyVF7zy0er4Doj1n5XUtxzo1TWvd/FCODUtdLJ4RB/wzWdev1n4M1KmeZdEKgS/M/kU5wuhYHSSeQYuH+xHUjmgaSuQAuBTAFwGOOt3kKwIMAnvnRy/8/a+0/dn6BMWZPAGcCGAKgG4Cpxpgia20ozD61fLgwoMgoHvyWdEKgfq9dKp3glLUqUTrBKWOj/gvV6s4Z0gmSWjNeAVEesywM6q3ur+/71h4tnRAG3TdVASA/uVI6wemQ9OXSCYG0n9MFAGtu0X+B1eU7/cvuazrq/r4IAHi25ZsyhfWTgrW2EcCjzf+ExVr7iTGmT5ivfhKAF621tQBWGGOWomkNvf5tz4iISI3WjFfNb8cxi4iIIiKsCyxjzBgAfwLQu/ltDABrrW3NLYarjDHnAfgawDhr7XYA3QF8udPrrG1+2a5axgIYCwAZBVn4ZHtRKxJip3j9h9IJ7YIPM4HPledLJwRKzKuVTnDqNniLdILTRT0/lU5w2hHSvynM5f9wv05rRHi8AtowZu08XnXslopU5TvhvNLfuTJf3LCbrpJOcNq0ept0gtMxHcSPhguUm1AtneDU56iV0glOm0dlSSc4pSfr/r4IAHi25W8S7lqX+wCcCmCutbYtDws/jKaBzzb/+x4AF6JpAPyxXX4ca+0EABMAIMfkWe27CI6G/gsDioxn9ugpnRCoH76TTmgXHof+Z7DiXKTGK6CNY9bO41Vq3x72HzN+0cac6PrX8lTpBKeed06XTnAy03Z5f1iVD8qHSCcEmlvaTTrBKXS4/mcWeZqYnHB3EVwD4Pu2DlbW2o3W2tBOSzhGNP/WWgA7/3TaA4D+r1wiItImIuMVwDGLiIhaJ9wZrN8BmGKM+RjAf9cZWWvvbckHM8YUWmtLmv/zFADfN/96MoDnjTH3oumB4YEAZrreX9GwKhQX674r78PSNh+s/Mso6QSn/Lm6d4Lefpr+B6+TZmZLJzglV+j+cwaAqkLpgjDc1vKHhsMUkfEKiOyY1S97M5487JGWJsTUM0N/Jp3gtORP0gVupxZ+K53gtL1B9zLiO3o5N/4UdwMOlE4gxcK9wPoLgAoAaQBSwnkDY8wLAA4D0MkYsxbA7QAOM8bsg6alFCvRtMMTrLXzjDEvA5gPoAHAleHsILh4Tob6C5ji9bovAAE/LgL73KL/2fHyM/nNtq3Stuq/eFG+ERwAIG++/s9jFLV4vAKiP2YtL++MMz++rMX/M7H0yqEPSyc43fzfSUS9pm0bJJ3gNDhrg3RCoEar/8iODdd5sIvgV1XSCU71HZKlE9zeitIuggDyrLUtWjxurT1rFy9+POD1/4KmgZGIiKi1WjxeARyziIgocsK9wJpqjPmFtfa9qNa0Qz7MDvkwy3bopWOlE5ym36t7+c+wf1whneBUoXufEABAmv6NDhEKe96mXdI5XoUMzA7dd2p/NV3/WXkdLk2TTnAak6v7QGkAOCV7nnRCoPl1HaUTnGpHlUsnOK3ooXspKAB4MFkJtOKY03AvsK4E8DtjTC2AerR921tSxIeLwLPmvy2d4DRs5q5ugOtReK/+3beqT9a//Cfr4yXSCRRM5XiVUA+kl4S7r5SM2i76l5Z2+rf+5eIDb9C9/A4Avq/TfazIZxW6j+ABgN5nzJVOoBhZ2Yq3CfegYf1PnhMRUdzjeEVERNLCncGCMaYjmnZK+u/8vLX2k2hEhcukpyFhj8GSCU6NsxdIJ7QLl+Wuk05wOmLfx6QTAh3z4PXSCU6pm/TvIJG1uLN0gpNN1v95RBTPYtU4XuXmVeDEMz+TTHAalrFGOsHpSfSWTnDKS6yQTnBKQaN0QqAxOfofXfgK+0snkGJhXWAZYy4GcA2azvr4DsCBAL4AcET00oiIiFqG4xUREUkLdwbrGgAHAPjSWnu4MWYQgD9ELys8AwduwzvvvCCd4T0fnsHyQVGy7odJB9+1VjrBqfxx/bszbB+l+zkaACjIKJVOcPt51N6zyvGqY2IlzuzoPN5R1LAU/RtI+DCDlWkapBOcMozzJBxRX1b2k05wCh22n3SCU+JH30gnOJmksBfTyalv+ZuE+39VY62tMcbAGJNqrV1ojNmj5R8usnw4B4sig3/OkaB/mWX6aOkCt3TpgDBUSwfIUjlerZ2bhRv66D4rr2Sc/nN9CqF/s57/G/Mb6QSnVXfo/qHWGP0brvTw4OLFB7ZB/w2J1gj3b9haY0wugDcAvG+M2Q5gffSyiIiIWoXjFRERiQp3F8FTmn95hzHmQwAdALwbtSoiIqJW0DpehQamouyB/tIZgSqW6l425oul5+g/w+lXA3RvuPL1tl7SCU7659hIkvMCyxiTAGCOtXYoAFhrP456VZiKhlWhuFj3TjNc2hYZPhyGzD/rttsydpR0QrtQ1dWDkxv/MDHi71LzeNU5pRyX9v1UOiNQqI/+5wtfRlfpBKfHz3hYOsHpwfVHSicE2lSRJZ3gpH8/WZLk/G5qrW0EMNsYo/92AhERxS2OV0REpEG4z2AVAphnjJkJoPKHF1prT4xKVZi4yUX84J9zfOg04QvpBIqRxdF71yrHq23zUvDCoG6SCU7c5CIy7jzxTOkEJ25yQRRd4f4NE9/iluJb4uCB0glO9737lHRCoGPe1H/QcLePpAvccj5cIp3QPmyJ2ntWOV7V9szEkht07yLYZeAm6QS3e6QD3A5+Xv+S9j9nz5FOCFRv9R+WfjsPGqYA4W5yoWYdOxER0e5wvCIiImlhXWAZYw4E8ACAwQBSACQCqLTW5kSxzYmbXMSP0AL9swanf3uxdEKggVfNkE5wqjhD9x1+ANh0sviRSk5ebHLx5+i8W63jVd/cTXj0hIckE5zO/eQS6QSnDtIBYUhNaMWppDG2OZQtnRDog7I9pRPCwGWMtHvhLhF8EMCZAF4BMBzAeQDE12zxGaz44cMugoDuxuN7HC+d4FSdr//CoPD15dIJ7cKi6L1rlePVyq1dcPEzV0lnBOq4f/TWbcaTK3MXSCc4bQzVSScEWp2+UTrBaQ66SCeQYmE/5WitXWqMSbTWhgA8aYzR/6QpERHFHY5XREQkKdwLrCpjTAqA74wxfwNQAiAzellE/8uHmcqEvQdLJwRqXKv/rmqXJ/TfQW+o033nl3SOV2nZtRh4uO7Zz++/6yOd4NRJOiAMN2/UvxvjnhnrpRMCfbZjgHRCGMqkA0ixcE8VPLf5da9C07a3PQGcFq0oIiKiVuJ4RUREopwzWMaYfQH0BzDPWrsASrfAJZL2zjsvSCcE8mEW0O6rfwOJhLnLpBPah4rIv0vN45WBRVqi7s0PBg5dK53QLnRKjsIXd4SNSFshnRCoNDtdOsFpI3RvFEKyAi+wjDG/B3AOgFkA/maMudNa+2hMyoh24sM5WNo3ufBBY7L+s08SEsKd+KdY0j5e1W9Kw7r7dX8f2zhCusCtP/RfBPZN3Syd4JRqQtIJgbITaqQTwsALLNo91wzWrwDsY62tMsbkA3gXgJoBi4iIqBnHKyIiUsF1gVVjra0CAGvtVmOMqlu3PAcrfvhwDtbkygzphECLH9F/ezpzZdgbm4pJGTpUOsGpqlC6IAy3Rfw9qh6vCrtvwW1/fVI6I9BjJYdIJziVSweE4aUNB0gnOPXvuUk6IdAB6bqXMALAv684STrBqev0HdIJTjUFun92AgC8O7HFb+L6aaa/MWZy86/Nj/4b1toTW/wR44wP5zfxIjAy7rrtPOmEQGmnVUonONWWZUknuI3Qv3NUl8wq6QSnKNwyUT1eWRjU2GTJBKdbe7wtneA0HiOlE5yWbta/1+G0jroP8h2TM1s6wanLQ/pPf2iUDghDinRAlLgusH58ef6PaIUQERG1AccrIiJSIfACy1r7caxCWmPxnAz1sy+JOTnSCWHQf0feh5nAkobPpBMCHTx5nHSCU1qpkU5w6j1O/w5hsFa6IOa0j1fbGjLx8ibdS8fOK9B/R94HXxyo/9G/tQ3SBcF2NKZKJxC1if4HHjwXKtN/8eID7RfSALDj3FHSCYEGPvuFdIJTYseO0glODaX8O00tF7IJKKtPk84IdPVrF0onOPWD/u9jv1t/hHSC05G586UTAn1UOlg6IQw+7HRIUlQ9BExEREREROQzr2ewantnYPHtw6UzAhVd9LV0AsXIjLsflk4IdOiOsdIJTjW5+s/Byiypk05wqsv14Fv7Ky9JF8RUVmItDszTvTPaIttHOqFd2CNjo3SC0yFp66QTAjVa/ff/l6G3dAIp5jpo+E0Au13ML70rU+qqKl7AxAkfnsEaOf5y6YRAuW/qX1qTMFr3DRMASPt6qXSCk+6FaNGhfbyqDKXg6+26fyCzPbjkKRLGKl9+BwBTqrpLJwSaXj5AOiEM9dIBpJjrNmerd2EyxjwBYAyATdbaoc0vewnAHs2vkgtgh7V2H2NMHwALACxq/r0vrbWXtfZjExFR3OF4RUREKkRzF8GnADwI4Jmd3t+vfvi1MeYeAKU7vf4ya22LdjLgQcPxw4fPY8Yxyrdl8kBCg/7d77acpPv8GACo7KZ/N0b8NbLvTvt41Tm5HJd0V73RIX476wLphHbh/m17Syc49U7dIp0QqLbRg2XOnMGiAK4lgnOx6yUXBoC11g7b3dtaaz9pvtO3q/drAJwBQP9WO0REpB7HKyIi0sJ1i2BMlD7uIQA2WmuX7PSyvsaYb9F0KNOt1tpPd/WGxpixAMYCQK/uPtzhoHjx4RO6zz45fsTx0glOm4v0n+luGqUL3Br1fxqjQfV41b17AvZO0T1rkL/HVumEdmFFdSfpBKdzcmdJJwSqaUyWTnBaia7SCaSYa4ngql293BhzMIBfA7iylR/3LAAv7PTfJQB6WWu3GmP2B/CGMWaItfYnB85YaycAmAAAOSbP+rB0jOLDyUtGSycEalire9coAOj6UYZ0gpNdsUY6oV1YHOH3p328Su3Twx7y/tWtTIgNk6T/7oH+k/KAYVlrpROcVjZkSScEWluXJ51A1CZhTwEZY/ZB0yB1BoAVAF5rzQc0xiQBOBXA/j+8zFpbC6C2+dezjDHLABQB4BaBRETUIhyviIhIkusZrCIAZ6LpDt5WAC8BMNbaw9vwMY8CsNBa+99bPMaYzgC2WWtDxph+AAYCWN6Gj0EUc9WH6j/7RLvQgiXuVyLaBe3jlQ/HipSMO0g6oV2Y8qtR0glOD99xiHRCIGP0b3jUA/OkE0gx1wzWQgCfAjjBWrsUAIwx14Xzjo0xLwA4DEAnY8xaALdbax9H0wD4wo9e/ecA/miMaQAQAnCZtXZb2P8XRArsOFf3oJr7rP5zsBI76l8AFCr9yUowao1QxN+j6vGqrl86Vty52302VKiv0H+IdqF0QBgOe0H3800AMCBV9w3BT8uLpBOc5h2yr3SCU8Kn30onxC3XBdZpaBpgPjTGvAvgRTTtyORkrT1rNy+/YBcvexXAq+G8XyIiol3geEVERCq4Nrl4HcDrxphMACcDuA5AgTHmYQCvW2vfi0EjEYrX6z7vDABGjtc9g+WD6hH9pROc0mdwGWNEbI/su9M+XtmQQX2l7u0dMzpWSye0C4dnzZdOcFpTny+d4D3ODlGQsDa5sNZWAngOwHPGmDwAvwRwIwBeYBE1+81Nk6UTAi28Vv/imuLJun8ABYB+C3OkE9qHCF9g/UDteJVgkZiu+zDy6spU6YR2oaqRn8e2uqKT7kO5AeBqHCydQIoltPQNrLXbrLX/ttby0EUiIlKL4xUREUngSb1R5sPStorGGumEMKRJBzg9tPjn0gmBCk9eIJ3gFHqhUjrBqeQB/Wd1bd/gwSzbJdIBsWVqE5C8WPfXzlu/+Zt0gtPVOcdJJzh1TtT/fazG6j7I96lt+pfcL35khHSCU9ZS/T/m20TpgjDcNbHFb6L/M+85HoQcGT5cqM4Z8ePNxnQ5vsfx0glOWZ/o/gEUAApf3yCd4FSIHdIJTqulA2LMNAIJtdIVwY6d3tqzmGOnb9ls6QSngkT9BzZ3SNgqnRBoddoW6QSnostmSidQjLTm9nSLlwgSERERERHRrnk9g2WSkpCY11k6I1Bo82bphHbh1yvaclZobDzf90PphEANa9dJJzglV/SSTnBL9GA9gwlrd3KKoeyOVTjytK+kMwK9OVP/uT4+mF6j++cSABiYrHuGqCKk/7EAoiCcwSIiIiIiIooQr2ewbEMDZ4jixNaDo7SncwSNBp+3a6vcZ7+QTnDSvdE2aVWzwGLR8HrpjEDZ47z+kUCNh085UTrBadUduv+sjbHSCU49ME86gRTT/TfMoWhYFYqLdW9+wE0uIsOHTS608+Frcem9B0onOGU75zt1AAAgAElEQVSv1D/xX1Wo/4cT3NjyXZl8ljekDme9tl46I9BjK/Vv4IJ7pAPcHnr7MekE7xVX7iGd4PQ69C8FJTn6f1IgIiIiIiLyhNczWIvnZHhxV57a7vgR+rcYf3vm29IJ3uv3mv4z2ZJmL5NOaBfi7bO4qTYH/1yk+7zjY3vNl05wmuXBfeFndug/H+mMDl9LJwTSfk4XkYvXF1gdh9TjlIm6n8F6fU9OIUcCL17abse5+g9u3DxS//kxeYOHSic4VXfyYBfBv0oHxFaX1DJcs8c06YxAf593tHSCkw/PvZzS4RvpBKfcBN3fa7sl63/uGsiVDiDF9N8KIiIiIiIi8oTXM1jb5yVzhihOcClo2+VC/w59uc9KF1CsLJQOiLFt81LwwqBu0hmBEsd1kE5oF8aPuUA6wYm7CLadD7OpJEf33zAiIqJ2oK4wE2suOUg6I1DjXuXSCe1C/ydXSCc4HZa2TToh0LdlPaUTnHxYxEhyuESQiIiIiIgoQjiDRV6cMeXDEkHtn8eR4y+XTnCq7qJ/c4akSv1LV1Iq9Dfi2fg6ByuhAUjbovvP5a/7vSyd4HT5o+dJJziNyXhPOsHp9Gzdy9uGpq+RTnD6w9snSCc4bd6gf9mvqUmUTnC7ouXjFS+wyIuLFx9ov4DJfVb/M1j5HTtKJziFSsukE8hDNhGo66D7BsIj6w6TTnAquuQr6QQ33dcuAIBPa7pLJwSaXj5QOsGp4/FLpBOc9I+ofljVirfhEkEiIiIiIqII4QwWUYT4MEOkXWg7Hxum9im7YxWOOF337Mt7bx8gneDUGxulE5wmLD5YOsHpF7107+O5sLRAOiEM66QDSDHOYBEREREREUWI1zNYJi0Vif10r9MNLdC/Rpci46nVn0knBLqg18+kE5waf6b/ecCUpSXSCW5JHnxr1/8Me0RVh5Ixb0ehdEYg0yhd0D4MzN8sneB0Yf7n0gmBpqUNkk5weotPOFEAD0bh3bM1tbyAITV8uIDRLuEz3TsxAkCDdAB5ySyuQ9JRq6UzAiWP6yGd0C5UX5EvneB0xh8ulk7wHg8apiBcIkhERERERBQhXs9gFQ2rQnGx7jve3AI9MrSfMQUAw2aeJZ0QqPDkBdIJTqvvOEg6wSlN/+ofVBXqPm8JAHBLfJ2D1WOvCvz9rS+lMwKd9OH+0glOuhdZNrnujVelE7z3QfkQ6QQn/T+VkCSvL7AWz8lQfwHjw4WB9s8h4EfjnPUvSCcEGnb9FdIJTklV0gVuplH/xUvHhfob483K6nxcMOd86YxAUw5/QDrB6VrovwnzUflg6QSny/KnSycEU34QMgB8B/0XgSSHSwSJiIiIiIgixOsZLB/4MPNCkXHc3kdLJwQq3Kz8jiUAJCRKF7g1hqQLyEelScCUPOmKQNdmniGdEIa10gFOHZKqpROcJpYNk04I9F25DxuulEkHkGJeX2Dl7BnCES9XSmcEmrZXpnRCu7BuvP5lITdd+JJ0QqCvKvpKJzi9PVX/Qae5us/nBADUdDbSCW53x9czWIUF23DL9c9JZwQaP/nX0glO/T24wOqUVC6d4NQzeat0QqDRWfqXCF6PUdIJpFjUlggaY3oaYz40xiwwxswzxlzT/PI8Y8z7xpglzf/u2PxyY4y53xiz1BgzxxizX7TaiIiIfsDxioiIIslYG52HoY0xhQAKrbXfGGOyAcwCcDKACwBss9beZYy5EUBHa+14Y8xxAK4GcByAkQD+aa0dGfQxckyeHWmOjEo/ERFFz1Q7cZa1drh0B8Dx6gcl4/SvFCi8R/9S54Sh+g/JXf0HD5ZjK9fjNP2zbBQZrRmvorZE0FpbAqCk+dflxpgFALoDOAnAYc2v9jSAjwCMb375M7bpiu9LY0yuMaaw+f3sUsch9Thlou49k1/fs7N0Qrvgw26M2v3s6kulE5zWHyZd4JY3W//eQNU+LBH8q54lghyvmtz9eb10gpMP27SPn/SydILTHsm6nx+aVt1bOsHpGfSUTiDFYvKTgjGmD4B9AcwAUPDDINT87y7Nr9YdwJqd3mxt88t+/L7GGmO+NsZ8XbFd/2BARET+4HhFRERtFfVNLowxWQBeBXCttbbMmN3eWd3Vb/xk/aK1dgKACUDTkgvOEMUH7sbYdpmYIZ3gNJDnc8YNjXuFxPt4lT0uWTqhXbj7JP27Ma66Q/ceZ8boP8uvB7hEkHYvqjNYxphkNA1Wz1lrX2t+8cbm9e4/rHvf1PzytcD/zLf2ALA+mn1EREQAxysiIoqcqN3CME23/h4HsMBae+9OvzUZwPkA7mr+96SdXn6VMeZFND00XBq0nh3gNu1ELbH8bv1byvaYpn8ZVX22/ofDqzrrf04MD+l5BisW41WHISGMfln3cy+Tb9D/988H2//WIJ3gNLyj7vsBp3f6WjrB6V8okk4gxaI5R3wwgHMBzDXG/LBDwc1oGqheNsZcBGA1gF82/94UNO3ItBRAFYDfuD5A2fxEXsDECR82uZhVWyedEOhm/cdgYduF+i8CO7+9TDrBKUc6IAyzpQP+V9THq807cvDIW6Mj3R1RSRd5cH7TO9IFbq8PfUo6wWl9KEU6IdC0ysHSCURtEs1dBD/DrtepA8BP9qpt3o3pymj1EBER7QrHKyIiiiTdTzk6FA2rQnGx7pkNbs4QGT58Hn2YZdOu0YPvSBtP6i+d4FTV1YNt2v8gHRBbg/M3Yto597pfUdC+b1wrndAuPLI98Eg0Fc7OnSmdECgjQfeKECIXD36c2b3FczK8+MGb2q7yNP0D1uhu0gX+K3h5vnSCU6isQjqhXVgsHRBj87cWYPh/rpPOCNRxyFbphHZhSPpa6QSnl0v3l04INKfsJ6ceKMS/L7R7HjwJTURERERE5AevZ7C4RDB+ZL6q/wwnarvQoN7SCU47BurfWKeymwdLBO96SbogpgbkbcSLZ+peInj6k+OkE8Kgf+5zVqX+HYUS0SidEKiqQfcmHEQuXl9gcYlg/PDh+abnyvOlEwLdt+Qnz+qrU/lFlnSCU99nVksnOHWSDgjDAumAGFta2gVj3rlGOiNYV/3bi/vggo7TpROcNoZ0f689I/cr6QSn8dD/6ALJ4RJBIiIiIiKiCPF6BotLBEmTs7N1P/B6/0u6Z9gAoPoQ/Qedrj29l3SCU1VXK53g9jvpgNganLsJH4y5Tzoj0P6vXS+d0C50TtT/9y83Qfeh1zNqu0onELUJZ7CIiIiIiIgixOsZLB+ewfLh2SHtn0NfXFsyXDoh0Khr9a9pf2vhXtIJTjYhWTrBKWeZdAH92LLqPJyy4NfSGYFeOfF+6QSnm68ZIZ3gtNyDDRpW1ut+UnOvlBLphDAMkA4gxby+wPIBL14iw4fP48ardQ/8BQ/of/C6qLP+82Mat2+XTiAPGWORmqh7E4kzJv1WOsFpAL6UTnB6ftso6QSnw3IWSicEenjLodIJYdC/pJ3kcIkgERERERFRhHAGiyhCCj/RPbOh+9STJo1luh+8BgAbCkknkIfSE+sxOHeDdEagJbkF0gntQmqC7plKACgLpUknBGqwidIJYeAMFu0eL7CijM9gRYYPn0dAd6MPf85L7txXOsEpe6X+if/kCv27mOHxV6QLYqpseyben6h7GfHyqx+STnAaDf3fx67N/0w6wWlHo+7vY6Uh/Qe6f33+cdIJTp3fWyGd4GQ75kgnuM1r+Zvo/htGRERERETkEa9nsGr7ZmDJH/eTzgg0upt0AVGTHefqf/A6wYMVF9lr9C8RrO3Ae2fa5OWV46yzpklnBNp75lnSCU5dsUA6wWl5Q4Z0glO/pCrphEA9U3SfKwkAHZ/+QjrBSf9iVQAlupdOt5bXF1iDsjZiymG6t5W9AD+TTmgXfFjetvHqg6QTAl120yTpBKdHHj5JOsGpOl//xUtVVyOdQD+SmVCLERm6989/YsfB0glOPhw/e/Gs86UTnC7d81PphECjM+dLJ4SB27TT7un/SYGIiIiIiMgTXs9grZibjQt6cYYoHviwycXPrh4pnRDo9T07Syc45R1VK53glDZziXRCu7BIOiDGSuo64O6Vx0pnBOrWVfdOqL749MBHpBOcplT2lk4I9LcNo6UTwlAuHUCKeX2BZZKSkJin+4fGKbPfl05w8mH5nQ+NT6y6Vzoh0DGHXy+d4JRWon9r3pQ9h0onOCVVerCL4GPSAbGVlVSLUZ107+h1c6dZ0glOp0D3TowAcEvJUdIJTr/tovt5wP5dNkknOP2136nSCU4Ny1dKJ8QtLhEkIiIiIiKKEK9nsGxDA0KbN0tnBPJh5oUi4+reuh8QH4gZ0glEcausPg0flBRJZwR6cf7+0glO/ZWfNwgAI7J1z1QCwMRS3X/Ws0u7Syc4cXaIgnAGi4iIiIiIKEK8nsEqGlaF4mLdd7M4g0XUAgcOky5w2jEwUzrBqbKbB9u03zVRuiCmuqaW4f/6634m9/dPniOd0C78Zar+4yYuPvQj6YRAKYn6zxuslA4g1by+wKL44cMugtr5cLG/9Az9B3Rmr9R/8VKX68EmF3Gm0RpUNqZIZwRq3I+7okXCE8c9Kp3g1C+5TDoh0LvJ+r8WX0UX6QRSjEsEiYiIiIiIIsTrGazFczK8uCtPbefDn/N5i9ZIJwTa9pbuB+wBoNvj+mdesj9aIJ3QLiyTDoixslA63tume4v/xMRG6YR24bGNP5dOcBrb9SPphEAHpi+XTnDiDNb/3959x8dRnXsD/z2WZblbNjbuBRMZTAvdlBAC4Y3p5g2EkoSavCYECNw4l5oEh1QIyU1yww2Q0CFAAgQIF3BMT0KzTTEY4Yorwr3Ili2rPO8fM7JGq905K2t2nzPS7/v56KPdqb8drfbsmXPmDMVJdQWLyCf37THSOkKsAZhnHaFD8P/KAPJR3UcNWH3EBusYsbpM2cs6QoewdvIQ6whO35rq9/V2Iv6fbBuBOdYRyGPsIkhERERERJSQVLdgcRRB8onvA3Gk4b24ZvLh1hE6hJoh/g/EgR91rlEEd91nGy59wu9W5P94+jDrCB3CUX9+xzqC0//0m2UdIda0LXtYR3D6GwZZRyCPpbqCxWuwiPK37ZRDrSM4lS/cbh3BqWzGfOsIHYLfVY3krdhajus/OM06Rizt6n+3rDToU7LNOoJTd8/PwZSX1FhHIGqXgnURFJGRIvKSiFSKyBwRuSKc/ksR+UhEZovI30SkPJw+RkS2isi74c9thcpGRETUhOUVERElqZAtWPUApqjq2yLSB8AsEZkOYDqAa1W1XkRuAnAtgKvDdRaqat5NUuwi2Hn43v0uDbaVl1hHcFp1iv9n0Pvu5/dIcABQM8T/44irrAO0UPDyakiPTbhmr2mJB0/S96tOt47QIZzX7yPrCE4bPR8wsk/JVusIRO1SsAqWqlYBqAofV4tIJYDhqvqPyGJvADhjZ/fBLoKdRxr+zr5XArtv8H/8u7GPeF7qAyib8YF1hA7Bp0GYi1FerdveCw9+MqF9QQusZCvHvUrC9VVfsI7gdP3gF6wjxKpu6GEdgahdivJpKiJjABwA4M2MWRcBeDbyfDcReUdEXhGRo3Jsa7KIzBSRmXWoLUheIiLqnApVXm3fyDPyRESdRcEHuRCR3gAeA3Clqm6KTL8eQbeMB8NJVQBGqepaETkIwBMisnd0HQBQ1TsA3AEAfWVACvrBUBJ8bx0CgHl1W6wjxFo20fOrmgH0WFFmHcFpzNw+1hE6ho3WAVorZHnV4zPDdPnGfsV4GTtNB/OkZRIuHPhP6whOyxr8/qwdU7rGOkIeRlsHII8VtAVLREoRFFYPqurjkennAzgZwNdUVQFAVWtVdW34eBaAhQDGFTIfERERwPKKiIiSU7AWLBERAHcCqFTVX0emH4/gIuGjVbUmMn0QgHWq2iAiYwFUwK9u+mQoDddgbTjX73s4Vdz/unUEp5L+/a0jONVv3OReiFKlGOVVY20JauaXFyR/UioOWmodwSkN3VbuXPN56whOX+hXaR0h1mvVFdYR8lBvHYA8VsgugkcCOBfA+yLS1L/rOgC/A1AGYHpQpuENVf0WgM8DuFFE6gE0APiWqq4rYD5KkTR0Ebyyyu/bylXeb53Abd1J/t9ccsC0hdYROoZV1gFaKHh51bNXLfY/zO97qC3ZOMA6gpP/CYH/3PV56whOKz0fRGJO1zRcs1hqHYA8VshRBP8FINtFH8/kWP4xBN0ziIiIioblFRERJcnvU+5EoQlXX2Idwak8BV3wfDfgf+daR3BqYBdB2gnbNpah8hm/L9OqGVtnHcEpDS1YM2qHW0dwmrF5rHWEWHM2DrWOkIdPrAOQx1JdweKNhjuPAbM3WEdwetbzboxHXzzZOoLTuvH+fySVVvt/FUhNGr6b/OAR6wRFNXbQSvzl4lusY8S6fukk6whOfo/VGli+fRfrCE7f9Xykwxd7+T9C330YaR2BPMa7ChIRERERESXE/9PFMebN7skWok6i8T2/RzwCgNPmT7SOEKv739+yjuA0coH/I0fpx8usI3QIfg/3kLwFmwfh5H9eah0jljb6f6+8Cqy2juD06lr/P8cm9FxgHSHWx7WDrCMQtUuqK1jsIth5pGEUQd9NONf/69hWHeX/sLd9K/0v+GuG+N+NEVc9YJ2gqPbuswavHnO7dYxYV1UdZR3BKQ0V8z+Nfdy9kLE69fszYlnZSusITv/kjYYpBrsIEhERERERJSTVLVjsIth58O/cfutusk7gtteN/o/K1LDS/y5KadDZ7iJfuWUXHPrWhdYxYl211zTrCE7zMcI6gtO3l5xqHSH1vj6Yo/JSuqW6gkWdR8kg/7tldXvU7wbhsUf7X2A17rOndQQnWbPWOgKlUXUJSl7pZ50i1g0b/R9FcBxmWUdwmrjLHOsIThVln1pHiPXqZv/LAqI4fn8jJCIiIiIiShG2YFEqPPPedOsIqTcR/nezXHVEf+sITkPWrLeO0DFstQ5QZH0agGP8fu+UVpZbR+gQDuq+xDoCERljCxYREREREVFCUt2C1TCwF9ZNOtw6RqwBd/l/3UsacJCL9pt326HWEZz2vG2jdQQn7dfHOoJTY88y6whuVdYBiqtf2TYcP9rv+/kNrdhgHcHp2Wv9b2X72YoTrSM4DSzbYh0h1ukDZlhHcPon9rWOQB5LdQVr/LDVeOsnf7COEWviXawYJOHahbOtIzh95/2zrCPEGnea/zcaXvL9I6wjOJWts07gVjPU73vcAADetg5QXEO6bsY1A1+zjhHrkFf9vhEyAOyOd6wjOH118JvWEZzKu9RYR4j1j037WEcgahd2ESQiIiIiIkpIqluweB+szuMXZ3/NOoLT1Q88Yx0h1k1PfMk6glP5Q43WEZzKX5hvHaFDWGAdoMgqN+2Kg170u4Wod7/ONvJIYczdNtQ6gtOEngutI8Q6s9z/LoKzMME6Anks1RWscfvVYNq0d61jxGIFMBnPPXm/dQQn3//WQ+H39R8AsGay39dUAsDKL4+zjuBUM0SsI7j9yDpAce3RZyWePuZ31jFiHffGJdYROoTz+vnfjfG+jQdYR4g1Y8No6wh5WGMdgDzGLoJEREREREQJSXULFrsIdh78O3cOA+/gqJudxTzrAEW2+P0++Maoz1nHiNVtiv8jZKbBRSd+0zqC05Kpfn/9E/F/oJ4RbMGiGH7/hzmwi2DnMe0Tv//OADCvzu9hby8ffaR1BKeFtxxmHcGp91L/G/5rhvj/5QTXPmqdoKiG7bsFP3zK76ETv/78IdYRnPy/ugn487N3WUdwWlTv99e/Z6v3s47g9E90t45AHvP/mwIREREREVFK+H0KgyhFrlr8ZesIDiutAziVbPN/cAbxf6BDpKB3TadTikYMK/H73kOUDN9bhwBgm/qdsU5LrCMQtYvf/2EOddqAqvrN1jGoCNLQ1bLLZwdYR3Dwv4I16jn/h4muHu1/t5D67uyc4Jvqxu54seYz1jFila5J9VcCb3xz9nnWEZwuGfeqdYRYS7f6Xp4CQLV1APIYS2EiIiIiIqKEpPp01cfv98EFno/KRJ1H43v+32fKd13+5f9gJv3+ZZ3ArZ91gDx8aB2gyNbNKcVfxg+xjhGr+xT/u+imwZDrrRO4/WbqsdYRYqVjFME51hHIY2zBIiIiIiIiSkiqW7Bqd+uJ+TceaB0jVsV5fg/LmxZyyL7WEZyee/J+6wixKu6/xDqCU89P/D+D3n9+nXUEp9ryFFwg/mDnGqa9714NOPYvft/KYdm2WdYRnOb/yjqB27EPz7CO4DSpz2zrCLGW1fe1juB0M/z/XkJ2Ul3B2rfPGrx1nN/3m5gI/wdnSAPfKy9pMPZq/2/iu+DX/t8HS7TUOoJTzVD/u9d0Nj26bMdneyy1jhHr9lf97jYGABV40zqCk++VFwDo7nkXvIXbB1tHIGoXdhEkIiIiIiJKSKpbsObN7pmK4bup/fh37hw+8903rCNQkSy0DlBkKz4diOtvvsg6RqyBk9ZYR+gQ/rjW/8G3NtT1tI4Qa/7GQdYRnMqw2DoCeSzVFaxx+9Vg2jS/Rx1jxYCoDQ7bzzqB04aKXtYRnLYM8/9aNvyic12DNXzIGvz0Kr+7tF875/9aR+gQGtX//7/P9FxlHSHW2lq/K4AA4PcVlWStYF0ERWSkiLwkIpUiMkdErginTxWRFSLybvhzYmSda0VkgYjMFZGJhcpGRETUhOUVERElqZAtWPUApqjq2yLSB8AsEZkezvsvVb0lurCI7AXgbAB7AxgG4HkRGaeqDbl2wC6Cnce0T/xuqQTYWtlZDHjc/wvYB1gHyINnd40reHm1YtVAXPPbbxQofjJKJ7KLYBJ+PNj/rs63bhhvHSFWt5Kc/0reYAsWxSlYBUtVqwBUhY+rRaQSwPCYVSYBeFhVawF8LCILABwKwP+hz4jgfyUwDRXANfv53/1uyOLe1hE6hs3WAZoVo7zSPg3QL65PMHXySro0WkfoEKoatltHcJrY2++b5JaK/xWsZ1FuHYE8VpRRBEVkDIADgB3jq14mIrNF5C4R6R9OGw5gWWS15chSwInIZBGZKSIz61BbwNRERNTZFKq8athUU8DURETkk4IPciEivQE8BuBKVd0kIn8A8GMAGv7+FYCLAGS7KrTVjRpU9Q4AdwBA2egROu/6QwsVPRHjvvWWdYQO4eiLJ1tHcDpgqt83lR7v/z1E8cJD/l8cXrP/KOsITrX9UnCj4YetA7RWyPJq+N7l+pWx7xQqeiLeWLebdQQn/2/zDTxZ7f9gPcNK/W5NPdHzFjYAeBZHWkcgjxW0giUipQgKqwdV9XEAUNWVkfl/BPB0+HQ5gJGR1UcA+CRu+yVbBf3fTcEXCWq3Hp9utY7gdPWgl60jxPrGPie6FzLW/9h66whO3T/1v+d96Sb/b4bsm0KXV1sauuGt9WOSjJy4ea+PsY7gtFvQk9NrDVnr3345sPty6wixXq6psI5A1C6FHEVQANwJoFJVfx2ZPjSy2P8F8EH4+CkAZ4tImYjsBqACAJt/iIiooFheERFRkgrZgnUkgHMBvC8iTVf/XwfgHBHZH0F3isUALgYAVZ0jIn8B8CGCEZ0ujRuRCQC6rt6CQbdxDIzOQGe8bx3B6YJRvt9ccpN1AKceT/j/HTUNwwD4f/7cOwUvrxrn1qP26E8LFD8Z3aeMtY7QITx/0RHWEZweuM7vyyta9bf10FDfxkIlrxRyFMF/IXs5/0zMOj8F8NNCZSIiIsrE8oqIiJJU8EEuCmncfjWYNo1DY5MffB+mfcLVl1hHcNq6q/9tL123+H9utWao/8cRP3zUOkFR9d+7Dqc/tso6Rqyb3+FIh0k49Z6XrSM4fbm3360vr20bZh3B6Q6wxZdyS3UFizcaJp88WL2LdYRYjWettY7g1O/O/u6FjPV5eZ51hA5hvnWAIltT2xt/WuT3qGOytId1hA5h9uYR1hGc9i5bYR0hVkU3v09GBFjBotyKch8sIiIiIiKiziDVLVhEPrlvj5HuhQwNAFtekhA7kgFRDiULatH/JL/b7bZNGWQdoUNYetFo6whO3566u3WEWCL+d8UeAf/v1UV2Ul3B4jVYncf830+wjuC06Mu3W0eINf6Ob1tHcKrr4/8Yfb2X+N/wXzPU/y8nuKZzXYM1ct/N+M3Tr1nHiHXSKwdaR+gQznr0ResITkf3WGQdIdai+n7WEZxuxr7WEchj/n9TICIiIiIiSolUt2BRMh5b/oZ1BKfT/b9mGBMv87u1svfTa6wjOG2eOdA6gtO2z1VbR3Aa0HurdQSnhdYBiqxUgCEl1iniXXbQy9YRnF7Yzf9Wg9c3+T9YyMzq3awjxDpjwAzrCE6L/ux3mQ8AWtXdOoJTQy//e67g4rb3uGAFq8B8H7obACYOO8w6glMajqPvowjO2Oz/v3v9SUusIzjN2TDEOoLT4J7+VwL9//qUrC2NJXiz1u9RMs/r5/8N3ad93Nc6gtNVg/9lHcFpfp3f5dWgki3WEZzqN3SzjuDUd5n/HdXqevufcWd0zFdFRERERERkwP9T2jF4H6zOIxV/5xf87sc4qvd66whOc3+2t3UEp96v+j0SHABssg5Arazc3he/WXqcdYxYK57xf/S7YfB7oBAAuPCjc60jOJ0/6nXrCLFGd/W/vBr37besI1CRzN2JddiCRURERERElJBUt2BxmHbyyheXWyeItdQ6QB56HLbNOoLT2pP3tI7gtGWYWEdw+4V1gOIa2309Hqj4i3WMWEf2+551hA6haq3/Q4xP772XdYRYf9z4OesITv2wwDoCeSzVFSx2EUxGyfgK6whOz7zwV+sIqZeG+2BJCu7iO+bR1dYRnHZp8H9UpkrrAEVWuXFXHPLsd6xjxDruS7OtIzgt/YF1ArfXj7rVOoLTOs8/IpYN8X8wk//3+29aR3DqO8/zoUsBNJRZJ8jDzW0fRZBdBPw6yQQAAB3DSURBVImIiIiIiBKS6hYsSkZDpf8X7aehpbJk0CDrCLFGrfb/4nB0ScHZtsYUNLORd7ptAEb93e+um8/X+z/IzDj4P7DARYtOt47gdOzAj6wjxHqveqR1BKeKy960jkBF8uFOrMMKFlFCGlb733XMe6y8UAe1vRxYeopax4j15YNmWUdw+sA6QB7mr/L7ZBsA7NFnpXWEWFU1/ncRBPy/3yDZYRdBIiIiIiKihKS6BYujCHYe0z7x+++cBkdfPNk6gtPSk6wTuPX9yP+PzZohfreUAACubvtFw2k2ss863HLMg9YxYk157HzrCE5j4ff9mwDgoYP/ZB3BaXBJnXWEWC/28v+ebPfB/26MZMf/bwpEAObVbbGO4DSutJd1hFjLJvp9/QcASA+/C30A0C4p+Nhk3wTvdJN6jCldZx0j1pRTn7KO4PS3a/zvfre4fhfrCE5lsso6QqzhKbjRMFjBohgshomIiIiIiBKSglOxufE+WJ3H5aOPtI6QehXgiEfkj0XWAYps+fu98Z9jDrOOEatqyhHWEZyGwv/RUG8//WTrCE5Lb/B/xFbfjcAc6wjksVRXsKjzSMM1WBMr/S5Ux5d/ah3Bafqjh1pHcBrzwFLrCB1DJzuMtbv1wIIfH2AdI9Y3PvuCdQSnV37VwzqC08PP3G0dwelvm/2+xmlC98XWEZyuhP8nJMgOuwgSERERERElhC1YlApp6Ara5bN9rCPEqnxvuXUEp5Fl/t+Hp377dusIlEKyvQtKlnW3jhHrT+p/V+wKvG0dwemMuWdaR3A6YbDf3dt+sOxU6wh5WGMdgDzGFiwiIiIiIqKEpLoFi/fBIp/oR53tsv3kNR64p3UEp42f6WkdwWnLMP+H5Mcv/mqdoKh2H7ASD3/119YxYp15+xTrCB3Cwsph1hGcugx53zpCrJ5d/e8pUG0dgLyW6goWRxHsPNIwyIXv9+o6/u/ftY7g1GOF/yNbjbl/iXUEp4HWAfJQaR2gyBZs2hUnT/+OdYxYXfbaah2hQ3hjkt8VaQD4tMHvz9pDevh/wvJGHGgdgTzGLoJEREREREQJSXULVhqkoeUlDa2Ax0861zqCk87wu8tFyU3+n08pX9hoHcFp5cRR1hGcyqrVOoLbI9YBiqtsdSP2uK3GOkas3z1+h3UEp8vh/0Acd2/wv0z9aMsQ6wixzhn4hnUEonZhBavA0lB5SQPfKy8AsOHcw60jxBp79evWEZxK+ve3juDUsHGTdQRKoxENaLjZ7/fO8S/53YURACrg/0ij2xpLrSM4Tez/gXWEWE+tT0P3u1rrAOSxgp3SFpGRIvKSiFSKyBwRuSKc/oiIvBv+LBaRd8PpY0Rka2TebYXKRkRE1ITlFRERJamQLVj1AKao6tsi0gfALBGZrqpnNS0gIr8CsDGyzkJVZZMPpdL1P7zXOkKsKyf4381y1LP+dxHs+e951hE6hvXWAVooeHnVqILaer87jZy8z2zrCE5zrQPkYe7mwdYRnCb28btXSMXAldYRnK7DodYRyGMF+7RX1SoAVeHjahGpBDAcwIcAICIC4EwAx+7sPup2746qX49PIG3hDD2ts42V1Xmd2svv6ytuvexN6whOm888zDqCU83h46wjOG0b4PcIYQCAB6wDNCtGedWvdBuOH/phAmkL58H5B1tHcBoBv2+QCwCHl/s/Al7PLnXWEWJN27y3dQSidinKVe8iMgbAAQCi3/COArBSVedHpu0mIu+IyCsiclSObU0WkZkiMrNhk99faImIKF0KVV5tWe//fX2IiCgZBe+vICK9ATwG4EpVjV7hew6AhyLPqwCMUtW1InIQgCdEZO+MdaCqdwC4AwD6ygBlC1HnkIbRGH3n+yAcALB1V/9vkDvy4aXWEZx6WQdIqUKWV2Vjh+td7x9R2BfQTkeMXWgdwcn/jmPAOX39bqkEgDr1e6TR0d3WWEfIQx/rAOSxglawRKQUQWH1oKo+HpneFcCXARzUNE1VaxEOyaKqs0RkIYBxAGbm2v64/WowbZrfX7w5imAyeBzbrxz+jyJYfth+1hGc1hw72jqC05Zh/ldU8QvrAC0Vurwa3msDfnLwEwVKn4wb/nq2dQSnMSn4HDv6rcnWEZwOGLrCOkKsjzcNsI7g1Bv+dwUlO4UcRVAA3AmgUlUzb2t+HICPVHV5ZPlBIlISPh4LoALgu5eIiAqL5RURESWpkC1YRwI4F8D7TUPbArhOVZ8BcDZadrcAgM8DuFFE6gE0APiWqq4rYD6iRP3s47esI8T6yv9ebh3BqccK/wdn6LbRvYy1+l5+d//xUMHLq75d6nBcz+Vxi5i7cc9q6wgdwhdH+z/S6Pm7/Ns6Qqy6If6XBTc0N2oTtVLIUQT/BSBrPxVVvSDLtMcQdM8gIiIqGpZXRESUJL9vyuEwb3ZPXpvTSaRjkItu1gFiDXqzKIOGtsvWXa0TuA17crF1hA5hvnuRDqWyelcc+tKl1jFinTje/yHQ0/C+uXjgq9YRnHpKg3WEWB/WDbSO4NR1xHDrCE71y/2+1q4jS3UFS8q6oeuIMdYxYtUvWmwdoUNIQ0X6kHf9LrDK7/f/4vD+B/l/7xOtrbWO4CRdU/3R3iH1KqvFobsvto4R65kP/f//q8Db1hGc7lp7pHUEp6/0n2EdIdZbW3a3juDEygvF8f+UNhERERERUUqk+jSn1m5nCxF5Y8b+/l+U6zud5X8XJb/bKclX9R81YP2Rfo/b1HvKntYROoQPzx9nHcHpoql7WUeIJeL/QD0j4H95RXZSXcGiziMN12A9WL2LdYRYP3hzknUEpx6V3a0jOI25f4l1hI5hmXWA4qrdrSfm33igdYxY3Xtuci9ETjc9fY91BKd1DT2tI8Qq77LVOoLT1ZhgHYE8xi6CRERERERECWELFqXC0RdPto7g9Mrtd1hHiPW76WXWEZy6bfG/A55u9f/MKvmnpKQB5eVbrGPE6l223TpCh/Bs9b7WEZzO6/eOdYRYr20bZh2BqF1YwaJU8L3ykgYDX1pqHcGp6pRR1hGc+paWWkegFNLqrqh71e9uxD1O8P8zIg0+12uudQSnbZ5f4rTB8y6MRC7sIkhERERERJQQtmARdRKV14ywjuDUvUqsI7ixBYt2QmOZYvPYeusYsfYp/8Q6gtMH1gHyMLZrjXUEp2r1+7N2zzL/34vAEOsA5DFWsCgVTtzz89YRnI7+d5V1hFinHj7LOoLTjJsOto7gpDX+f3ki/0hXRdlAv6/fe2lFhXUEp0Hwv/vdf689wjqC06G9F1lHiLVH6SrrCETtwi6CRERERERECRFVz690jCEiqwHwpjREROkzWlUHWYcoFpZXRESp1ebyKtUVLCIiIiIiIp+wiyAREREREVFCWMEiIiIiIiJKCCtYRERERERECWEFi4iIiIiIKCGsYBERERERESWEFSwiIiIiIqKEsIJFRERERESUEFawiIiIiIiIEsIKFhERERERUUJYwSIiIiIiIkoIK1hEREREREQJ6WodoD0mHtNL16xrCJ8pNDIv+qzl9IxlNDpdYpZt+i0Z8zLWUclYJ3P55m00TW/xXPNbx73fbK8ncz+5t53fck1P4o6btJiRc17GvpC5rxY5Wi/Tev3cGVrPzzUvfr22zRPH/LZvW9qxbrb5EjOvTdvWnK+2TdvOuo08/w6t1s21Xq5jkO3NlMe+c+9Xc0yP25ZrHc0xPZ/9xqyb+Y8aNz3rP13L5TTHctVYP01Vj8+2p46oEOVVdJm48iq6TrvKjcznmt86Wcs0dZSlaS6vIg+SLq92TC5EebVjfvLlFeAos3Zi2xIzL+9tJ1RetciT537bvF5S5VU435vyasd0R3mVa7uelleprmCtXdeAN6eNAAA0aCMaw6PUiEY0qO543oDIY1U0hus3QNEQHsdGAA0QNGrTPEFD+PZr1ObHDejS8rl2QWPYENgAQYN2CbcnaECXyPMuaNDoNrrseNw0vTFcvjGy7YZw28E+mx53CbM273fH44z1G7Vl7h1ZVXbMa9pe034aW2yjedvB+i2Xi05v3l7GY7ScHp2nO15783TV1o91x98psk5kuobbal4vUoBHClRVCf6fsiyHyPbQYnrzeoHIYw3Wa9pW9Lk0PQ+33XKd5uktlkOkEMpYp+X2si8XXWbH4+hzZFu/9fPMea1fT459Nc3L3E+Obcfts+U8zb1OzteuLaZLI7JsQ3Nk0Nbba8yWIXM5RdMHjGjkg7vFfjTI0uI17XgjZ6yviLz5Wy4Xt05jruWaHjc6lmtsPb1RodrYcp3Ichpdp1GDfQDQHI+f10cHohNxlVfBY21TeQUEh9pVXgHN5VNceRXss0uLcsJVXu1Y31FeZX3sKK+a9xNfXjVnaF7GVV41T48vr6KP8y2vgsfu8io6Pa68al4nOt1RXoWPneUV0FwORZfJUga0Wg6O8ipjfcnYdmwZ0vTYub02lFfRx9HnMfvJt7xqmSdbWeEqu7T1MWlsvUxseRWdHlde7ZieUbZkXU4jZae6y6tc013rNGZbrulxo2O5mPIquk5ceRXuJ8nyil0EiYiIiIiIEsIKFhERERERUUJYwSIiIiIiIkoIK1hEREREREQJYQWLiIiIiIgoIaxgERERERERJYQVLCIiIiIiooSwgkVERERERJQQVrCIiIiIiIgSwgoWERERERFRQljBIiIiIiIiSggrWERERERERAlhBYuIiIiIiCghrGARERERERElhBUsIiIiIiKihLCCRURERERElBBWsIiIiIiIiBIiqmqdYaeJyAcAtlnnSIGBANZYh/Acj1F+eJzyw+Pk1l1V97EOUSwsr/LG/5388Di58Rjlh8fJrc3lVddCJSmSbap6sHUI34nITB6neDxG+eFxyg+Pk5uIzLTOUGQsr/LA/5388Di58Rjlh8fJbWfKK3YRJCIiIiIiSggrWERERERERAlJewXrDusAKcHj5MZjlB8ep/zwOLl1tmPU2V7vzuJxyg+PkxuPUX54nNzafIxSPcgFERERERGRT9LegkVEREREROQNVrCIiIiIiIgSktoKlogcLyJzRWSBiFxjnccHIjJSRF4SkUoRmSMiV4TTB4jIdBGZH/7ub53VByJSIiLviMjT4fPdROTN8Dg9IiLdrDNaEpFyEXlURD4K31OH873Umoj8R/j/9oGIPCQi3fleAkTkLhFZFd7/qWla1vePBH4Xfp7PFpED7ZInj+WVW67yi1rLLLuotWzll3Um32Qru6wz+aAtZVecVFawRKQEwK0ATgCwF4BzRGQv21ReqAcwRVXHAzgMwKXhcbkGwAuqWgHghfA5AVcAqIw8vwnAf4XHaT2Ab5ik8sdvATynqnsC+CyCY8X3UoSIDAfwHQAHhzchLAFwNvheAoB7AByfMS3X++cEABXhz2QAfyhSxoJjeZW3XOUXtZZZdlFr2covCsWUXdS2siunVFawABwKYIGqLlLV7QAeBjDJOJM5Va1S1bfDx9UIPlCGIzg294aL3QvgNJuE/hCREQBOAvCn8LkAOBbAo+Einfo4iUhfAJ8HcCcAqOp2Vd0Avpey6Qqgh4h0BdATQBX4XoKqvgpgXcbkXO+fSQDu08AbAMpFZGhxkhYcy6s8xJRfFJFZdlFrMeUXtZRZdn1inMcLbSy7ckprBWs4gGWR58vBD+IWRGQMgAMAvAlgsKpWAUEhBmBXu2Te+A2AqwA0hs93AbBBVevD5539PTUWwGoAd4ddUf4kIr3A91ILqroCwC0AliKoWG0EMAt8L+WS6/3TkT/TO/JrK4iM8otayiy7qLVc5ReFspVdqvoP21Rea/N3n7RWsCTLNI43HxKR3gAeA3Clqm6yzuMbETkZwCpVnRWdnGXRzvye6grgQAB/UNUDAGxBJ+8OmE3YD3sSgN0ADAPQC0FXsEyd+b2Uj478/9eRX1viWH7llqPsotZYfjlkK7tE5Ou2qTqWtFawlgMYGXk+AmzaBACISCmCwulBVX08nLyyqbtN+HuVVT5PHAngVBFZjKC7zrEIzgqWh03lAN9TywEsV9WmM8iPIiiw+F5q6TgAH6vqalWtA/A4gCPA91Iuud4/HfkzvSO/tkTlKL+oWauyS0QesI3kpVzlFzXLVXZRdm3+7pPWCtYMABXhSF3dEFyY95RxJnPhdUR3AqhU1V9HZj0F4Pzw8fkAnix2Np+o6rWqOkJVxyB477yoql8D8BKAM8LFOvVxUtVPASwTkT3CSV8E8CH4Xsq0FMBhItIz/P9rOk58L2WX6/3zFIDzwtEED0PQXaXKImABsLzKQ0z5RaEcZRdbHTLElF/ULFvZxYFAcmvzdx9RTWdPBRE5EUGrQwmAu1T1p8aRzInI5wD8E8D7aO6ffR2Cfux/ATAKwT/VV1Q18wK+TklEvgDge6p6soiMRXBWcACAdwB8XVVrLfNZEpH9EVxI3Q3AIgAXIjgpw/dShIj8CMBZCEZBewfANxFcY9Op30si8hCALwAYCGAlgBsAPIEs75+wgP89gpGbagBcqKozLXIXAssrt1zll6o+Y5fKX9GyyzqLj7KVX6q63jaVX7KVXZ2tnMqmLWVX7HbSWsEiIiIiIiLyTVq7CBIREREREXmHFSwiIiIiIqKEsIJFRERERESUEFawiIiIiIiIEsIKFhERERERUUJYwaKiEpHXRUQjP+MLuK+XM/aV+XNBofadNBEZkyV/g4isFZHnReSUdm5/avhzQUKRs+2jj4isDLNfGJl+toh8ICJbwt9nZln3rnD+6Czz7gu3yeGciSjVws/hzM/67SLyiYg8JSJHFWi/iyP7m5Ex7/jIvHsi0y/IkrVORFaIyCMisnfGdjLLsTUiUpaxzMEZyyzOmN9HRG4QkdkiUi0im0VkiYi8ICK3ZCx7j+M7wNSEDh9RK6xgUdGIyO4ADsuYfK5Flg6iC4L7LH0RwJMiMqkd27oh/LkggVy5XANgVwT3kHgAAMKbyv4ZQB2CO8vXAXhIRCY0rSQiR4S5fqKqS7Js92cAFMAJInJcAfMTEVkoBTAUwCkAXhSRgwu8v4NF5NSdXLcrgGEAzgTwbxEZFbPsLgC+kjHtklwLi0gpgnulTQWwL4DeAHohuDfRsQCu3MnMRIljBYuKKdsd578a3mS00I5RVcn4uce1koh0b8/8tmjDtpaoqgDoh+BGigAgAC5LKkvSRKQngEvDp3eral34+DQE2e9R1dcB3IPgc+m0cL2uAP4AYC6AX2Xbtqp+hKDQBYDvFSI/EZGBH4Wf9f0BTAundQVwdhH2PbWNZfO9YdbRAGaF0/rBfRJ1R4VKRPoh/rVNAvDZ8PFdAIYA6AFgTwAXovkYZXNhlu8AUx3ZiHYaK1hUTE0VrK0AHg4fjwZQkC4PbRHpHrFYRI4SkddEZCuA2/KZHy5zgYj8O+y2UCsiC0XkNyIysC37ypeqbgLw35FJLc4UisgfRORtEVkddtuoFpG3ROSSpoKzqYtHZLWjI90nXo5sa7yI3B92/dguIqtE5FER2S/PuGchKGyB5r89AHQLf9dl/G6afjmA/QBcqqrbY7bftM0vZetGSESUVqq6AcCTkUmJndjLoQHAAQhPdLWFqi4FcF9kUq4WrBUA6gEcESlHzgfQE8DiHOtURB6/oKorVXWbqs5V1XtU9aS25iUqFFawqCjCrmCfCZ8+B+DeyGyfugkOAvAPAIcjeyGWdb6I3A7gbgBHIOi20A3AWABXAJgpIkN2Yl/5iJ5hXJUx70IEheRABGc9ewM4BMD/APhh3jsQ+RyCM5JfR9D1ozTMfjqAN/O8JmBi+HtN2OLU5MXw9yQR6YPgDCUAvCAiwwD8CMCfVfVFxGtqwRIAX8ojDxFRKoQtO9HrbJ/MtWxCHgx/t7UVq0lcudTkUwB/Dx9/K+P37TnWWRZ5fJeIPCEi3xORw8PeDkTeYAWLiiXaPfBRAC8A2BA+PyPzQtcCeCnLBa7lWZbrCeBVBJWj3gB+6pofXiM0OZy/BMD+CK6NujucNhrAjTuxr1gi0hdBC0+ThzIWuQjBGb8+CCp8+wFYHs67QkQkPOsXLQxfiXSf+EI47Y8IumEsAXAQgDIEFbfVCCqGt+YR95Dw9+zoRFV9GsCPARwDYBOALyDoFvMMgP9CcG3VlMhrLs2x/Q8RnA0FgAk5liEiSpMbwh4GGwCcEE77papOL/B+fwagFkGZcUZbVhSRkWg+adoA4K8xizf12vi6iJwMYDyCHi735lj+b2hu3SpDcELulwBeA/CJiMR1k787y3eA/Z0viGgnsYJFBRd+KT4rfFoL4OnwGpyms1flAE62yJbDhar6sapuUdX5ecyPZv+tqr6nqusBfBdBBQEATtzJfWUzOix0NwL4BoBtAH6C4FqlqAYE12gtDZeZDWBEOK8/ggEnYolIBYL+7UBz3/paAO8gaMUCgH1ztNBFNc1fkzlDVX+IoIK5O4A+qjo1HKziTADfB7BBRG4VkY0AtonIe5mtZqraCGBdxr6IiDqa/xSRye7F2mUFgDvCxzcgv++K54fl0lIEJ+I+BXC2qs6OWWc6gAUITgI2VaoeBrA+28KqugXAoWG2tRmzBwH4bxFpc7dGokJgBYuK4XgE3dQA4G0Ao0RkHwDvRZYpdDfBbINcbMiy3CpV/SRmO9nmD4o8Xtr0INz+pvBptsqMa1/56oJgJKUdRORsBAXV0QgqU9n+13vksW1nJSy0S57LadaJqttVdZGqbheRbgB+j6AS9z8ArgfwbQSF8bkIupo+KSKZ+yzGYClERMXSNMhFdwBfjUz/uYgU+vvbzxG0Ju2N4GRXW5WFPzmpqqK5Ijcg/J15ojBzndWqejGAwQh6RlyFltdsZY5K2CTbIBfvOl4D0U5jBYuKIdo98HAA74c/0XtWnCAiA2Bv607MXx15vOOC3rALYt/wabZ+6K595bIEwf/ungDmIOj+9x9oOURtdCSmywH0CAvqt9u4r2ju6VkKKAHQRVXnOLazMvw9KHapwFUAxgG4RFUb0Nw15ieq+mcAzyOoNO4Y8j/8stE/Y19ERKmnqrWq+hCaewAMQP4nv3Z2n1WIdOHLY5V7EVyf+yUA1Qg+j+8VkUNi1wq60teGj2ep6oxcC4bX6Tbla1DVmar6SwBfiyzmw/cIIlawqLDC64TyuZ9GN0TOkkmOmwx66unI4++IyL5h5eoWNLeq/G+SO9TAXADnoblVaGpkxML6yOKbAIgEN/c9IMcmm7pbjBaRpooKwm6L88Kn/0dErhSR8vDnYBH5IVqOCphLU6EZO+qgiIwBcB2AP6rqm+HkxvB35kiDDZFV90IwkEd0X0REqSciZSJyDpp7gmxD2I1OWt689+WEd30TgBoAJfksrKr14fVhN4STSgD8xrHOGgTX4T4Z/o5zlojMCkfCHSci3cIy75zIMpX5ZCUqNFawqNDOQPMIeY9kaf2YGFm2kN0Esw1yEfvBny9VfQ3N3RzGILjWaT2C66OAoMXphtZrJrLvt9FcwemLoHICBBcDN7kXQSF5K4K+9dm8Ef4eA2CdtLzL/WQEBToQDDyxPvyZgWCUv8F5RG26P8lAEdkzZrnfAdgC4NrItKbXcpmIHIPgxsorAbweWSZ6TVahLwAnIiqGpkEutiG4IXuT21S1Nsc6iVHVlQi6abfVrWjutneEOG5arKo/VdXTVDWf0REPDDPNRdDytRrN94DcgJa3LonKNsjFE3nsj2insIJFhRbtWnBflvnPo/lL/xEiMrbwkZIX9gm/EMGX/s0IWlkWAfgtgINV9dMC7v77aG7V+baIjFLVBxF0G/wYQeE8E0FXu4U5tnE5gGeQ5eJiVX0FwUXL9yEYhbAOwYASsxEUZtdlrpPFI2i+Hi3rjSTDQvgUAFep6rrIrFsQjGp1MoCnwv2eqKobI8s0bfNFVc31GomI0kgRfH6+gaAyMSV+8UTdjKBMy1t4z8LorUB+ltA1Y88BuAbB7U0+RnAyrg7B8O33A5igqh8nsB+idpPgGkMiosISkZ8jKByXAthdVesdq+S73T0RDNMuAI5X1WmOVYiIiIgKhi1YRFQsP0cwaMYoJNsd9DoElavnWLkiIiIia2zBIiIiIiIiSghbsIiIiIiIiBLCChYREREREVFCWMEiIiIiIiJKCCtYRERERERECWEFi4iIiIiIKCGsYBERERERESXk/wPXZr5smnAB8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a0380a1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set carbon dioxide case to 400 ppm\n",
    "casenum = 1\n",
    "\n",
    "# Plot Error Rate and NRMSE\n",
    "fig2 = plt.figure(figsize=(12, 12))\n",
    "\n",
    "plotnum = 1\n",
    "plotmetrics.heatmap_subplot(error_heatdata, casenum, 0, 100,\n",
    "                            plotnum, 'A.  Error Rate (%)', highperform_num)\n",
    "\n",
    "plotnum = plotnum + 1\n",
    "plotmetrics.heatmap_subplot(nrmse_heatdata, casenum, 0, 10,\n",
    "                            plotnum, 'B.  NRMSE', highperform_num)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Figure 2.__ FATES model performance as measured by (A) error rate and (B) normalized root mean square error (NRMSE) for simulations run with 400 ppm atmospheric carbon dioxide. Performance metrics are shown for leaf area index (LAI), above-ground biomass (AGB), basal area (BA), gross primary productivity (GPP), latent heat flux (LH), sensible heat flux (SH), and weighted averages across variables using three weighting approaches:  even (Av$_E$), favoring structural variables (Av$_S$), and considering correlations between individual performance metrics (Av$_C$). Top panels highlight the performance of the 10 highest-perfoming parameter sets. Bottom panels show the performance of all 287 parameter sets we tested."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results\n",
    "This analysis identifies three high-performing parameter sets for use in future FATES simulations. We recommend the highest-performing parameter set (parameter set number 86; Figures 1 and 2) as the primary parameter set for future experiments. The next two highest-performing parameter sets (parameter sets numbers 151 and 260; Figures 1 and 2) are recommended for testing the sensitivity of experiments to model parameterization. Parameter set number 260 is the second highest-performing parameter set and is similar in parameter values and resulting ecosystem properties to parameter set number 86. Parameter set number 151, on the other hand, differs more substantially in its parameter values and results in a different combination of ecosystem properties. In particular, parameter set number 151 performs well in above-ground biomass and basal area measures across carbon dioxide levels, but results in low leaf area index and high gross primary productivity compared to observations. See Kovenock (2019) for further details. \n",
    "\n",
    "These three high-performing parameter sets are made publicly available through the University of Washington ResearchWorks digital repository at http://hdl.handle.net/1773/43779."
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    "## Further Information\n",
    "__Details of the parameter ensemble and analysis herein:__\n",
    "\n",
    "Kovenock, M. (2019). Ecosystem and large-scale climate impacts of plant leaf dynamics (Doctoral dissertation). Chapter 4: \"Within-canopy gradient of specific leaf area improves simulation of tropical forest structure and functioning in a demographic vegetation model.\" http://hdl.handle.net/1773/44061\n",
    "\n",
    "__Details of the Functionally Assembled Ecosystem Simulator (FATES):__\n",
    "\n",
    "GitHub code repository: <br>\n",
    "https://github.com/NGEET/fates\n",
    "\n",
    "Fisher, R. A., Koven, C. D., Anderegg, W. R., Christoffersen, B. O., Dietze, M. C., Farrior, C. E., et al. (2018). Vegetation demographics in Earth System Models: A review of progress and priorities. Global Change Biology, 24(1), 35–54. https://doi.org/10.1111/gcb.13910\n",
    "\n",
    "Fisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell, N. G., et al. (2015). Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes. _Geoscientific Model Development, 8_(4), 3293–3357. https://doi.org/10.5194/gmdd-8-3293-2015\n"
   ]
  },
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   "source": [
    "## References\n",
    "\n",
    "Baraloto, C., Molto, Q., Rabaud, S., Hérault, B., Valencia, R., Blanc, L., et al. (2013). Rapid simultaneous estimation of aboveground biomass and tree diversity across Neotropical forests: a comparison of field inventory methods. _Biotropica, 45_(3), 288–298. https://doi.org/10.1111/btp.12006\n",
    "\n",
    "Condit, R. (1998). Tropical forest census plots. Berlin, Germany, and Georgetown, Texas: Springer-Verlag and R. G. Landes Company.\n",
    "\n",
    "Condit, R. S., Aguilar, S., Perez, R., Lao, S., Hubbell, S. P., & Foster, R. B. (2017). Barro Colorado 50-ha Plot Taxonomy as of 2017. https://doi.org/10.25570/stri/10088/32990\n",
    "\n",
    "Condit, R., Lao, S., Pérez, R., Dolins, S. B., Foster, R., & Hubbell, S. (2012). Barro Colorado forest census plot data (version 2012). Center for Tropical Forest Science Databases. https://doi.org/10.5479/data.bci.20130603\n",
    "\n",
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    "\n",
    "Faybishenko, B., Paton, S., Powell, T., Knox, R., Pastorello, G., Varadharajan, C., et al. (2018). QA/QC-ed BCI meteorological drivers. United States: Next-Generation Ecosystem Experiments Tropics; STRI; LBNL. https://doi.org/doi:10.15486/ngt/1423307\n",
    "\n",
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    "\n",
    "Fisher, R. A., Koven, C. D., Anderegg, W. R., Christoffersen, B. O., Dietze, M. C., Farrior, C. E., et al. (2018). Vegetation demographics in Earth System Models: A review of progress and priorities. _Global Change Biology, 24_(1), 35–54. https://doi.org/10.1111/gcb.13910\n",
    "\n",
    "Fisher, R. A., Muszala, S., Verteinstein, M., Lawrence, P., Xu, C., McDowell, N. G., et al. (2015). Taking off the training wheels: the properties of a dynamic vegetation model without climate envelopes. _Geoscientific Model Development, 8_(4), 3293–3357. https://doi.org/10.5194/gmdd-8-3293-2015\n",
    "\n",
    "Hubbell, S. P., Foster, R. B., O'Brien, S. T., Harms, K. E., Condit, R., Wechsler, B., et al. (1999). Light-gap disturbances, recruitment limitation, and tree diversity in a neotropical forest. _Science, 283_(5401), 554–557. https://doi.org/10.1126/science.283.5401.554 \n",
    "\n",
    "Koven, C. D., et al. (_in review_). Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama. _Biogeosciences Discussions_. https://doi.org/10.5194/bg-2019-409\n",
    "\n",
    "Kovenock, M. (2019).  Ecosystem and large-scale climate impacts of plant leaf dynamics (Doctoral dissertation). http://hdl.handle.net/1773/44061\n",
    "\n",
    "Meakem, V., Tepley, A. J., Gonzalez-Akre, E. B., Herrmann, V., Muller-Landau, H. C., Wright, S. J., et al. (2018). Role of tree size in moist tropical forest carbon cycling and water deficit responses. _New Phytologist, 219_, 947–958. https://doi.org/doi:10.1111/nph.14633\n"
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