{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8725437a",
   "metadata": {},
   "source": [
    "# Overview of the evaluation module\n",
    "\n",
    "\n",
    "This notebook demonstrates the evaluation of four bias adjustment methods - ISIMIP (Lange 2021), CDFt (Vrac 2017), Quantile Delta Mapping (Cannon et al 2015) and Quantile Mapping - to the variables tas (daily mean 2m surface temperature, K) and pr (total daily precipitation flux, kg/m2/s). ISIMIP is additionally applied to tasmin (daily minimum 2m surface temperature, K) and tasmax (daily maximum 2m surface temperature, K).\n",
    "\n",
    "The notebook is structured along the following three components of the module:\n",
    "\n",
    "- **Evaluating the bias adjusted model on a validation period**: In order to assess the performance of a bias adjustment method, the bias adjusted model data is compared to observational / reanalysis data. This component provides insight into the adjustment of marginal biases, as well as temporal, spatial and spatiotemporal metrics.\n",
    "- **Investigating whether the climate change trend is preserved**: Bias adjustment methods can significantly modify the trend projected in the climate model simulation (Switanek 2017). Have a look at the documentation of individual debiasers to see whether the method is explicitely trend-preserving or not. This component shows the bias in trend of different metrics between the raw and bias corrected climate model.\n",
    "- **Testing assumptions of different debiasers**: Different bias adjustment methods rely on different assumptions - some are parametrics, others non-parametric, some bias correct each day of the year separately, others are applied to all days of the year in the same way. One of the reasons that a bias adjustment method could fail in a given use-case is that these assumptions are not met. This component provides a starting point for assessing the validity of different assumptions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5324e8",
   "metadata": {},
   "source": [
    "## Set-up and data pre-processing\n",
    "\n",
    "This step requires the user to have downloaded and preprocessed observational or reanalysis data (such as ERA5) as well as simulations of a climate model during a historical and future (or application) period. Necessary pre-processing steps include regridding the datasets to the same area and grid in space and time and conducted checks for corrupted or missing data.\n",
    "\n",
    "For the purpose of this demonstration, we will work with the testing data uploaded to the github folder of ibicus. This data is already preprocessed and numpy arrays of data have been extracted. We can get it using: \n",
    "\n",
    "```\n",
    "!wget https://github.com/ecmwf-projects/ibicus/blob/main/notebooks/testing_data.zip -c\n",
    "!unzip testing_data.zip\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa46cfb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import iris\n",
    "import math\n",
    "\n",
    "from scipy.stats import norm, laplace, gamma, beta\n",
    "\n",
    "from ibicus.variables import *\n",
    "from ibicus.utils import gen_PrecipitationHurdleModel, gen_PrecipitationGammaLeftCensoredModel\n",
    "\n",
    "from ibicus.debias import ECDFM, ISIMIP, QuantileMapping, DeltaChange, CDFt\n",
    "from ibicus.debias import QuantileDeltaMapping, ScaledDistributionMapping, LinearScaling\n",
    "\n",
    "from ibicus.evaluate import assumptions, correlation, marginal, multivariate, trend\n",
    "from ibicus.evaluate.metrics import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd381dd",
   "metadata": {},
   "source": [
    "The second component of the evaluation requires a split of the historical period into training and validation period."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "02365cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(variable, path = \"testing_data/\", split_ratio = 0.7037):\n",
    "    \n",
    "    data = np.load(f\"{path}{variable}.npz\", allow_pickle = True)\n",
    "    \n",
    "    cut_off = math.floor(data[\"obs\"].shape[0]*split_ratio)\n",
    "    \n",
    "    obs = data[\"obs\"][0:cut_off, :, :]\n",
    "    obs_validate = data[\"obs\"][cut_off:, :, :]\n",
    "    cm_hist = data[\"cm_hist\"][0:cut_off, :, :]\n",
    "    cm_validate = data[\"cm_hist\"][cut_off:, :, :]\n",
    "    cm_future = data[\"cm_future\"]\n",
    "    time_obs = data[\"time_obs\"][0:cut_off]\n",
    "    time_obs_validate = data[\"time_obs\"][cut_off:]\n",
    "    time_cm_hist = data[\"time_cm_hist\"][0:cut_off]\n",
    "    time_cm_validate = data[\"time_cm_hist\"][cut_off:]\n",
    "    time_cm_future = data[\"time_cm_future\"]\n",
    "    \n",
    "    return obs, obs_validate, cm_hist, cm_validate, cm_future, time_obs, time_obs_validate, time_cm_hist, time_cm_validate, time_cm_future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e72ea97",
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_obs, tas_obs_validate, tas_cm_hist, tas_cm_validate, tas_cm_future, tas_time_obs, tas_time_obs_validate, tas_time_cm_hist, tas_time_cm_validate, tas_time_cm_future = get_data(\"tas\")\n",
    "pr_obs, pr_obs_validate, pr_cm_hist, pr_cm_validate, pr_cm_future, pr_time_obs, pr_time_obs_validate, pr_time_cm_hist, pr_time_cm_validate, pr_time_cm_future = get_data(\"pr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729760e1",
   "metadata": {},
   "source": [
    "## Initializing and applying the debiasers\n",
    "\n",
    "The following section initializes and applies the debiasers ISIMIP, CDFt, Quantile Mapping (QM) and Quantile Delta Mapping (QDM) to the chosen variables. For a more detailed explanaition of the bias adjustment methods we refer to the documentation, and for some examples on how to customize different debiasers, we refer to the notebook on adjusting bias adjustment methods."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a222317a",
   "metadata": {},
   "source": [
    "**ISIMIP**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "85c557ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/debias/_debiaser.py:536: UserWarning: progressbar argument is ignored when parallel = True.\n",
      "  warnings.warn(\"progressbar argument is ignored when parallel = True.\")\n"
     ]
    }
   ],
   "source": [
    "tas_debiaser_ISIMIP = ISIMIP.from_variable(variable = 'tas')\n",
    "tas_val_debiased_ISIMIP = tas_debiaser_ISIMIP.apply(tas_obs, tas_cm_hist, tas_cm_validate, time_obs = tas_time_obs, time_cm_hist = tas_time_cm_hist, time_cm_future = tas_time_cm_validate, parallel=True)\n",
    "tas_fut_debiased_ISIMIP = tas_debiaser_ISIMIP.apply(tas_obs, tas_cm_hist, tas_cm_future, time_obs = tas_time_obs, time_cm_hist = tas_time_cm_hist, time_cm_future = tas_time_cm_future, parallel=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8390a3c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:3133: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  aest = (3-s + np.sqrt((s-3)**2 + 24*s)) / (12*s)\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:3132: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  func = lambda a: np.log(a) - sc.digamma(a) - s\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:3133: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  aest = (3-s + np.sqrt((s-3)**2 + 24*s)) / (12*s)\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:3132: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  func = lambda a: np.log(a) - sc.digamma(a) - s\n"
     ]
    }
   ],
   "source": [
    "pr_debiaser_ISIMIP = ISIMIP.from_variable(variable = 'pr')\n",
    "pr_val_debiased_ISIMIP = pr_debiaser_ISIMIP.apply(pr_obs, pr_cm_hist, pr_cm_validate, time_obs = pr_time_obs, time_cm_hist = pr_time_cm_hist, time_cm_future = pr_time_cm_validate, parallel=True)\n",
    "pr_fut_debiased_ISIMIP = pr_debiaser_ISIMIP.apply(pr_obs, pr_cm_hist, pr_cm_future, time_obs = pr_time_obs, time_cm_hist = pr_time_cm_hist, time_cm_future = pr_time_cm_future, parallel=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f65350",
   "metadata": {},
   "source": [
    "**CDFt**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e86f1448",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 225/225 [00:09<00:00, 24.53it/s]\n",
      "100%|█████████████████████████████████████████| 225/225 [00:30<00:00,  7.48it/s]\n"
     ]
    }
   ],
   "source": [
    "tas_debiaser_CDFT = CDFt.from_variable(variable = 'tas')\n",
    "tas_val_debiased_CDFT = tas_debiaser_CDFT.apply(tas_obs, tas_cm_hist, tas_cm_validate, time_obs = tas_time_obs, time_cm_hist = tas_time_cm_hist, time_cm_future = tas_time_cm_validate)\n",
    "tas_fut_debiased_CDFT = tas_debiaser_CDFT.apply(tas_obs, tas_cm_hist, tas_cm_future, time_obs = tas_time_obs, time_cm_hist = tas_time_cm_hist, time_cm_future = tas_time_cm_future)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5990122",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 225/225 [00:08<00:00, 25.06it/s]\n",
      "100%|█████████████████████████████████████████| 225/225 [00:29<00:00,  7.53it/s]\n"
     ]
    }
   ],
   "source": [
    "pr_debiaser_CDFT = CDFt.from_variable(variable = 'pr')\n",
    "pr_val_debiased_CDFT = pr_debiaser_CDFT.apply(pr_obs, pr_cm_hist, pr_cm_validate, time_obs = pr_time_obs, time_cm_hist = pr_time_cm_hist, time_cm_future = pr_time_cm_validate)\n",
    "pr_fut_debiased_CDFT = pr_debiaser_CDFT.apply(pr_obs, pr_cm_hist, pr_cm_future, time_obs = pr_time_obs, time_cm_hist = pr_time_cm_hist, time_cm_future = pr_time_cm_future)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "058c8ce2",
   "metadata": {},
   "source": [
    "**Quantile Delta Mapping**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0582c566",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/debias/_debiaser.py:560: UserWarning: The debiaser output contains values outside the reasonable physical range of [100, 400] for the variable: Daily mean near-surface air temperature. This might be due to values outside the range in the input, or to a problem of the debiaser for the given dataset at hand. It is recommended to check the output carefully.\n",
      "  self._check_output(output)\n"
     ]
    }
   ],
   "source": [
    "tas_debiaser_QDM = QuantileDeltaMapping.from_variable(variable = \"tas\")\n",
    "tas_val_debiased_QDM = tas_debiaser_QDM.apply(tas_obs, tas_cm_hist, tas_cm_validate, time_obs = tas_time_obs, time_cm_hist = tas_time_cm_hist, time_cm_future = tas_time_cm_validate, parallel=True)\n",
    "tas_fut_debiased_QDM = tas_debiaser_QDM.apply(tas_obs, tas_cm_hist, tas_cm_future, time_obs = tas_time_obs, time_cm_hist = tas_time_cm_hist, time_cm_future = tas_time_cm_future, parallel=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "df0f6f64",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/debias/_quantile_delta_mapping.py:322: RuntimeWarning: divide by zero encountered in divide\n",
      "  cm_future\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/debias/_debiaser.py:560: UserWarning: The debiaser output contains inf or nan values. This might be due to inf or nan values inside the input, or to a problem of the debiaser for the given dataset at hand. It is recommended to check the output carefully\n",
      "  self._check_output(output)\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/debias/_debiaser.py:560: UserWarning: The debiaser output contains values outside the reasonable physical range of [0, 0.01] for the variable: Daily mean precipitation. This might be due to values outside the range in the input, or to a problem of the debiaser for the given dataset at hand. It is recommended to check the output carefully.\n",
      "  self._check_output(output)\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/debias/_quantile_delta_mapping.py:322: RuntimeWarning: divide by zero encountered in divide\n",
      "  cm_future\n"
     ]
    }
   ],
   "source": [
    "pr_debiaser_QDM = QuantileDeltaMapping.from_variable(variable = \"pr\")\n",
    "pr_val_debiased_QDM = pr_debiaser_QDM.apply(pr_obs, pr_cm_hist, pr_cm_validate, time_obs = pr_time_obs, time_cm_hist = pr_time_cm_hist, time_cm_future = pr_time_cm_validate, parallel=True)\n",
    "pr_fut_debiased_QDM = pr_debiaser_QDM.apply(pr_obs, pr_cm_hist, pr_cm_future, time_obs = pr_time_obs, time_cm_hist = pr_time_cm_hist, time_cm_future = pr_time_cm_future, parallel=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d10b8506",
   "metadata": {},
   "source": [
    "**Quantile Mapping**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d5a8f1b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████| 225/225 [00:00<00:00, 1452.93it/s]\n",
      "100%|████████████████████████████████████████| 225/225 [00:00<00:00, 924.95it/s]\n",
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      "100%|████████████████████████████████████████| 225/225 [00:02<00:00, 103.39it/s]\n"
     ]
    }
   ],
   "source": [
    "tas_debiaser_QM = QuantileMapping.from_variable(variable = \"tas\")\n",
    "tas_val_debiased_QM = tas_debiaser_QM.apply(tas_obs, tas_cm_hist, tas_cm_validate)\n",
    "tas_fut_debiased_QM = tas_debiaser_QM.apply(tas_obs, tas_cm_hist, tas_cm_future)\n",
    "\n",
    "pr_debiaser_QM = QuantileMapping.from_variable(variable = \"pr\")\n",
    "pr_val_debiased_QM = pr_debiaser_QM.apply(pr_obs, pr_cm_hist, pr_cm_validate)\n",
    "pr_fut_debiased_QM = pr_debiaser_QM.apply(pr_obs, pr_cm_hist, pr_cm_future)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eca0ac18",
   "metadata": {},
   "source": [
    "## Part 2: Evaluation on validation dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2038d266",
   "metadata": {},
   "source": [
    "### Overview: Statistical Properties and Threshold Metrics\n",
    "\n",
    "There are two types of metrics that the evaluation module enables you to analyse: bias in the mean or statistical properties such as the 5th or 95th percentile, and bias in threshold metrics such as frost days or location-wise defined high mean daily temperature. The reason for including the second type of metrics is that it is often specific threshold metrics that are relevant for impact models (for example agricultural or hydrological models), and where the success of bias adjustment methods is particularly desirable. \n",
    "\n",
    "The following table gives an overview, which types of analysis are currently available in this release of the package.\n",
    "\n",
    "|                  | Statistical Properties | Threshold Metrics  |\n",
    "| ------------     | ---------------------- | -----------------  |\n",
    "| **Marginal**     | x - marginal bias      | x - marginal bias  |\n",
    "| **Temporal**     |                        | x - spell length   |\n",
    "| **Spatial**      | x - RMSE corr matrices | x - spatial extent |\n",
    "| **Spatioteporal**|                        | x - cluster size   |\n",
    "| **Multivariate** | x - correlation        |                    |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f40eedf3",
   "metadata": {},
   "source": [
    "A new threshold metric can be defined by the user by simply specifying at least the a) threshold value or values, b) the type of threshold (whether values above, below, between or outside the given threshold(s) are of interest), c) the variable it applies to and d) a metric name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6dc8e9fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_frost_days = ThresholdMetric(name=\"Mean frost days (tas<0°C)\", variable=\"tas\", threshold_value=273.13, threshold_type=\"lower\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa48b8b0",
   "metadata": {},
   "source": [
    "Thresholds defined per day, month or season are also possible using the threshold_scope argument, as well as per location (“threshold_locality”) or any combination of the two. Since precipitation is an accumulative variable, a child class with special features is defined for precipitation metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6025349a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_heatwave = ThresholdMetric.from_quantile(tas_obs, 0.95, threshold_type = \"higher\", \n",
    "                                                        threshold_scope = \"season\", \n",
    "                                                        time = tas_time_cm_validate, \n",
    "                                                        name = \"Mean daily temperature >95th \\n seasonal percentile of gridcell\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ceb981f",
   "metadata": {},
   "source": [
    "For each variable, the package implements a number of out-of-the-box metrics which users can edit, next to creating new ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f52febf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AccumulativeThresholdMetric(threshold_value=1.1574074074074073e-05, threshold_type='lower', threshold_scope='overall', threshold_locality='global', name='Dry days \\n (< 1 mm/day)', variable='pr')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dry_days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0be3e615",
   "metadata": {},
   "outputs": [],
   "source": [
    "dry_days.threshold_type =\"lower\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68cfa6e6",
   "metadata": {},
   "source": [
    "A number of threshold metrics are associated with each variable by default:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bc5e1c8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_metrics = [warm_days, cold_days, mean_frost_days]\n",
    "pr_metrics = [dry_days, wet_days, R10mm, R20mm]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "692c1803",
   "metadata": {},
   "source": [
    "Overall, all ETCCDI climate indices are fully integrated and compatible with the ibicus threshold metrics class (https://www.climdex.org/learn/indices/). "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7189eee",
   "metadata": {},
   "source": [
    "### Location-wise evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43cd6761",
   "metadata": {},
   "source": [
    "We first investigate the location-wise bias of the chosen metrics and descriptive statistics. The plot below shows the distribution of marginal (location-wise) biases across locations. For the mean, this is for example calculated as follows:\n",
    "\n",
    "$$ \\text{Bias}_{ij} = 100 * \\frac{\\bar{\\text{tas}}_{obs, ij} - \\bar{\\text{tas}}_{cm, ij}}{\\bar{\\text{tas}}_{obs, ij}} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23d4ed17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/utils/_utils.py:464: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for index, value in row.iteritems():\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2100x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_marginal_bias_data = marginal.calculate_marginal_bias(metrics = tas_metrics,\n",
    "                                                          statistics = ['mean', 0.05, 0.95],\n",
    "                                                          percentage_or_absolute = 'absolute',\n",
    "                                                          obs = tas_obs_validate,\n",
    "                                                          raw = tas_cm_validate, \n",
    "                                                          CDFT = tas_val_debiased_CDFT,\n",
    "                                                          ISIMIP = tas_val_debiased_ISIMIP,\n",
    "                                                          QM = tas_val_debiased_QM, QDM = tas_val_debiased_QDM)\n",
    "\n",
    "tas_marginal_bias_plot = marginal.plot_marginal_bias(variable = 'tas', \n",
    "                                                     bias_df = tas_marginal_bias_data,\n",
    "                                                    metrics_title = \"Absolute bias [days / year]\",\n",
    "                                                    statistics_title = \"Absolute bias [K]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cd7f1a8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/evaluate/marginal.py:94: RuntimeWarning: divide by zero encountered in divide\n",
      "  bias = 100 * (cm_metric - obs_metric) / obs_metric\n",
      "/var/folders/3g/80lqcgbn19l711ck1nnjxplr0000gn/T/ipykernel_21176/1936019458.py:1: UserWarning: raw: Division by zero encountered in bias of AccumulativeThresholdMetric(threshold_value=0.0002314814814814815, threshold_type='higher', threshold_scope='overall', threshold_locality='global', name='Extremely wet days \\n (> 20 mm/day)', variable='pr') calculation, not showing results for this debiaser.\n",
      "  pr_marginal_bias_data = marginal.calculate_marginal_bias(metrics = pr_metrics,\n",
      "/var/folders/3g/80lqcgbn19l711ck1nnjxplr0000gn/T/ipykernel_21176/1936019458.py:1: UserWarning: CDFT: Division by zero encountered in bias of AccumulativeThresholdMetric(threshold_value=0.0002314814814814815, threshold_type='higher', threshold_scope='overall', threshold_locality='global', name='Extremely wet days \\n (> 20 mm/day)', variable='pr') calculation, not showing results for this debiaser.\n",
      "  pr_marginal_bias_data = marginal.calculate_marginal_bias(metrics = pr_metrics,\n",
      "/var/folders/3g/80lqcgbn19l711ck1nnjxplr0000gn/T/ipykernel_21176/1936019458.py:1: UserWarning: ISIMIP: Division by zero encountered in bias of AccumulativeThresholdMetric(threshold_value=0.0002314814814814815, threshold_type='higher', threshold_scope='overall', threshold_locality='global', name='Extremely wet days \\n (> 20 mm/day)', variable='pr') calculation, not showing results for this debiaser.\n",
      "  pr_marginal_bias_data = marginal.calculate_marginal_bias(metrics = pr_metrics,\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/evaluate/marginal.py:51: RuntimeWarning: overflow encountered in divide\n",
      "  100\n",
      "/var/folders/3g/80lqcgbn19l711ck1nnjxplr0000gn/T/ipykernel_21176/1936019458.py:1: UserWarning: QDM: Division by zero encountered in bias of mean calculation, not showing results for this debiaser.\n",
      "  pr_marginal_bias_data = marginal.calculate_marginal_bias(metrics = pr_metrics,\n",
      "/var/folders/3g/80lqcgbn19l711ck1nnjxplr0000gn/T/ipykernel_21176/1936019458.py:1: UserWarning: QDM: Division by zero encountered in bias of AccumulativeThresholdMetric(threshold_value=0.0002314814814814815, threshold_type='higher', threshold_scope='overall', threshold_locality='global', name='Extremely wet days \\n (> 20 mm/day)', variable='pr') calculation, not showing results for this debiaser.\n",
      "  pr_marginal_bias_data = marginal.calculate_marginal_bias(metrics = pr_metrics,\n",
      "/var/folders/3g/80lqcgbn19l711ck1nnjxplr0000gn/T/ipykernel_21176/1936019458.py:1: UserWarning: QM: Division by zero encountered in bias of AccumulativeThresholdMetric(threshold_value=0.0002314814814814815, threshold_type='higher', threshold_scope='overall', threshold_locality='global', name='Extremely wet days \\n (> 20 mm/day)', variable='pr') calculation, not showing results for this debiaser.\n",
      "  pr_marginal_bias_data = marginal.calculate_marginal_bias(metrics = pr_metrics,\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/utils/_utils.py:464: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for index, value in row.iteritems():\n"
     ]
    },
    {
     "data": {
      "image/png": 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pY8eOWr16tRo0aKC5c+dmu94jR46oXbt2Dsv++lq6WZKiQoUK9td8zgMACkNW34dhYWEOr4ODg3X//ffby4V98sknslqt6tu3b5brvHTpkpKSkhy+38qUKZNpvceOHdOjjz6qevXqqWLFiqpbt64k2c99zGazBg4caN/uwYMH9d///tf+fdytWzeFhoaqXr16GjRokOLj4x3mGwJKqvDwcF29elWJiYnasWOHGjRooGrVqqlz585KTEzU1atXtXXrVtWuXVv16tXT/v37ZRiGGjRo4HAtYtu2bdmWFs2OM79lz507p5EjR6pBgwby9/eXv7+/rly54nBdY/jw4fbRGefOndP69evtlW+aNWume+65R02bNlXfvn317rvv6uLFi/n5UwFF5rbbblP79u3t31nHjh3Tjh077P+u9+3bp0WLFjkcgxEREUpLS9Px48ft6/nrd2Vu34VZ6dKli3bs2KG0tDRt27ZNXbp0UZcuXbRt2zadPXtW33//vX3k1r59+/Tjjz+qQoUK9riqVKkiq9Wap8+HY8eOKSUlxeHzoEqVKmrYsKFDuwMHDigyMlKhoaGqUKGCunTpIunP7/7cfnP07dtX165dU7169TRixAh99NFHOc4dioIjuQWUIsOGDdOiRYu0ePHiTCUJ08trrF+/XgcPHrQ/Dh8+bJ93a/Xq1Ro7dqyGDRumjRs36uDBgxo6dGimSSTLli3r8NpkMtnXDwD4U2BgoLy9vXX27FmH5efOnVP16tWz7efr66uFCxfqjz/+0IkTJ3Tq1Cl7winjjQsZeXl5qXXr1vrhhx+yXa/hZDkVPucBAEXhyJEj9qRSunLlymVqN3z4cK1cuVLXrl1TXFyc+vXrJz8/vwJt+4EHHtCFCxf07rvvau/evdq7d68kOZz7DB8+XJs2bdKZM2e0cOFC3XPPPQoNDZUkVahQQfv377fPHfLiiy+qWbNm+v333wsUF1Dc1a9fXyEhIdqyZYu2bNliv0gdFBSkunXraufOndqyZYvuvvtuSTevRaTPwZPxWsSRI0cyzfHtClFRUdq3b5/mzJmjXbt26eDBgwoICHA4tgcPHqz//e9/2r17t5YtW6Y6deqoY8eOkm7eSLZp0yZ9+umnaty4sebOnauGDRs6JACA4ig6OloffPCBkpOTFRcXp9DQUN1zzz2Sbh6Hf//73x2Owf/+97/64YcfHEr8ZvcdnN13YVY6deqky5cva//+/dqxY4e9bOm2bdu0ZcsWVatWTY0aNbLH1apVK4e4Dh48qO+//16PPvqo0/vuzHnu1atX1b17d5UvX17Lli1TYmKivWTxX7/7s/vNUatWLR09elRvvvmmfH19NWrUKHXq1Mlh6gC4FsktoBRJr0mbkpKiiIgIh/caN24ss9msU6dOqX79+g6PWrVqSZJ27Nih9u3ba9SoUWrRooXq16+f5zupAAB/8vHxUatWrbRp0yaH5Zs2bcq2VnhGZcuWVUhIiLy9vbVy5Ur17NlTXl5Z/7wzDEMHDx50mJz3rxo3bqw9e/Y4LPvrawAAisLmzZt16NAhPfTQQ7m2ve+++1SuXDm99dZb+vTTTzPdyJeRv7+/goODHb7fbty44TDvxoULF3TkyBFNmjRJ99xzjxo1apTlyIymTZsqLCxM7777rpYvX55pu2XKlFHXrl01c+ZMffPNNzpx4oQ2b97szO4DHi08PFxbt27V1q1b7SMfJKlz585KSEjQnj177PPxtGjRQqmpqTp37lymaxFBQUGSbv5mTk1NzXW7jRo1yvW37I4dO/Tkk0/qvvvu0+233y6z2azz5887tAkICFDv3r0VFxenuLg4DR061OF9k8mkDh06aMqUKTpw4IB8fHwyzdsHFDcPP/ywvL29tXz5ci1evFhDhw61j45u2bKlvvvuu0zHYP369eXj45PjenP7Lvyr9Hm33njjDZlMJjVu3FgdO3bUgQMH9Mknn9gT4ulx/fDDD6pWrVqmuPz9/SU59/lQv359lS1b1uHz4OLFi/r+++/tr//v//5P58+f18svv6yOHTvqtttuy7I6SW6/OXx9fdWrVy+9/vrr2rp1q3bv3q1Dhw7lGB/yr4y7AwBQdLy9vXXkyBH784wqVKigp59+WmPHjlVaWpruuusuJScna9euXSpfvryGDBmi+vXra8mSJUpISFDdunW1dOlSJSYmZrqbEgDgvHHjxmnQoEEKCwtTu3bt9M477+jUqVMaOXKkvc2ECRP0008/acmSJZKk77//Xl999ZXatGmjixcv6rXXXtO3336rxYsX2/tMmTJFbdu21a233qrk5GS9/vrrOnjwoN58881sY3nyySfVvn17zZw5U71799bGjRv12WefFd7OAwAgyWaz6ezZs0pNTdUvv/yizz77TDNmzFDPnj01ePDgXPt7e3srKipKEyZMUP369bMsqZvRU089pZdfflm33nqrGjVqpNdee81hRFXlypUVEBCgd955R8HBwTp16pSef/75LNc1fPhwjR49Wn5+fnrwwQftyz/55BP973//U6dOnVS5cmVt2LBBaWlpmUogASVReHi4Hn/8cV2/ft3hQnXnzp31j3/8Q1ar1Z7catCggQYMGKDBgwcrNjZWLVq00Pnz57V582Y1bdpU9913n+rUqaMrV67oiy++ULNmzeTn55fl6MynnnpKQ4YMUVhYmO666y7Fx8fru+++U7169ext6tevr6VLlyosLEzJycl65pln5Ovrm2ldw4cPV8+ePZWamqohQ4bYl+/du1dffPGFunfvrmrVqmnv3r369ddf7SNNgOKqfPny6tevn1544QVdunTJoXTgc889p7Zt2+rxxx/XiBEjVK5cOR05ckSbNm3Ksax9uuy+C7PTpUsX/fvf/9aDDz4ok8mkypUrq3Hjxlq1apVef/11e7sBAwbo1VdfVWRkpKZOnaqQkBCdOnVKH374oZ555hmFhISoTp06SkhI0NGjRxUQECB/f/9MlUbKly+v6OhoPfPMMwoICFD16tU1ceJEhxtDa9euLR8fH82dO1cjR47Ut99+q2nTpmWKPaffHIsWLVJqaqratGkjPz8/LV26VL6+vjmOZEPBMHILKGUqVqyoihUrZvnetGnT9OKLL2rGjBlq1KiRIiIitG7dOnvyauTIkerTp4/69eunNm3a6MKFCxo1alRRhg8AJU6/fv00Z84cTZ06Vc2bN9f27du1YcMGhx/ASUlJDvMApKamKjY2Vs2aNVO3bt1ktVq1a9cu1alTx97m999/12OPPaZGjRqpe/fu+umnn7R9+3bdeeed2cbStm1bvffee5o7d66aN2+ujRs3atKkSYWy3wAApPvss88UHBysOnXqqEePHtqyZYtef/11rVmzJtNNedmJjo5WSkpKrneMS9L48eM1ePBgRUVFqV27dqpQoYLDxTgvLy+tXLlS+/btU5MmTTR27Fi9+uqrWa7rkUceUZkyZfToo4/KYrHYl1eqVEkffvih7r77bjVq1Ejz58/XihUrdPvttzu1P4AnCw8P17Vr11S/fn2HUtudO3fW5cuXdcstt9grxEhSXFycBg8erPHjx6thw4bq1auX9u7da2/Tvn17jRw5Uv369VPVqlU1c+bMLLfbr18/vfjii3ruuefUqlUrnTx5Uv/4xz8c2ixcuFAXL15UixYtNGjQID355JOqVq1apnV17dpVwcHBioiIUI0aNezLK1asqO3bt+u+++5TgwYNNGnSJMXGxuree+8t0N8MKArR0dG6ePGiunbtqtq1a9uX33HHHdq2bZt++OEHdezYUS1atNDkyZNzrPqRUXbfhdkJDw9XampqppGdqampDglxPz8/bd++XbVr11afPn3UqFEjDRs2TNeuXbNf2xwxYoQaNmyosLAwVa1aVTt37sxym6+++qo6deqkXr16qWvXrrrrrrvUqlUr+/tVq1bVokWL9J///EeNGzfWyy+/rFmzZmW5rux+c1SqVEnvvvuuOnTooDvuuENffPGF1q1bp4CAgFz/Jsgfk+Hs5AoAAAAodRYtWqQxY8YwRwgAoFjbuXOnunTpojNnzuQ4b6WrnT59WnXq1FFiYqJatmxZZNsFULj++OMP1ahRQwsXLlSfPn3cHQ5QrJW270J3/eZAZpQlBAAAAAAAHslms+n06dOaPHmyHn744SK7yHT9+nUlJSXp+eefV9u2bUvFxTygNEhLS9PZs2cVGxsrf39/9erVy90hAcVWafsudNdvDmSPsoQAAAAAAMAjrVixQg0bNtSlS5eyLVVWGHbu3KnQ0FDt27dP8+fPL7LtAihcp06dUs2aNbV69WotXLhQZcowLgDITmn7LnTXbw5kj7KEAAAAAAAAAAAA8BiM3AIAAAAAAAAAAIDHILkFAAAAAABcYtCgQfrXv/7l7jCKHZPJpI8//thl6zt06JBCQkJ09epVl60TyAnHdtY4tuFJOI7z58SJEzKZTDp48KDL1vnJJ5+oRYsWSktLc9k6SyOSWwAAAAAAoMC++eYbrV+/Xk888YTL1vnhhx8qIiJCgYGBLr+w5EoxMTHq379/kW2vadOmuvPOOzV79uwi2yZKL45tjm14PncdxzabTU888YQCAwNVrlw59erVS2fOnHFZDK4SFRWl559/vsi217NnT5lMJi1fvrzItlkSkdwCAAAAAAAF9sYbb6hv376qUKGC032SkpJ048aNbN+/evWqOnTooJdfftkVIRaatWvXKjIyski3OXToUL311ltKTU0t0u2i9Mnq2D5z5owMw8j3Ojm2s8exjcLgruN4zJgx+uijj7Ry5Up9+eWXunLlinr27Fms/n2npaVp/fr1bjnW586dW6TbLGlIbgEAkEeuLj0BAADg6dLS0vSf//xHvXr1yrWt1WrVqlWrdN9996lWrVo5lt8aNGiQXnzxRXXt2tXpWKKiotS7d2/961//UvXq1VWpUiVNmTJFN27c0DPPPKMqVaooJCRECxcutPdJLzm0evVqdezYUb6+vmrdurW+//57JSYmKiwsTOXLl1ePHj3066+/Omzv9OnT+vbbb3XvvfdKkn744Qd16tRJFotFjRs31qZNmzLF+Nxzz6lBgwby8/NTvXr1NHnyZF2/ft0ei5eXl77++muHPnPnzlVoaKj9QmRERIQuXLigbdu2Of23AfIqu2N78uTJqlevnl566SX973//y/N6Obb/xLGNwuau4/jSpUtasGCBYmNj1bVrV7Vo0ULLli3ToUOH9Pnnn2e73i5duuiJJ57QmDFjVLlyZVWvXl3vvPOOrl69qqFDh6pChQq65ZZb9Omnn9r7bN26VSaTSQkJCWrRooV8fX11991369y5c/r000/VqFEjVaxYUY888oj++OMPh+3t3LlTXl5eatOmjSTpq6++UosWLWSxWBQWFqYDBw44tE9NTVV0dLTq1q0rX19fNWzYUP/+97/t72/fvl1ly5bV2bNnHfqNHz9enTp1sr/u1auXvvrqq3z97XETyS0AgEeKioqSyWTSyJEjM703atQomUwmRUVFObWu9B9Bv//+u1Ptk5KS7Cc4AAAAuFnu6Pfff1dYWFi2bXbv3q2RI0cqODhY48aN0+23366DBw/K39/f5fFs3rxZP//8s7Zv367XXntNMTEx6tmzpypXrqy9e/dq5MiRGjlypE6fPu3Q76WXXtKkSZO0f/9+lSlTRo888oieffZZ/fvf/9aOHTt07Ngxvfjiiw591q5dq06dOqlSpUpKS0tTnz595O3trT179mj+/Pl67rnnMsVXoUIFLVq0SIcPH9a///1vvfvuu/YyZHXq1FHXrl0VFxfn0CcuLs7+G1iSfHx81KxZM+3YscOVfzrAQXbH9uuvv67Jkydr27ZtuvXWW9WpUyctWLBAly9fLtR4OLaBvHPXcbxv3z5dv35d3bt3ty+rUaOGmjRpol27duXYd/HixQoMDNRXX32lJ554Qv/4xz/Ut29ftW/fXvv371dERIQGDRqUKVEVExOjN954Q7t27dLp06f18MMPa86cOVq+fLnWr1+vTZs2ZRottXbtWj3wwAPy8vLS1atX1bNnTzVs2FD79u1TTEyMnn76aYf2aWlpCgkJ0erVq3X48GG9+OKLeuGFF7R69WpJUqdOnVSvXj0tXbrU3ufGjRtatmyZhg4dal8WGhqqatWqcawXhAEAgAcaMmSIUatWLcPf39/4448/7MuvXbtmVKpUyahdu7YxZMgQp9a1ZcsWQ5Jx8eLFHNvZbLYCRAwAAFByffTRR4a3t7eRlpbmsPz06dPG9OnTjQYNGhh+fn7GgAEDjISEBCM1NTVP6z9+/LghyThw4ECubYcMGWKEhoY6bKNhw4ZGx44d7a9v3LhhlCtXzlixYoXD+t977z17mxUrVhiSjC+++MK+bMaMGUbDhg0dttetWzfj9ddfNwzDMBISEgxvb2/j9OnT9vc//fRTQ5Lx0UcfZRvzzJkzjVatWtlfr1q1yqhcubJhtVoNwzCMgwcPGiaTyTh+/LhDvwcffNCIiorK7U8C5Ft2x3ZGJ06cMKZNm+ZwnG/cuDHHPuk4tjm2UfjcdRzHx8cbPj4+mdp369bNeOyxx7JdX+fOnY277rrL/jr9uB40aJB9WVJSkiHJ2L17t2EYf17X+fzzz+1tZsyYYUgyjh07Zl/297//3YiIiHDYXoMGDYy1a9cahmEYb7/9tlGlShXj6tWr9vffeuutXD+nRo0aZTz00EP216+88orRqFEj++uPP/7YKF++vHHlyhWHfi1atDBiYmKyXS9yxsgtAIDHatmypWrXrq0PP/zQvuzDDz9UrVq11KJFC/sywzA0c+ZM1atXT76+vmrWrJnef/99STdLQ4SHh0uSKleu7DDiq0uXLho9erTGjRunwMBAdevWTVLmsoRnzpxR//79VaVKFZUrV05hYWHau3dvIe89AABA8XHt2jWZzWb7yIN0kyZN0sSJE9WkSROdPn1ay5YtU/fu3eXlVbiXI26//XaHbVSvXl1Nmza1v/b29lZAQIDOnTvn0O+OO+5w6CPJoV/16tUd+iQnJ2vbtm32Uk9HjhxR7dq1FRISYm/Trl27TPG9//77uuuuuxQUFKTy5ctr8uTJOnXqlP393r17q0yZMvroo48kSQsXLlR4eLjq1KnjsB5fX99Md60DrpTdsZ1RaGioJk2apKNHj2revHlas2aNunfvrkuXLrk8Ho5tIO+K23FsGEaOsUiOx2z6cf3XY1ZSrsd6eonQjMsy9jly5IjOnDljL6145MgRNWvWTH5+fvY2WR3r8+fPV1hYmKpWrary5cvr3XffdTjWo6Ki9OOPP2rPnj2Sbh7rDz/8sMqVK+ewHo71giG5BQDwaEOHDnUo67Bw4UINGzbMoc2kSZMUFxent956S999953Gjh2rgQMHatu2bapVq5Y++OADSdLRo0eVlJTkUCt58eLFKlOmjHbu3Km333470/avXLmizp076+eff9batWv13//+V88++6zS0tIKaY8BAACKn8DAQP3xxx9KSUlxWD5p0iQ9//zz2rt3rxo0aKDRo0cXyU1AZcuWdXhtMpmyXPbX32wZ26RfePvrsox90ufxCA0NlST7nDl/3U5Ge/bsUf/+/XXvvffqk08+0YEDBzRx4kSHv52Pj48GDRqkuLg4paSkaPny5Zl+40rSb7/9pqpVq2b9RwBcILtjO6Pz58/rjTfe0J133qkRI0bo7rvv1gcffFAoJUc5toG8c9dxHBQUpJSUFF28eNFh+blz5+zJqezkdqynH3+5Heu5fT6sXbtW3bp1k6+vr6Ssj/W/Wr16tcaOHathw4Zp48aNOnjwoIYOHerw961WrZoeeOABxcXF6dy5c9qwYQPHeiEo4+4AAAAoiEGDBmnChAn2iYJ37typlStXauvWrZKkq1ev6rXXXtPmzZvtd9vUq1dPX375pd5++2117txZVapUkXTzx0elSpUc1l+/fn3NnDkz2+0vX75cv/76qxITE+3rqV+/vut3FAAAoBhr3ry5JOnw4cP259LN30UzZszQ9OnT9fnnn2vx4sUKDw9XSEiIBg0apIEDB6pu3bruCdoF1qxZYx/ZIUmNGzfWqVOn9PPPP6tGjRqSbs41ltHOnTsVGhqqiRMn2pedPHky07qHDx+uJk2aaN68ebp+/br69OmTqc23336rv/3tb67aHSCT7I5tm82mdevWacmSJfrss890++23a8iQIVq/fn2JuFDLsY2SxF3HcatWrVS2bFlt2rRJDz/8sKSbc5h/++23OV5nKUpr1qzR8OHD7a8bN26spUuX6tq1a/aEV/roq3Q7duxQ+/btNWrUKPuyY8eOZVr38OHD1b9/f4WEhOiWW25Rhw4dHN63Wq06duyYQ+Uh5A3JLQCARwsMDNT999+vxYsXyzAM3X///QoMDLS/f/jwYVmtVntJwXQpKSlO/YDIaVJ0STp48KBatGhhT2wBAACURlWrVlXLli315ZdfOlw4S+fl5aXu3bure/fuSk5O1urVq7V48WLFxMTo4sWLqlixYpbr/e233+wXlKWbI+2lm3eDBwUFFdr+OOPGjRv69NNP9fnnn9uXde3aVQ0bNtTgwYMVGxur5ORkhwvd0s2E36lTp7Ry5Uq1bt1a69evt5coy6hRo0Zq27atnnvuOQ0bNsx+kS3diRMn9NNPP9lLKQGFIbtje9SoUVq/fr0effRRff311w6lwJzBsc2xjaLjruPY399f0dHRGj9+vAICAlSlShU9/fTTatq0abH4933u3DklJiY6TDvx6KOPauLEiYqOjtakSZN04sQJzZo1y6Ff/fr1tWTJEiUkJKhu3bpaunSpEhMTM92sExERIX9/f/3zn//U1KlTM21/z549MpvNWZY9hHMoSwgA8HjDhg3TokWLtHjx4kzDvNOHm69fv14HDx60Pw4fPmyfdysnf62H/Fd/PREBAAAorR577DHFx8fn2q5ixYoaPny4duzYoe+//z7H31Nr165VixYtdP/990uS+vfvrxYtWmj+/Pkuizu/tm3bpvLly6tVq1b2ZV5eXvroo49ks9l05513avjw4Zo+fbpDv8jISI0dO1ajR49W8+bNtWvXLk2ePDnLbURHRyslJSXLUkYrVqxQ9+7d7WXTgMKS1bE9YcIEnTlzRq+99lqeL4hLHNsc2yhq7jqOZ8+erd69e+vhhx9Whw4d5Ofnp3Xr1snb27tgO+QC69atU5s2bVStWjX7svLly2vdunU6fPiwWrRooYkTJ+qVV15x6Ddy5Ej16dNH/fr1U5s2bXThwgWHUVzpvLy8FBUVpdTUVA0ePDjT+ytWrNCAAQMc5vdC3pgMZwpJAgBQzERFRen333/Xxx9/rNTUVNWuXVuSdOrUKXl7e6t3796qVKmS5s6dq6pVq+rdd9/VoEGDslzXrl271KFDB50/f14BAQH25V26dFHz5s01Z84ch/Ymk0kfffSRevfurcWLF+vJJ5/U8ePHGb0FAABKNavVqoYNG2rlypWl4i7kJ598Ujdu3NC8efMKbRvTp0/XypUrdejQIYflNptNt956q1asWJGpzBHgahzbrsexjaJW2o5jZ/Tq1Ut33XWXnn322ULbxogRI/TLL79o7dq1Dst//fVX3Xbbbfr66689ujyzu1GWEADg8by9vXXkyBH784wqVKigp59+WmPHjlVaWpruuusuJScna9euXSpfvryGDBmi0NBQmUwmffLJJ7rvvvvk6+ur8uXLO7XtRx55RP/617/Uu3dvzZgxQ8HBwTpw4IBq1KjBD0YAAFCqWCwWLVmyROfPn3d3KEWiSZMmhfZ778qVKzpy5Ijmzp2radOmZXr/5MmTmjhxIhe/USQ4tl2HYxvuUtqOY2fcddddeuSRRwpl3ZcuXVJiYqLi4+O1Zs2aTO8fP35c8+bNI7FVQIzcAgB4pIwjt7KSPnJr0aJFMgxDc+fO1bx58/S///1PlSpVUsuWLfXCCy+oU6dOkqRp06Zp3rx5+uW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      "text/plain": [
       "<Figure size 2100x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr_marginal_bias_data = marginal.calculate_marginal_bias(metrics = pr_metrics, \n",
    "                                                         statistics = ['mean', 0.05, 0.95],\n",
    "                                                        percentage_or_absolute = 'percentage',\n",
    "                                                         obs = pr_obs_validate,\n",
    "                                                         raw = pr_cm_validate, \n",
    "                                                         CDFT = pr_val_debiased_CDFT,\n",
    "                                                         ISIMIP = pr_val_debiased_ISIMIP,\n",
    "                                                         QDM = pr_val_debiased_QDM,\n",
    "                                                         QM = pr_val_debiased_QM)\n",
    "\n",
    "pr_marginal_bias_plot = marginal.plot_marginal_bias(variable = 'pr', \n",
    "                                                    bias_df = pr_marginal_bias_data,\n",
    "                                                   remove_outliers = True,\n",
    "                                                   outlier_threshold_statistics = 10,\n",
    "                                                   metrics_title = 'Absolute bias [days / year]',\n",
    "                                                   statistics_title = 'Absolute bias [mm]')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e01e96f4",
   "metadata": {},
   "source": [
    "**Spatial distribution of marginal biases**: The boxplots above plot the bias across locations. To investigate this further, we can plot the bias spatially. The function shown below works with input of the type produced by the output of the 'calculate_marginal_bias' function, and produces simple heatmaps that have no further package dependencies other than numpy. The user can build on this to produce more sophisticated plots using packages such as xarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e393562f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/utils/_utils.py:464: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for index, value in row.iteritems():\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3000x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_bias_map_mean = marginal.plot_bias_spatial(variable = 'tas', metric = '0.05 qn', bias_df = tas_marginal_bias_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "881038c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x500 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pr_bias_map_mean = marginal.plot_bias_spatial(variable = 'pr', metric = 'Mean', bias_df = pr_marginal_bias_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12451358",
   "metadata": {},
   "source": [
    "### Spatial, temporal and spatiotemporal properties \n",
    "\n",
    "For each threshold metric, the temporal spell length, the spatial extent and the 'spatiotemporal cluster size' can be calculated as follows:\n",
    "\n",
    "- Spell length: counts the number of temporally consecutive threshold exceedances at each location. \n",
    "- Spatial extent: percentage of the area is beyond the threshold, given that one location is above the threshold.\n",
    "- Spatiotemporal clusters: spatiotemporally connected sets of threshold exceedance across the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "73609582",
   "metadata": {},
   "outputs": [],
   "source": [
    "spelllength_dry = dry_days.calculate_spell_length(minimum_length= 3,\n",
    "                              obs = pr_obs_validate,\n",
    "                              raw = pr_cm_validate, CDFT = pr_val_debiased_CDFT,\n",
    "                              ISIMIP = pr_val_debiased_ISIMIP,\n",
    "                              QM = pr_val_debiased_QM,\n",
    "                              QDM = pr_val_debiased_QDM\n",
    "    )\n",
    "\n",
    "spatiotemporal_dry = dry_days.calculate_spatiotemporal_clusters(\n",
    "                              obs = pr_obs_validate,\n",
    "                              raw = pr_cm_validate, CDFT = pr_val_debiased_CDFT,\n",
    "                              ISIMIP = pr_val_debiased_ISIMIP,\n",
    "                              QM = pr_val_debiased_QM,\n",
    "                              QDM = pr_val_debiased_QDM\n",
    "    )\n",
    "\n",
    "spatial_dry = dry_days.calculate_spatial_extent(\n",
    "                              obs = pr_obs_validate,\n",
    "                              raw = pr_cm_validate, CDFT = pr_val_debiased_CDFT,\n",
    "                              ISIMIP = pr_val_debiased_ISIMIP,\n",
    "                              QM = pr_val_debiased_QM,\n",
    "                              QDM = pr_val_debiased_QDM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d429a2a5",
   "metadata": {},
   "source": [
    "A useful way of visualising these metrics is to plot their empirical cumulative distribution functions as visual identification of differences in the distributions are possible more easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2d6bea3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spatiotemporal_fig = marginal.plot_spatiotemporal(data = [spelllength_dry, spatiotemporal_dry, spatial_dry])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f9e2cf9",
   "metadata": {},
   "source": [
    "We see that bias adjustment can significantly modify the spatiotemporal extent of threshold metrics. Depending on the application case and context, individual spatiotemporal extent metrics can and should be investigated in more detail.\n",
    "\n",
    "So far, the spatiotemporal evaluation has focused on threshold metrics. To evaluate the spatial correlation for a single variable, a function to calculate the RMSE between the observed and the model correlation matrix for a single location is currently implemented in ibicus. At each location, the RMSE between the correlation matrix of this location with all others in the observational data, and the correlation matrix in the climate model data is calculated and output in the following map as a single number at each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b7c0cbab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/3g/80lqcgbn19l711ck1nnjxplr0000gn/T/ipykernel_21176/777485544.py:9: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  tas_rmsd_spatial_plot.show()\n"
     ]
    },
    {
     "data": {
      "image/png": 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QnZ2NWrVqqe336PW5u7vj1KlTKtsVr0uNGjWwYMECLFiwAP/884+ytbdfv374448/ADz8p5yTk6N2jtISA23maN6yZQs6d+6MNWvWqJQ/PpDHyckJx44dQ0lJSalJb926dSFJEo4ePar8cPEoTWWPx6LNPdbkaeen1uY9oSuZmZkayxQf2BQxzZ49G4MGDdJ4jMd/zzXdB13c36d9L1bkWjTtX9bfSZlMhm+//RYDBw7Ea6+9hpKSklJndPj333+VxyTSFSa8VC2lp6dj5syZcHBwwCuvvALg4T8BIYRaArB+/XqVAVXaGj9+PObPn4+XX34ZFy9exNKlS58q9vKwtbVFly5dkJycjBYtWihbgDWRJAlWVlYq/wQzMzM1ztJQGTp06IBatWrhwoULeO2118qsK0mS2uv1/fff48aNG2jUqFGZ+1pZWSEgIOCJ8bi4uGDs2LE4d+4cYmJi8ODBA9ja2sLb2xs7duxAQUGBMobbt28jMTHxqaer03Rdv/76K5KSkuDh4aEs69WrF7Zu3YqNGzeW2q2hb9++WLJkCW7cuIGhQ4fqJJby3mNtPd7ar6DNe0JXtm7disjISOXvxdWrV5GYmIjRo0cDeJgANm7cGOfOnVNLVrVR3vtb2r0B8NTvxae9Fi8vL9jY2ODvv/8us55MJsM333yDgQMHYurUqSgpKcG0adNU6ty8eRP5+flo1qyZ1nEQlYUJL5m88+fPK/uj3bp1C0ePHkV8fDzMzc3x9ddfK+cNtbe3R6dOnbB8+XLUrVsX3t7eSEhIwIYNG8rd0qJJrVq1MHr0aKxZswZeXl7o16+fjq6sbB9++CE6duyI4OBgvPrqq/D29sa9e/fw119/4bvvvsOhQ4cAPEyIdu3ahcmTJ2PIkCG4du0aFi1aBDc3N1y6dKlSYn2UnZ0dPvroI4wZMwZ37tzBkCFD4OzsjKysLJw7dw5ZWVnKls++ffti48aNaNq0KVq0aIEzZ85g+fLlqF+//lPF0K5dO/Tt2xctWrSAo6MjUlNTsXnzZgQGBsLW1hbAw+mTPvnkE4wcORITJkzA7du3sWzZMp3Mzdy3b18sWrQI8+bNQ0hICC5evIiFCxfCx8dHZe7gl156CfHx8Zg0aRIuXryILl26oKSkBCdOnICvry+GDx+ODh06YOLEiQgLC8Pp06fRqVMn1KhRAxkZGTh27BiaN2+OV199tcxY9HGPNfHz8wMAfPrpp6hZsyZkMhl8fHxQp06dcr8ndOXWrVsYOHAgJkyYgJycHMybNw8ymQyzZ89W1vnkk0/Qq1cvhIaGYuzYsahXrx7u3LmD1NRUnD17Fjt27Hjiecp7fxs2bAgbGxt8/vnn8PX1hZ2dHdzd3eHu7q6T9+LTXIuVlRUCAwNVxgWUxtraGl9//TUGDx6M6dOno6SkBBEREcrtimN06dKl3LETlYtBh8wR6ZFiZLTiYWVlJZydnUVISIiIjo4Wt27dUtvn+vXrYvDgwcLR0VHUrFlTPP/88+L8+fPCy8tLjBkzRlmvvLM0KBw5cqTUUcylCQkJEc8++6xauZeXl+jTp49aOTSM9k9LSxPh4eGiXr16wtLSUjg5OYmgoCDx7rvvqtRbsmSJ8Pb2FtbW1sLX11esW7dO4/VoOocipkfvjyaKe7Zjxw61GKFh9HlCQoLo06ePqF27trC0tBT16tUTffr0Udn/33//FePGjRPOzs7C1tZWdOzYURw9elRt1Hpp5y7NrFmzREBAgHB0dBTW1taiQYMGIiIiQmRnZ6vU++yzz4Svr6+QyWSiWbNmYtu2baWOjF++fLnGc2m6pwUFBWLmzJmiXr16QiaTiTZt2ohvvvlG7dhCCJGXlyfmzp0rGjduLKysrESdOnVE165dRWJiokq9uLg40a5dO1GjRg1hY2MjGjZsKEaPHq0yC4gm5b3HZc3SkJWVVeY5HhUTEyN8fHyEubm52vHK854o7ZxjxowRNWrUUDvf479nivfK5s2bxdSpU4WTk5OwtrYWwcHBGu/VuXPnxNChQ4Wzs7OwtLQUrq6uomvXrmLt2rXKOmXNGFPe+yuEEFu3bhVNmzYVlpaWAoCYN2+ecpsu3ovluZbSbNiwQZibm4ubN2+qlJd23wsKCkS/fv0EAPH+++8ry0eNGiWaN2/+xPMRaUsSogos9UJk4mbMmIE1a9bg2rVrGgd9EVHVcOTIEXTp0gU7duwoc5AcqcrPz4enpydmzJiBqKioCh0jNzcX7u7uWLlyJSZMmKDjCKm647RkRHp0/PhxbNq0CatXr8bEiROZ7BKRSZLJZFiwYAFWrFihNkVcea1cuRKenp4ICwvTcXRE7MNLpFeKPp99+/bFu+++a+hwiIj0ZuLEibh79y4uX76sMt1jednb22Pjxo2wsGBqQrrHLg1EREREZNLYpYGIiIiITBoTXiIDuHLlCiRJUj7MzMzg6OiIbt26Yf/+/Wr158+fr6x3+fJlte33799Xrgo3duxYlW3Xrl3D5MmT8cwzz8DGxga1a9dG8+bNMWHCBFy7dk3tHKU9rly5orPrf/vtt+Hp6QkLC4unmvJNX6Kjo/HNN9+olR85cgSSJD1xOV5Nnmbfqmrs2LHw9vau0L5ffPEFYmJiNG6TJAnz58+vcFzGoLT3WFk2btyo899FouqCHWWIDOj111/HiBEjIJfL8ccff2DBggXo3bs3Dh06hE6dOqnVt7OzQ3x8PBYtWqRSvmPHDhQVFcHS0lKl/Pr162jTpg1q1aqFGTNmoEmTJsjJycGFCxewfft2XL58WWUhAwDYt28fHBwc1M79tMvDKnz77bd477338NZbb6FXr15PXOnLEKKjozFkyBAMGDBApbxNmzZISkripPg68MUXX+D8+fOYPn262rakpCS9zPNblZT2HitLnz59kJSUpLPfRaLqhAkvkQF5enoql0rt0KEDGjdujJCQEGzYsEFjwjts2DB89tlnWLBggcpSshs2bMDAgQOxe/dulfrr1q1DdnY2Tp48qVxKGAAGDBiAOXPmoKSkRO0c/v7+el3W8/z58wCAqVOnwtnZWW/n0Qd7e/tSl7Y1FnK5HMXFxRo/aChWkTM0Y7/HupaXlweZTAYnJyflQjlEpB12aSCqQhRL3f7zzz8at4eHh+PatWs4cOCAsuzPP//EsWPHNC4te/v2bZiZmZWaWD6aND+tkpISLFu2DE2bNoW1tTWcnZ0xevRoXL9+XVnH29sbb7/9NoCHS/Y+6avry5cvY/jw4XB3d4e1tTVcXFzQrVs3pKSkqByzb9+++Prrr9GiRQvIZDI0aNAAq1atUjlWfn4+ZsyYgVatWsHBwQG1a9dGYGCg2hLKkiTh/v37+Oyzz5TdOTp37gxAc7eE06dPY/jw4fD29oaNjQ28vb3x0ksv4erVqxW7kQBu3LiBiRMnwsPDA1ZWVnB3d8eQIUNU3hfp6ekYOXIknJ2dYW1tDV9fX3zwwQcqH2IUXWeWLVuGd999Fz4+PrC2tsbhw4eVXVjOnj2LIUOGwNHREQ0bNgQACCGwevVqtGrVCjY2NnB0dMSQIUM0dqd5XGxsLDp16gRnZ2fUqFEDzZs3x7Jly1BUVKSs07lzZ3z//fe4evWqSrcZBU3vi/Pnz+OFF16Ao6MjZDIZWrVqhc8++0yljuL12bp1K9566y24u7vD3t4e3bt3x8WLF58Yu+Ke/Prrr3jxxReV75PIyEgUFxfj4sWLeP7551GzZk14e3tj2bJlKvvr4j2m6Lawf/9+hIeHw8nJCba2tigoKCi1S8O+ffvQrVs3ODg4wNbWFr6+vli8eLFKndOnT6N///6oXbs2ZDIZWrduje3bt6vUefDgAWbOnAkfHx/IZDLUrl0bAQEB2Lp16xPvHVFVxxZeoiokLS0NAPDMM89o3N64cWMEBwcjLi4OoaGhAIC4uDh4e3ujW7duavUDAwMRGxuLQYMGITIyEoGBgU9cblTRAvgoSZJgbm5e5n6vvvoqPv30U7z22mvo27cvrly5gnfeeQdHjhzB2bNnUbduXXz99deIjY3Fhg0blF0nyvrqunfv3pDL5Vi2bBk8PT2RnZ2NxMRE3L17V6VeSkoKpk+fjvnz58PV1RWff/45pk2bhsLCQsycORMAUFBQgDt37mDmzJmoV68eCgsL8dNPP2HQoEGIj4/H6NGjATz8Or1r167o0qUL3nnnHQAo855duXIFTZo0wfDhw1G7dm1kZGRgzZo1eO6553DhwgWtW8tv3LiB5557DkVFRZgzZw5atGiB27dv48cff8S///4LFxcXZGVlISgoCIWFhVi0aBG8vb2xZ88ezJw5E3///TdWr16tcsxVq1bhmWeewfvvvw97e3s0btxYuYTroEGDMHz4cEyaNEk5f+orr7yCjRs3YurUqVi6dCnu3LmDhQsXIigoCOfOnYOLi0up8f/9998YMWIEfHx8YGVlhXPnzuG9997DH3/8gbi4OABQzkv9999/4+uvv37iPbl48SKCgoLg7OyMVatWoU6dOtiyZQvGjh2Lf/75B2+++aZK/Tlz5qBDhw5Yv349cnNzERUVhX79+iE1NfWJ72MAGDp0KEaOHIlXXnkFBw4cUCbsP/30EyZPnoyZM2fiiy++QFRUFBo1aoRBgwYB0O17LDw8HH369MHmzZtx//59te5KChs2bMCECRMQEhKCtWvXwtnZGX/++afymxQAOHz4MJ5//nm0a9cOa9euhYODA7788ksMGzYMDx48UPb7j4yMxObNm/Huu++idevWuH//Ps6fP4/bt28/8Z4RVXkGXeeNqJpSLPG5dOlSUVRUJPLz80VKSooIDAwUbm5uIi0tTaX+o8ulxsfHC2tra3H79m1RXFws3NzcxPz584UQQtSoUUNlid+SkhLxyiuvCDMzMwFASJIkfH19RURERKnn0PRo2LBhmdeTmpoqAIjJkyerlJ84cUIAEHPmzNF4LWXJzs4WAERMTEyZ9by8vIQkSSIlJUWlvEePHsLe3l7cv39f437FxcWiqKhIjBs3TrRu3Vpl2+P3UUHTktKajvvff/+JGjVqiA8//FCrfYUQIjw8XFhaWooLFy6UWmfWrFkCgDhx4oRK+auvviokSRIXL14UQvz/+6xhw4aisLBQpa7idZg7d65KeVJSkgAgPvjgA5Xya9euCRsbG/Hmm28qyzQtdfwouVwuioqKxKZNm4S5ubm4c+eOclufPn1K3RePLZs7fPhwYW1tLdLT01Xq9erVS9ja2oq7d+8KIf7/Hvfu3Vul3vbt2wUAkZSUVGqsQvz/PXn82lu1aiUAiF27dinLioqKhJOTkxg0aFCpx6vIe0yxDPHo0aNL3ab43b13756wt7cXHTt2FCUlJaXG0bRpU9G6dWtRVFSkUt63b1/h5uYm5HK5EEIIPz8/MWDAgFKPQ2TM2KWByICioqJgaWmp/Ir2/Pnz+O6778oc+f7iiy/CysoKn3/+Ofbu3YvMzEy1mRkUJEnC2rVrcfnyZaxevRphYWEoKirCypUr8eyzzyIhIUFtn59++gmnTp1SeTxpNPnhw4cBQC2Otm3bwtfXFwcPHixzf01q166Nhg0bYvny5VixYgWSk5M19jkGgGeffRYtW7ZUKRsxYgRyc3Nx9uxZZdmOHTvQoUMH2NnZwcLCApaWltiwYQNSU1O1jk/hv//+U7b0WVhYwMLCAnZ2drh//36FjvvDDz+gS5cu8PX1LbXOoUOH0KxZM7Rt21alfOzYsRBC4NChQyrl/fv3L7WFcPDgwSrP9+zZA0mSMHLkSBQXFysfrq6uaNmy5RNnmUhOTkb//v1Rp04dmJubw9LSEqNHj4ZcLseff/5Z5r6lOXToELp166Y2wHLs2LF48OABkpKSVMr79++v8rxFixYAUO5uJn379lV57uvrC0mS0KtXL2WZhYUFGjVqpHZMXb3HHn9dNElMTERubi4mT56s0iXkUX/99Rf++OMPvPzyywCg8pr27t0bGRkZyu4ebdu2xQ8//IBZs2bhyJEjyMvL0ypmoqqMCS+RAU2bNg2nTp3CsWPH8P7776OoqAgvvPBCmV8h1qhRA8OGDUNcXBw2bNiA7t27w8vLq8zzeHl54dVXX8WGDRtw6dIlbNu2Dfn5+XjjjTfU6rZs2RIBAQEqDz8/vzKPr4hX0+hxd3f3Cn0lKkkSDh48iNDQUCxbtgxt2rSBk5MTpk6dinv37qnUdXV1VdtfUaY4965duzB06FDUq1cPW7ZsQVJSEk6dOoXw8HDk5+drHZ/CiBEj8PHHH2P8+PH48ccfcfLkSZw6dQpOTk4VShiysrKeOEPB7du3S73Xiu2PKmtU/+Pb/vnnHwgh4OLiAktLS5XH8ePHkZ2dXeqx0tPTERwcjBs3buDDDz/E0aNHcerUKcTGxgJAhRMoba/38SW8FQP0ynv+2rVrqzy3srKCra0tZDKZWvmj7x1dvsfKMxNDVlYWAJT5flH0+545c6ba6zl58mQAUL6mq1atQlRUFL755ht06dIFtWvXxoABA3Dp0iWtYieqitiHl8iA6tevrxyo1qFDB7i6umLkyJGYN28ePv7441L3Cw8Px/r16/Hrr7/i888/1/q8Q4cOxeLFi1X6+T0NRYKRkZGh9s/35s2bFZ71wcvLCxs2bADwcHDe9u3bMX/+fBQWFmLt2rXKepmZmWr7KsoUsW3ZsgU+Pj7Ytm2bSmtYQUFBhWIDgJycHOzZswfz5s3DrFmzVI55586dCh3TyclJZaCfJnXq1EFGRoZa+c2bNwFA7X6X1vqnaVvdunUhSRKOHj2qcSaHsqaR++abb3D//n3s2rVL5UPYo4MMK0Lb6zUUXb7HynrNFBQzNpT1flHcm9mzZyv7Gj+uSZMmAB5+mF6wYAEWLFiAf/75R9na269fP/zxxx/aXgJRlcIWXqIq5OWXX0bnzp2xbt26Mr9+DQwMRHh4OAYOHIiBAweWWk9TkgA8/Br+2rVryhayp9W1a1cAD//hP+rUqVNITU3VOKBOW8888wzefvttNG/eXKWbAgD8/vvvOHfunErZF198gZo1a6JNmzYAHiYQVlZWKolEZmam2gh64GFSV57WQEmSIIRQSwLXr18PuVxe7mt7VK9evXD48OEyZxXo1q0bLly4oHYfNm3aBEmS0KVLlwqdG3j4db4QAjdu3FBr6Q8ICEDz5s1L3Vdxbx+9H0IIrFu3Tq1uee8x8PB6Dx06pExwFTZt2gRbW9sqM42ZPt5jZQkKCoKDgwPWrl0LIYTGOk2aNEHjxo1x7tw5ja9nQEAAatasqbafi4sLxo4di5deegkXL17EgwcPnipWIkNjCy9RFbN06VK0a9cOixYtwvr160utp2j5LMt7772HX375BcOGDVNOMZWWloaPP/4Yt2/fxvLly9X2OXPmjMaFJ5o1a1bqbAVNmjTBxIkT8dFHH8HMzAy9evVSztLg4eGBiIiIJ8b6uF9//RWvvfYaXnzxRTRu3BhWVlY4dOgQfv31V5XWVODhV9v9+/fH/Pnz4ebmhi1btuDAgQNYunSpcl7Zvn37YteuXZg8eTKGDBmCa9euYdGiRXBzc1P7yrZ58+Y4cuQIvvvuO7i5uaFmzZrKVrBH2dvbo1OnTli+fDnq1q0Lb29vJCQkYMOGDRVeQW7hwoX44Ycf0KlTJ8yZMwfNmzfH3bt3sW/fPkRGRqJp06aIiIjApk2b0KdPHyxcuBBeXl74/vvvsXr1arz66qulzvJRHh06dMDEiRMRFhaG06dPo1OnTqhRowYyMjJw7NgxNG/eHK+++qrGfXv06AErKyu89NJLePPNN5Gfn481a9bg33//VavbvHlz7Nq1C2vWrIG/vz/MzMyU33Y8bt68edizZw+6dOmCuXPnonbt2vj888/x/fffY9myZRrfr4agj/dYWezs7PDBBx9g/Pjx6N69OyZMmAAXFxf89ddfOHfunPJbok8++QS9evVCaGgoxo4di3r16uHOnTtITU3F2bNnsWPHDgBAu3bt0LdvX7Ro0QKOjo5ITU3F5s2bERgYWCXmZyZ6KoYcMUdUXSlGzy9fvlzj9hdffFFYWFiIv/76SwhR/pkNHh/5ffz4cTFlyhTRsmVLUbt2bWFubi6cnJzE888/L/bu3auyb1mzNAAQBw4cKPPccrlcLF26VDzzzDPC0tJS1K1bV4wcOVJcu3ZN43medC3//POPGDt2rGjatKmoUaOGsLOzEy1atBArV64UxcXFynpeXl6iT58+4quvvhLPPvussLKyEt7e3mLFihVqx1yyZInw9vYW1tbWwtfXV6xbt04Zz6NSUlJEhw4dhK2trQAgQkJChBCaZ1q4fv26GDx4sHB0dBQ1a9YUzz//vDh//rzw8vJSeS3KO0uDEA9nRAgPDxeurq7C0tJSuLu7i6FDh4p//vlHWefq1atixIgRok6dOsLS0lI0adJELF++XDniXoiy32dPeh3i4uJEu3btRI0aNYSNjY1o2LChGD16tDh9+rSyjqZZGr777jvRsmVLIZPJRL169cQbb7whfvjhB7Vrv3PnjhgyZIioVauWkCRJ5TXAY7M0CCHEb7/9Jvr16yccHByElZWVaNmypYiPj1epo7jHO3bsUClX3IfH65f3nowZM0bUqFFDrX5ISIh49tlnVcqe9j2mmInh1KlTaud7fJYGhb1794qQkBBRo0YNYWtrK5o1ayaWLl2qUufcuXNi6NChwtnZWVhaWgpXV1fRtWtXsXbtWmWdWbNmiYCAAOHo6Cisra1FgwYNREREhMjOzi7zvhEZA0mIUr4HISIyAt7e3vDz88OePXsMHQoREVVR7MNLRERERCaNCS8RERERmTR2aSAiIiIik8YWXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikWRg6gKqopKQEN2/eRM2aNSFJkqHDISIiIqLHCCFw7949uLu7w8ys7DZcJrwa3Lx5Ex4eHoYOg4iIiIie4Nq1a6hfv36ZdZjwalCzZk0AD2+gvb29gaMhIiIiosfl5ubCw8NDmbeVhQmvBopuDPb29kx4iYiIiKqw8nQ/5aA1IiIiIjJpTHiJiIiIyKQx4SUiIiIik8aEl4iIiIhMGhNeIiIiIjJpTHiJiIiIyKQx4SUiIiIik8aEl4iIiIhMGhNeIiIiIjJpTHiJiIiIyKQx4SUiIiIik8aEl4iIiIhMGhNeIiIiIjJpTHiJiIiIjFhCQgJ69+6NhIQEQ4dSZTHhJSIiIjJSeXl5iI6ORkZGBqKjo5GXl2fokKokJrxERERERiouLg5ZWVkAgKysLMTHxxs4oqqJCS8RERGREUpPT0d8fDyEEAAAIQTi4+ORnp5u4MiqHia8REREREZGCIElS5aUWq5IgukhJrxERERERiYtLQ1JSUmQy+Uq5XK5HElJSUhLSzNQZFUTE14iIiIiI+Pj44PAwEBIkqRSLkkSgoKC4OPjY6DIqiYmvERERERGRpIkzJo1S63rghACs2bNUkuEqzsmvERERERGKCMjQ2P5zZs3KzmSqo8JLxEREZGRKSkpQVRUlMZtUVFRKCkpqeSIqjYmvERERERG5tixY8jJydG4LScnB8eOHavkiKo2JrxERERERqZjx45wcHDQuK1WrVro2LFjJUdUtTHhJSIiIjIyZmZmWLp0qcZtS5cuhZkZU7xH8W4QERERGaF27drBxcVFpczV1RVt27Y1UERVFxNeIiIiIiOUnp6OrKwslbKsrCwuLawBE14iIiIiI6NYQljTfLtcWlgdE14iIiIiI8OlhbXDhJeIiIjIyCiWFjY3N1cpNzc359LCGjDhJSIiIjIyiqWFSyvn0sKqmPASERERGSFPT0+EhYUpk1tJkhAWFgYPDw8DR1b1MOElIiIiMlLh4eFwcnICADg7OyMsLMzAEVVNTHiJiIiIjJSNjQ3mzJkDNzc3zJ49GzY2NoYOqUqSBOetUJObmwsHBwfk5OTA3t7e0OEQERER0WO0ydfYwktEREREJs3gCe/q1avh4+MDmUwGf39/HD16tMz6CQkJ8Pf3h0wmQ4MGDbB27Vq1OjExMWjSpAlsbGzg4eGBiIgI5Ofn6+sSiIiIiKgKM2jCu23bNkyfPh1vvfUWkpOTERwcjF69epW6JF5aWhp69+6N4OBgJCcnY86cOZg6dSp27typrPP5559j1qxZmDdvHlJTU7FhwwZs27YNs2fPrqzLIiIiIqIqxKB9eNu1a4c2bdpgzZo1yjJfX18MGDAAixcvVqsfFRWF3bt3IzU1VVk2adIknDt3DklJSQCA1157DampqTh48KCyzowZM3Dy5Mknth4rsA8vERERUdVmFH14CwsLcebMGfTs2VOlvGfPnkhMTNS4T1JSklr90NBQnD59GkVFRQCAjh074syZMzh58iQA4PLly9i7dy/69OlTaiwFBQXIzc1VeRAREREZg4SEBPTu3RsJCQmGDqXKMljCm52dDblcDhcXF5VyFxcXZGZmatwnMzNTY/3i4mJkZ2cDAIYPH45FixahY8eOsLS0RMOGDdGlSxeNq5EoLF68GA4ODsoHJ2wmIiIiY5CXl4fo6GhkZGQgOjoaeXl5hg6pSjL4oLXHl74TQpS5HJ6m+o+WHzlyBO+99x5Wr16Ns2fPYteuXdizZw8WLVpU6jFnz56NnJwc5ePatWsVvRwiIiKiShMXF4esrCwAQFZWFuLj4w0cUdVkYagT161bF+bm5mqtubdu3VJrxVVwdXXVWN/CwgJ16tQBALzzzjsYNWoUxo8fDwBo3rw57t+/j4kTJ+Ktt96CmZl6jm9tbQ1ra2tdXBYRERFRpUhPT0d8fLyy8U8Igfj4ePTt2xeenp4Gjq5qMVgLr5WVFfz9/XHgwAGV8gMHDiAoKEjjPoGBgWr19+/fj4CAAFhaWgIAHjx4oJbUmpubQwgBrrFBREREpkAIgSVLlqjlNiUlJRrLqzuDdmmIjIzE+vXrERcXh9TUVERERCA9PR2TJk0C8LCrwejRo5X1J02ahKtXryIyMhKpqamIi4vDhg0bMHPmTGWdfv36Yc2aNfjyyy+RlpaGAwcO4J133kH//v1hbm5e6ddIREREpGtpaWlISkpCSUmJSnlJSQmSkpKQlpZmoMiqJoN1aQCAYcOG4fbt21i4cCEyMjLg5+eHvXv3wsvLCwCQkZGhMievj48P9u7di4iICMTGxsLd3R2rVq3C4MGDlXXefvttSJKEt99+Gzdu3ICTkxP69euH9957r9Kvj4iIiEgffHx80KpVK6SkpKhta926NXx8fCo/qCrMoPPwVlWch5eIiIiqMiEExo0bh+TkZLVtrVu3xoYNG8qcBMAUGMU8vERERERUMWlpaRqTXQBITk5ml4bHMOElIiIiMjI+Pj4IDAxUG59kbm6OoKAgdml4DBNeIiIiIiMjSRJmzZqlNmhNCIFZs2aZfHcGbTHhJSIiIjIRnIZVMya8REREREZGMQ+vJpyHVx0TXiIiIiIjo5iH9/HEVgjBeXg1YMJLREREZGQU8/Bqwnl41THhJSIiIjJCHJhWfkx4iYiIiIwM5+HVDhNeIiIiIiPDeXi1w4SXiIiIyMgo5uHVNBsD5+FVx4SXiIiIyAh5enqiRYsWKmUtWrSAh4eHgSKqupjwEhERERmh9PR0/Pbbbyplv/32G9LT0w0UUdXFhJeIiIjIyJS28ISinAtPqGLCS0RERGRkFAtPyOVylXK5XM6FJzRgwktERERkZDhLg3aY8BIREREZGcUsDSUlJSrlQgjO0qABE14iIiIiEyGEYP9dDZjwEhERERkZxeC0x1tyJUnioDUNmPASERERGRnFoLXHuzSUlJRw0JoGTHiJiIiIjAwHrWmHCS8RERGRkVEMWiutnIPWVDHhJSIiIjJCnp6eCAsLUya3kiQhLCyMSwtrwISXiIiIyEiFh4fDyckJAODs7IywsDADR1Q1MeElIiIiMlI2NjaYM2cO3NzcMHv2bNjY2Bg6pCpJEpy3Qk1ubi4cHByQk5MDe3t7Q4dDRERERI/RJl9jCy8RERERmTQmvERERERk0pjwEhEREZFJY8JLRERERCaNCS8RERERmTQmvERERERk0pjwEhEREZFJY8JLRERERCaNCS8RERERmTQmvERERERk0pjwEhEREZFJY8JLRERERCaNCS8RERERmTQmvERERERk0pjwEhEREZFJY8JLRERERCaNCS8RERERmTQmvERERERk0pjwEhEREZFJM3jCu3r1avj4+EAmk8Hf3x9Hjx4ts35CQgL8/f0hk8nQoEEDrF27VmV7586dIUmS2qNPnz76vAwiIiIiqqIMmvBu27YN06dPx1tvvYXk5GQEBwejV69eSE9P11g/LS0NvXv3RnBwMJKTkzFnzhxMnToVO3fuVNbZtWsXMjIylI/z58/D3NwcL774YmVdFhERERFVIZIQQhjq5O3atUObNm2wZs0aZZmvry8GDBiAxYsXq9WPiorC7t27kZqaqiybNGkSzp07h6SkJI3niImJwdy5c5GRkYEaNWqUK67c3Fw4ODggJycH9vb2Wl4VEREREembNvmawVp4CwsLcebMGfTs2VOlvGfPnkhMTNS4T1JSklr90NBQnD59GkVFRRr32bBhA4YPH15msltQUIDc3FyVBxERERGZBoMlvNnZ2ZDL5XBxcVEpd3FxQWZmpsZ9MjMzNdYvLi5Gdna2Wv2TJ0/i/PnzGD9+fJmxLF68GA4ODsqHh4eHlldDRERERFWVwQetSZKk8lwIoVb2pPqayoGHrbt+fn5o27ZtmTHMnj0bOTk5yse1a9fKGz4RERERVXEGS3jr1q0Lc3NztdbcW7duqbXiKri6umqsb2FhgTp16qiUP3jwAF9++eUTW3cBwNraGvb29ioPIiIiImOQkJCA3r17IyEhwdChVFkGS3itrKzg7++PAwcOqJQfOHAAQUFBGvcJDAxUq79//34EBATA0tJSpXz79u0oKCjAyJEjdRu4CeMvDBERkXHJy8tDdHQ0MjIyEB0djby8PEOHVCUZtEtDZGQk1q9fj7i4OKSmpiIiIgLp6emYNGkSgIddDUaPHq2sP2nSJFy9ehWRkZFITU1FXFwcNmzYgJkzZ6ode8OGDRgwYIBayy9pxl8YIiIi4xMXF4esrCwAQFZWFuLj4w0cUdVk0IR32LBhiImJwcKFC9GqVSv8/PPP2Lt3L7y8vAAAGRkZKnPy+vj4YO/evThy5AhatWqFRYsWYdWqVRg8eLDKcf/8808cO3YM48aNq9TrMWb8hSEiIjIu6enpiI+PV45nEkIgPj6+1PUMqjODzsNbVVW3eXjT09MxaNAgyOVyZZmFhQV27twJT09PA0ZGREREmgghMGXKFJw8eVLl/7e5uTnatm2L2NjYMicBMAVGMQ8vVQ1CCCxZsqTUcn4eIiIiqnrS0tKQlJSkkuwCgFwuR1JSEtLS0gwUWdXEhLea4y8MERGR8fHx8UFgYCDMzc1Vys3NzREUFAQfHx8DRVY1MeGt5vgLQ0REZHwkScKsWbNKLTf17gzaYsJbzSl+MUpKSlTKhRD8hSEiIqrCPD09ERYWpvxfLUkSwsLCuGKsBkx4SSMhBPvvEhERVXHh4eFwcnICADg7OyMsLMzAEVVNTHirOcXgtMdbciVJ4qA1IiKiKs7Gxgb9+/eHubk5+vXrBxsbG0OHVCUx4a3mFIPWHu/SUFJSwkFrREREVVxeXh52794NuVyO3bt3c+GoUjDhreY4aI2IiMh4ceGo8mHCW81xlCcREZFx4kpr5ceElzjKk4iIyMhw4SjtMOElABzlSUREZEy4cJR2mPASgIejPOfMmQM3NzfMnj2bozyJiIiqMI7B0Y4k2OatJjc3Fw4ODsjJyYG9vb2hwyEiIiJSk56ejkGDBqm08lpYWGDXrl3VoluiNvkaW3iJiIiIjBDH4JQfE14iIiIiI8UxOOVjYegAiIiIiEyREAL5+fl6P8frr7+Ojz/+GJGRkQCg18UnZDKZUU5Zyj68GrAPLxERET2tvLw8BAUFGToMnUpMTKwyA9vZh5eIiIiI6H/YpYGIiIhID2QyGRITE/V6jry8PHTr1g0AcPDgQb23vspkMr0eX1+Y8BIRERHpgSRJlfr1v42NTZXpblDVsEsDEREREZk0JryklJCQgN69eyMhIcHQoRARERHpDBNeAvCwD1B0dDQyMjIQHR2t1ylNiIiIiCoTE14CAMTFxeHWrVsAgFu3biE+Pt7AERERERHpBhNeQnp6OuLi4lTK4uLikJ6ebqCIiIiIiHSHCW81J4TAkiVL8Pj6IyUlJRrLiYiIiIwNE95qLi0tDUlJSWqJrRACSUlJSEtLM1BkRERERLrBhLea8/b2hoODg8ZttWrVgre3d+UGRERERKRjTHiruStXriAnJ0fjtrt37+LKlSuVGxARERGRjjHhreZ8fHwQGBgISZJUyiVJQlBQEHx8fAwUGREREZFuMOGt5iRJwqxZs2BmpvpWMDc3x6xZs9QSYSIiIiJjw4SX4OnpibCwMGVyK0kSwsLC4OHhYeDIiIiIiJ4eE14CAISHh8PJyQkA4OzsjLCwMANHRERERKQbTHgJAGBjY4P+/fvD3Nwc/fr1g42NjaFDIiIiItIJJrwEAMjLy8Pu3bshl8uxe/du5OXlGTokIiIiIp1gwksAHi4lnJWVBQDIyspCfHy8gSMiIiIi0g0mvIT09HTEx8crV1sTQiA+Ph7p6ekGjoyIiIjo6THhreaEEFiyZEmp5Y8vOUxERERkbJjwVnNpaWlISkqCXC5XKZfL5UhKSkJaWpqBIiMiIiLSDSa81ZxipTVzc3OVcnNzc660RkRERCaBCW81p1hprbRyrrRGRERExq7CCW9hYSGuX7+O9PR0lQcZH660RkRERKZM64T30qVLCA4Oho2NDby8vODj4wMfHx94e3vz628jxpXWiIiIyFRZaLvD2LFjYWFhgT179sDNzY1feZsIGxsbzJkzB0uXLkVUVBRXWiMiIiKToXULb0pKCj755BP06tULrVq1QsuWLVUe2lq9ejV8fHwgk8ng7++Po0ePllk/ISEB/v7+kMlkaNCgAdauXatW5+7du5gyZQrc3Nwgk8ng6+uLvXv3ah1bdRMSEoK9e/ciJCTE0KEQERER6YzWCW+zZs2QnZ2tk5Nv27YN06dPx1tvvYXk5GQEBwejV69epfYFTktLQ+/evREcHIzk5GTMmTMHU6dOxc6dO5V1CgsL0aNHD1y5cgVfffUVLl68iHXr1qFevXo6iZmIiIiIjIsktFxZ4NChQ3j77bcRHR2N5s2bw9LSUmW7vb19uY/Vrl07tGnTBmvWrFGW+fr6YsCAAVi8eLFa/aioKOzevRupqanKskmTJuHcuXNISkoCAKxduxbLly/HH3/8oRZbeeXm5sLBwQE5OTlaXQ8RERFRZcrLy0NQUBAAIDExsVp1SdQmX9O6hbd79+44fvw4unXrBmdnZzg6OsLR0RG1atWCo6NjuY9TWFiIM2fOoGfPnirlPXv2RGJiosZ9kpKS1OqHhobi9OnTKCoqAgDs3r0bgYGBmDJlClxcXODn54fo6Gi1hRWIiIiIqHrQetDa4cOHdXLi7OxsyOVyuLi4qJS7uLggMzNT4z6ZmZka6xcXFyM7Oxtubm64fPkyDh06hJdffhl79+7FpUuXMGXKFBQXF2Pu3Lkaj1tQUICCggLl89zc3Ke8OiIiIiKqKrROeHU9oOnxWR6EEGXO/KCp/qPlJSUlcHZ2xqeffgpzc3P4+/vj5s2bWL58eakJ7+LFi7FgwYKnuQwiIiIiqqK0TniBh7MgbNiwAampqZAkCc2aNUN4eDgcHBzKfYy6devC3NxcrTX31q1baq24Cq6urhrrW1hYoE6dOgAANzc3WFpaqiyV6+vri8zMTBQWFsLKykrtuLNnz0ZkZKTyeW5uLhddICIiIjIRWvfhPX36NBo2bIiVK1fizp07yM7OxooVK9CwYUOcPXu23MexsrKCv78/Dhw4oFJ+4MABZefrxwUGBqrV379/PwICApQD1Dp06IC//voLJSUlyjp//vkn3NzcNCa7AGBtbQ17e3uVBxERERGZBq0T3oiICPTv3x9XrlzBrl278PXXXyMtLQ19+/bF9OnTtTpWZGQk1q9fj7i4OKSmpiIiIgLp6emYNGkSgIctr6NHj1bWnzRpEq5evYrIyEikpqYiLi4OGzZswMyZM5V1Xn31Vdy+fRvTpk3Dn3/+ie+//x7R0dGYMmWKtpda7SQkJKB3795ISEgwdChEREREOqN1l4bTp09j3bp1sLD4/10tLCzw5ptvIiAgQKtjDRs2DLdv38bChQuRkZEBPz8/7N27F15eXgCAjIwMlTl5fXx8sHfvXkRERCA2Nhbu7u5YtWoVBg8erKzj4eGB/fv3IyIiAi1atEC9evUwbdo0REVFaXup1UpeXh6io6Nx69YtREdHo23bttVqahMiIiIyXVonvPb29khPT0fTpk1Vyq9du4aaNWtqHcDkyZMxefJkjds2btyoVhYSEvLErhOBgYE4fvy41rFUZ3FxccjKygIAZGVlIT4+vtTXhYiIiMiYaN2lYdiwYRg3bhy2bduGa9eu4fr16/jyyy8xfvx4vPTSS/qIkfQsPT0d8fHxyhkvhBCIj48vdcU7IiIiImOidQvv+++/D0mSMHr0aBQXFwMALC0t8eqrr2LJkiU6D5D0Swih8XVTlMfGxpY5TRwRERFRVad1wmtlZYUPP/wQixcvxt9//w0hBBo1agRbW1t9xEd6lpaWplyW+VFyuRxJSUlIS0tDgwYNDBAZERERkW5o3aVBwdbWFs2bN0eLFi2Y7BoxHx8ftG7dWuO21q1bw8fHp5IjIiKip8VZd4hUlauFd9CgQdi4cSPs7e0xaNCgMuvu2rVLJ4FR5VH03SUiIuPHWXeI1JWrhdfBwUHZj9Pe3h4ODg6lPsi4pKWlISUlReO25ORkpKWlVW5ARET0VDTNukNU3ZWrhffRXxZNU4WR8VJ0aUhOTlbbxi4NRETGpbRZd/r27QtPT08DR0dkOFr34e3atSvu3r2rVp6bm4uuXbvqIiaqZPn5+RrLCwoKKjkSIiKqqCfNusPua1SdaZ3wHjlyBIWFhWrl+fn5OHr0qE6Cospz+fJlpKamatx24cIFXL58uZIjIiKiilDMuiOXy1XKH511h6i6Kve0ZL/++qvy5wsXLiAzM1P5XC6XY9++fahXr55uoyMiIqJy8fHxQWBgIE6cOIGSkhJluZmZGdq3b88ualStlTvhbdWqFSRJgiRJGrsu2NjY4KOPPtJpcKR/DRo0KLUPb5s2bTgHLxGRkZAkCbNmzcKAAQNUyoUQmDVrFhcRomqt3AlvWloahBBo0KABTp48CScnJ+U2KysrODs7w9zcXC9Bkv5IkoT58+djwIABKv27zMzMMH/+fP6BJCIycpIksf8uVXvlTni9vLwAQOVrEjINnp6eaN68uUq3lebNm8PDw8OAURERkTYUg9PMzMxU+vFKksSl4qna03ppYYULFy4gPT1dbQBb//79nzooqlzp6ek4f/68Stn58+eRnp7OaWyIiIwEl4onKp3WCe/ly5cxcOBA/Pbbbypfkyg+NT4+OpSqNkWLgKZP/WwRICIyHpxXnah0Wk9LNm3aNPj4+OCff/6Bra0tfv/9d/z8888ICAjAkSNH9BAi6ROnsSEiMh3sq0ukmdYJb1JSEhYuXAgnJyeYmZnBzMwMHTt2xOLFizF16lR9xEh6pJjG5vEBh+bm5ggKCmKLABGRkeBS8USl0zrhlcvlsLOzAwDUrVsXN2/eBPBwUNvFixd1Gx3pnWIam9LK2Z2BiMg4KBowzMxU/7WbmZmxAYOqPa0TXj8/P+Vo/nbt2mHZsmX45ZdfsHDhQnaGN1Kenp4ICwtTJreSJCEsLIyzNBARGZHSGirMzMzYgEHVntYJ79tvv62cmuzdd9/F1atXERwcjL1792LVqlU6D5AqR3h4OOzt7QEADg4OCAsLM3BERESkLTZgEGmm9SwNoaGhyp8bNGiACxcu4M6dO3B0dOSnRxPBQQ9ERMYrPDwcu3fvxq1bt+Ds7MwGDCJUoIVXk9q1azPZNXJxcXHIzc0FAOTm5iI+Pt7AERERUUXY2Nigf//+MDc3R79+/WBjY2PokIgMrlwtvIMGDSr3AXft2lXhYMgw0tPTER8fr2zZFUIgPj4effv25cITRERGJi8vDzt27IBcLseOHTsQHh7OpJeqvXK18Do4OJT7QcZFsfBEaeXs3kBEZFw+/fRT5OTkAABycnKwbt06A0dEZHjlauHl19umi0tREhGZjvT0dHz22WcqZZ999hkGDBjAb+yoWqtQH97i4mL89NNP+OSTT3Dv3j0AwM2bN/Hff//pNDjSPy48QURkGoQQmD9/vto3cyUlJRrLiaoTrRPeq1evonnz5njhhRcwZcoUZGVlAQCWLVuGmTNn6jxA0i/FvI2KqeYUhBCct5GIyIhcvnwZycnJGrclJyfj8uXLlRwRUdWhdcI7bdo0BAQE4N9//1XpBD9w4EAcPHhQp8GR4Qgh2BpAREREJkHrhPfYsWN4++23YWVlpVLu5eWFGzdu6CwwqhyKwWmPt+RKksRBa0RERqRBgwZo1aqVxm1t2rTheAyq1rROeEtKSiCXy9XKr1+/jpo1a+okKKo8ikFrj3dpKCkpUQ5aIyKiqk+SJCxYsEBjA8b8+fPZRY2qNa0T3h49eiAmJkb5XJIk/Pfff5g3bx569+6ty9ioEnDQGhGR6fD09MSYMWNUysaOHculhana0zrhXbFiBRISEtCsWTPk5+djxIgR8Pb2xo0bN7B06VJ9xEh6pBi0Vlo5WwSIiIzLxIkTlfPi16pVCxMmTDBwRESGp3XCW69ePaSkpOCNN97AK6+8gtatW2PJkiVITk6Gs7OzPmIkPfP09ERYWJgyuZUkCWFhYWwRICIyQjY2NliwYAHc3Nwwf/58rrJGBEASWoxKKioqQpMmTbBnzx40a9ZMn3EZVG5uLhwcHJCTkwN7e3tDh1Mp8vLyMGDAANy6dQsuLi74+uuv+UeSiIioisvLy0NQUBAAIDExsVr979YmXyvXSmsKlpaWKCgo4NfclUwIgfz8fL2f4/XXX8fHH3+MyMhIAA9/ifRFJpPxfURERESVQquEFwBef/11LF26FOvXr4eFhda7UwXk5+crP71VhqioKL2fo7p9CiUiqkwJCQlYunQpoqKiEBISYuhwiAxO64z1xIkTOHjwIPbv34/mzZujRo0aKtt37dqls+CIiIhIO3l5eYiOjsatW7cQHR2Ntm3bsoGBqj2tE95atWph8ODB+oiFSiGTyZCYmKjXc+Tl5aFbt24AgIMHD+r9j6NMJtPr8YmIqqu4uDhkZWUBALKyshAfH4/JkycbOCoiw9Iq4S0uLkbnzp0RGhoKV1dXfcVEj5EkqVI/ndvY2LA1gIjICKWnpyM+Pl65SqYQAvHx8ejbty88PT0NHB2R4Wg1LZmFhQVeffVVFBQU6CseIiIiqgDFUvGllXOpeKrOtJ6Ht127dkhOTtZHLERERFRBiqXi5XK5SrlcLudS8VTtad2Hd/LkyZgxYwauX78Of39/tUFrLVq00FlwREREVD6KpeJPnDiBkpISZbmZmRnat2/PpeKpWtM64R02bBgAYOrUqcoySZIghIAkSWqfLImIiEj/FEvCv/DCCyrlJSUlXCqeqj2tE15+JUJERGRc2H+XqjutE14vLy99xEFERERPQQiB2bNna9w2e/ZsbNmyha28VG1pPWgNAP7++2+8/vrr6N69O3r06IGpU6fi77//rlAAq1evho+PD2QyGfz9/XH06NEy6yckJMDf3x8ymQwNGjTA2rVrVbZv3LgRkiSpPfS9NC8REZEh/f3337hw4YLGbRcuXKjw/2kiU6B1wvvjjz+iWbNmOHnyJFq0aAE/Pz+cOHECzz77LA4cOKDVsbZt24bp06fjrbfeQnJyMoKDg9GrVy+kp6drrJ+WlobevXsjODgYycnJmDNnDqZOnYqdO3eq1LO3t0dGRobKgwsdEBGRKbtx48ZTbScyZVp3aZg1axYiIiLU5vqbNWsWoqKi0KNHj3Ifa8WKFRg3bhzGjx8PAIiJicGPP/6INWvWYPHixWr1165dC09PT8TExAAAfH19cfr0abz//vsqq79JksSFMYiIqFoJDg5GzZo1ce/ePbVt9vb2CA4ONkBURFWD1i28qampGDdunFp5eHh4qV+laFJYWIgzZ86gZ8+eKuU9e/YsdRndpKQktfqhoaE4ffo0ioqKlGX//fcfvLy8UL9+ffTt2/eJ8wYXFBQgNzdX5UFERGRMzMzMsHz5co3bli9fDjOzCvViJDIJWr/7nZyckJKSolaekpICZ2fnch8nOzsbcrkcLi4uKuUuLi7IzMzUuE9mZqbG+sXFxcjOzgYANG3aFBs3bsTu3buxdetWyGQydOjQAZcuXSo1lsWLF8PBwUH58PDwKPd1EBERVRXt2rVTmw+/ZcuWaNu2rYEiIqoatO7SMGHCBEycOBGXL19GUFAQJEnCsWPHsHTpUsyYMUPrAB4fMaqYz1eb+o+Wt2/fHu3bt1du79ChA9q0aYOPPvoIq1at0njM2bNnIzIyUvk8NzeXSS8RERmlmJgYdOvWTfn/dOXKlYYOicjgtE5433nnHdSsWRMffPCBcvoTd3d3zJ8/X2UxiiepW7cuzM3N1Vpzb926pdaKq+Dq6qqxvoWFBerUqaNxHzMzMzz33HNltvBaW1vD2tq63LETERFVVY6Ojhg3bhzi4+MRFhYGR0dHQ4dEZHBad2mQJAkRERG4fv06cnJykJOTg+vXr2PatGlaze9nZWUFf39/tZkdDhw4gKCgII37BAYGqtXfv38/AgICYGlpqXEfIQRSUlLg5uZW7tiIiIiM2ZQpU3D69GlMmTLF0KEQVQlaJ7xpaWnK1tKaNWuiZs2aAIBLly7hypUrWh0rMjIS69evR1xcHFJTUxEREYH09HRMmjQJwMOuBqNHj1bWnzRpEq5evYrIyEikpqYiLi4OGzZswMyZM5V1FixYgB9//BGXL19GSkoKxo0bh5SUFOUxiYiIiKh60bpLw9ixYxEeHo7GjRurlJ84cQLr16/HkSNHyn2sYcOG4fbt21i4cCEyMjLg5+eHvXv3Kldzy8jIUJmT18fHB3v37kVERARiY2Ph7u6OVatWqUxJdvfuXUycOBGZmZlwcHBA69at8fPPP7PDPhEREVE1JQktF9i2t7fH2bNn0ahRI5Xyv/76CwEBAbh7964u4zOI3NxcODg4ICcnB/b29oYOp1Lk5eUpu5IkJibCxsbGwBEREVFFxcbGKvvwsluDaavO/7+1ydcq1IdX06TWOTk5kMvl2h6OiIiIdOjff/9FXFwc5HI54uLi8O+//xo6JCKD0zrhDQ4OxuLFi1WSW7lcjsWLF6Njx446DY6IiIi0ExkZiZKSEgBASUlJhaYMJTI1WvfhXbZsGTp16oQmTZoolyk8evQocnNzcejQIZ0HSEREROVz4sQJtcWhkpOTceLECbRr184wQRFVAVq38DZr1gy//vorhg4dilu3buHevXsYPXo0/vjjD/j5+ekjRiIiInqCkpISREVFadwWFRWlbPUlqo60buEFHi40ER0dretYiIiIqIKOHTuGnJwcjdtycnJw7NgxdOrUqZKjqtqEEMjPzzd0GE8lLy9P48/GTCaTabW2Q3lUKOElIiKiqqVjx47KEeuPq1WrFsfZaJCfn1/qYlfGqFu3boYOQSf0MduE1l0aiIiIqOoxMzPD0qVLNW5bunQpzMz4L5+qL7bwEhERmYh27dqhVatWKgPXWrduzcWXyuG93rNhZWFl6DC0JoRAkbwIAGBpbqnzrgCVpbC4EG/tXay34zPhJSIiMiErVqxA9+7dUVJSAjMzM3zwwQeGDskoWFlYwdoIE14AkFlaGzqEKo/fbxAREZkQR0dHhIeHw9zcHOHh4XB0dDR0SEQGV64W3tatW5e7ifzs2bNPFRARERE9nSlTpnBJYaJHlCvhHTBggJ7DICIiIiLSj3IlvPPmzdN3HEREREREesE+vERERERk0rSepUEul2PlypXYvn070tPTUVhYqLL9zp07OguOiIiIiOhpad3Cu2DBAqxYsQJDhw5FTk4OIiMjMWjQIJiZmWH+/Pl6CJGIiIiIqOK0Tng///xzrFu3DjNnzoSFhQVeeuklrF+/HnPnzsXx48f1ESMRERERUYVp3aUhMzMTzZs3BwDY2dkp1+zu27cv3nnnHd1GR0REZEKEEMjPz6+0c8hkMr2vvFUZ5yB6WlonvPXr10dGRgY8PT3RqFEj7N+/H23atMGpU6dgbc2VPoiIiEqTn5+PoKAgQ4ehU4mJibCxsTF0GERl0rpLw8CBA3Hw4EEAwLRp0/DOO++gcePGGD16NMLDw3UeIBERERHR09C6hXfJkiXKn4cMGYL69esjMTERjRo1Qv/+/XUaHBERkSmRyWRITEzU6zny8vLQrVs3AMDBgwf13voqk8n0enwiXdA64X1c+/bt0b59e13EQkREZNIkSarUr/9tbGzY3YAI5Ux4d+/ejV69esHS0hK7d+8usy5beYmIiIioKilXwjtgwABkZmbC2dkZAwYMKLWeJEmQy+W6io2IiIiI6KmVK+EtKSnR+DMRERERUVWn9SwNmzZtQkFBgVp5YWEhNm3apJOgiIiIiIh0ReuENywsTLnYxKPu3buHsLAwnQRFRERERKQrWie8QgiNK6pcv34dDg4OOgmKiIiIiEhXyj0tWevWrSFJEiRJQrdu3WBh8f+7yuVypKWl4fnnn9dLkEREREREFVXuhFcxO0NKSgpCQ0NhZ2en3GZlZQVvb28MHjxY5wESERERET2Ncie88+bNAwB4e3tj2LBhXFmFiIiIiIyC1iutjRkzRh9xEBERERHphdYJr1wux8qVK7F9+3akp6ejsLBQZfudO3d0FhwRERER0dPSepaGBQsWYMWKFRg6dChycnIQGRmJQYMGwczMDPPnz9dDiEREREREFad1wvv5559j3bp1mDlzJiwsLPDSSy9h/fr1mDt3Lo4fP66PGImIiIiIKkzrhDczMxPNmzcHANjZ2SkXoejbty++//573UZHRERERPSUtE5469evj4yMDABAo0aNsH//fgDAqVOnYG1trdvoiIiIiIiektYJ78CBA3Hw4EEAwLRp0/DOO++gcePGGD16NMLDw3UeIBERERHR09B6loYlS5Yofx4yZAjq16+PxMRENGrUCP3799dpcERERERET0vrhPdx7du3R/v27XURCxERERGRzlUo4b148SI++ugjpKamQpIkNG3aFK+//jqaNGmi6/iIiIiIiJ6K1n14v/rqK/j5+eHMmTNo2bIlWrRogbNnz8LPzw87duzQR4xERERERBWmdQvvm2++idmzZ2PhwoUq5fPmzUNUVBRefPFFnQVHREREpC9CCOXPhcWFZdQkfXv0/j/6uuiK1glvZmYmRo8erVY+cuRILF++XCdBEREREelbfn6+8ue39i42YCT0qPz8fNja2ur0mFp3aejcuTOOHj2qVn7s2DEEBwfrJCgiIiIiIl3RuoW3f//+iIqKwpkzZ5SzMxw/fhw7duzAggULsHv3bpW6T7J69WosX74cGRkZePbZZxETE1Nm4pyQkIDIyEj8/vvvcHd3x5tvvolJkyZprPvll1/ipZdewgsvvIBvvvlGuwslIiIikyaTyZQ/v9d7NqwsrAwYTfVWWFyobGV/9HXRFa0T3smTJwN4mKiuXr1a4zYAkCQJcrm8zGNt27YN06dPx+rVq9GhQwd88skn6NWrFy5cuABPT0+1+mlpaejduzcmTJiALVu24JdffsHkyZPh5OSEwYMHq9S9evUqZs6cyVZnIiIi0kiSJOXPVhZWsGbCWyU8+rroitZdGkpKSsr1eFKyCwArVqzAuHHjMH78ePj6+iImJgYeHh5Ys2aNxvpr166Fp6cnYmJi4Ovri/HjxyM8PBzvv/++Sj25XI6XX34ZCxYsQIMGDbS9RCIiIiIyIVonvJrcvXtX630KCwtx5swZ9OzZU6W8Z8+eSExM1LhPUlKSWv3Q0FCcPn0aRUVFyrKFCxfCyckJ48aNK1csBQUFyM3NVXkQERERkWnQOuFdunQptm3bpnz+4osvonbt2qhXrx7OnTtX7uNkZ2dDLpfDxcVFpdzFxQWZmZka98nMzNRYv7i4GNnZ2QCAX375BRs2bMC6devKHcvixYvh4OCgfHh4eJR7XyIiIiKq2rROeD/55BNlQnjgwAH89NNP2LdvH3r16oU33nhD6wAe76chhCiz74am+orye/fuYeTIkVi3bh3q1q1b7hhmz56NnJwc5ePatWtaXAERERERVWVaD1rLyMhQJrx79uzB0KFD0bNnT3h7e6Ndu3blPk7dunVhbm6u1pp769YttVZcBVdXV431LSwsUKdOHfz++++4cuUK+vXrp9xeUlICALCwsMDFixfRsGFDteNaW1vD2tq63LETERERkfHQuoXX0dFR2QK6b98+dO/eHcDDltbyDFRTsLKygr+/Pw4cOKBSfuDAAQQFBWncJzAwUK3+/v37ERAQAEtLSzRt2hS//fYbUlJSlI/+/fujS5cuSElJYVcFIiIiompI6xbeQYMGYcSIEWjcuDFu376NXr16AQBSUlLQqFEjrY4VGRmJUaNGISAgAIGBgfj000+Rnp6unFd39uzZuHHjBjZt2gQAmDRpEj7++GNERkZiwoQJSEpKwoYNG7B161YAD+dt8/PzUzlHrVq1AECtnIiIiIiqB60T3pUrV8Lb2xvXrl3DsmXLYGdnB+BhV4dH5+Etj2HDhuH27dtYuHAhMjIy4Ofnh71798LLy0t5zPT0dGV9Hx8f7N27FxEREYiNjYW7uztWrVqlNgcvEREREZGCJBSjvkgpNzcXDg4OyMnJgb29vaHDqRR5eXnKriSJiYmwsbExcERERFQR/Htefo/eq+X953HhCQMqKC7EG7sXACj/+1abfE0n8/ASEREREVVVTHiJiIiIyKQx4SUiIiIik8aEl4iIiIhMWrkT3pMnT6rMs/v4WLeCggJs375dd5EREREREelAuRPewMBA3L59W/ncwcEBly9fVj6/e/cuXnrpJd1GR0RERET0lMqd8D7eoqtpNjPOcEZEREREVY1O+/BKkqTLwxERERERPTWtV1ojdUII5OfnGzqMp5KXl6fxZ2Mmk8n4IYyIiIi0S3gvXLiAzMxMAA+TvD/++AP//fcfACA7O1v30RmJ/Px85UotpqBbt26GDkEnuMIQERERAVomvN26dVPpp9u3b18AD7syCCHYmkZEREREVU65E960tDR9xmEy/mkXDmFuaegwtCcEpJLihz+aWQBG+uFFkhfB5UScocMgIiKiKqTcCa+Xl5c+4zAZwtzSOBNeAAJWhg6BiIiISOfKPUvDnTt3cP36dZWy33//HWFhYRg6dCi++OILnQdHRERERPS0yp3wTpkyBStWrFA+v3XrFoKDg3Hq1CkUFBRg7Nix2Lx5s16CJCIiIiKqqHInvMePH0f//v2Vzzdt2oTatWsjJSUF3377LaKjoxEbG6uXIImIiIiIKqrcCW9mZiZ8fHyUzw8dOoSBAwfCwuJhN+D+/fvj0qVLuo+QiIiIiOgplHvQmr29Pe7evascvHby5EmMGzdOuV2SJBQUFOg+QiIiIiI9KywuNHQIFSKEQJG8CABgaW5ptFPE6vv+lzvhbdu2LVatWoV169Zh165duHfvHrp27arc/ueff8LDw0MvQRIRERHp01t7Fxs6BNKjcie8ixYtQvfu3bFlyxYUFxdjzpw5cHR0VG7/8ssvERISopcgiYiIiIgqqtwJb6tWrZCamorExES4urqiXbt2KtuHDx+OZs2a6TxAIiIiIn2QyWRITEw0dBhPJS8vD926dQMAHDx4EDY2NgaO6OnJZDKdH1OrpYWdnJzwwgsvaNzWp08fnQREREREVBkkSTKJBFHBxsbGpK5Hl8qd8G7atKlc9UaPHl3hYIiIiIiIdK3cCe/YsWNhZ2cHCwsLCCE01pEkiQkvEREREVUp5U54fX198c8//2DkyJEIDw9HixYt9BkXEREREZFOlHvhid9//x3ff/898vLy0KlTJwQEBGDNmjXIzc3VZ3xERKQDCQkJ6N27NxISEgwdChFRpSt3wgsA7dq1wyeffIKMjAxMnToV27dvh5ubG15++WUuOkFEVEXl5eUhOjoaGRkZiI6ORl5enqFDIiKqVFolvAo2NjYYPXo0FixYgLZt2+LLL7/EgwcPdB0bERHpQFxcHLKysgAAWVlZiI+PN3BERESVS+uE98aNG4iOjkbjxo0xfPhwPPfcc/j9999VFqEgIqKqIT09HfHx8crBxkIIxMfHIz093cCRERFVnnInvNu3b0evXr3QuHFjnDp1Ch988AGuXbuGZcuWoWnTpvqMkYiIKkAIgSVLlpRaXtqMO0REpqbcszQMHz4cnp6eiIiIgIuLC65cuYLY2Fi1elOnTtVpgESkHwkJCVi6dCmioqK4LLiJSktLQ1JSklq5XC5HUlIS0tLS0KBBAwNERkRUucqd8Hp6ekKSJHzxxRel1pEkiQkvkRFQDGK6desWoqOj0bZtW67OY4J8fHwQGBiIEydOoKSkRFluZmaG9u3bw8fHx4DRERFVnnInvFeuXNFjGERUmTQNYpo8ebKBoyJdkyQJs2bNwoABA1TKhRCYNWsWJEkyTGBERJWsQrM0lObGjRu6PBwR6QEHMZEkSey/S0TVik4S3szMTLz++uto1KiRLg5HRHrCQUzVi+J1NTNT/VMvSRJfbyKqVsqd8N69excvv/wynJyc4O7ujlWrVqGkpARz585FgwYNcPz4ccTFxekzViJ6SopBTHK5XKX80UFMZDr4ehMRPVTuhHfOnDn4+eefMWbMGNSuXRsRERHo27cvjh07hh9++AGnTp3CSy+9pM9YiegpKQYxmZubq5Sbm5sjKCiIg5hMDF9vIqKHyj1o7fvvv0d8fDy6d++OyZMno1GjRnjmmWcQExOjx/CISJcUg5gGDRqksZyDmEwLX2/tCSGQn59v6DCeyqNLR5vKMtIymYzvV3oq5U54b968iWbNmgEAGjRoAJlMhvHjx+stMCLSD09PT4SFhWHDhg0QQkCSJISFhcHDw8PQoZEe8PXWTn5+PoKCggwdhs5069bN0CHoRGJiIqdOpKdS7i4NJSUlsLS0VD43NzdHjRo19BIUEenXiBEjlK0lkiSxO5KJCw8PVyYLtra2CAsLM3BERESVq9wtvEIIjB07FtbW1gAefgqeNGmSWtK7a9cu3UZIRDr3xRdfKBciKCkpwdatWzkPrwnLz89XfrX94MED5Ofns7WsHOb6ZcPKzPhmshACKPpf2JYSYKw9AQpLJCw8X9fQYZCJKHfCO2bMGJXnI0eO1HkwRKR/6enpajOqxMXFoW/fvvD09DRQVKRPkZGRKvMuz5gxg7PqlIOVmYCV+ZPrVUXWhg5AJ4zvwwZVXeVOeOPj4/UZh1F7dC5LSV5kwEjo0fvPOUbVlTbfbklJCZYsWYLY2FgODDExJ06cQEpKikpZcnIyTpw4gXbt2hkmKCKiSlbuhJdK9+iIXpcTbDWpKvLz82Fra2voMKoUxbysjxNCKOdlbdCggQEiI30oKSlBVFSUxm1RUVE4dOiQ2qIURESmiH/piKoRb29vODg4aNxWq1YteHt7V25ApFfHjh1DTk6Oxm05OTk4duxYJUdERGQYBm/hXb16NZYvX46MjAw8++yziImJQXBwcKn1ExISEBkZid9//x3u7u548803MWnSJOX2Xbt2ITo6Gn/99ReKiorQuHFjzJgxA6NGjdLbNchkMuXP/7QLhzC3LKM26ZMkL1K2sj/6utBDV65cKTUBunv3Lq5cucIWXhPSsWNHODg4aHzNa9WqhY4dOxogKiKiymfQhHfbtm2YPn06Vq9ejQ4dOuCTTz5Br169cOHCBY2DZ9LS0tC7d29MmDABW7ZswS+//ILJkyfDyckJgwcPBgDUrl0bb731Fpo2bQorKyvs2bMHYWFhcHZ2RmhoqF6u49E+j8LckglvFcG+qOoUK28dP35cte+5JCEwMJArb5kYMzMzLF26VKVRQGHp0qXszkBE1YZB/9qtWLEC48aNw/jx4+Hr64uYmBh4eHhgzZo1GuuvXbsWnp6eiImJga+vL8aPH4/w8HC8//77yjqdO3fGwIED4evri4YNG2LatGlo0aIFv7ojwv+vsPV4omNubs6Vt0xUu3bt4OLiolLm6uqKtm3bGigiIqLKZ7CEt7CwEGfOnEHPnj1Vynv27InExESN+yQlJanVDw0NxenTp1FUpD47ghACBw8exMWLF9GpU6dSYykoKEBubq7Kg8hUeXp6qnXxGTVqFFfeMlHp6enIzs5WKcvOzkZ6erqBIiIiqnwGS3izs7Mhl8vVWh5cXFyQmZmpcZ/MzEyN9YuLi1X+oOfk5MDOzg5WVlbo06cPPvroI/To0aPUWBYvXgwHBwflg//4icgUKKahK62cU/cRUXVh8A5cj3+FqljrXZv6j5fXrFkTKSkpOHXqFN577z1ERkbiyJEjpR5z9uzZyMnJUT6uXbtWgSshMg7p6enYvHmzStnmzZvZ4meCFNPQyeVylXK5XK6cho6IqDowWMJbt25dmJubq7Xm3rp1S60VV8HV1VVjfQsLC9SpU0dZZmZmhkaNGqFVq1aYMWMGhgwZgsWLF5cai7W1Nezt7VUeRKaILX7Vi2KQorm56nJh5ubmCAoK4iBFIqo2DJbwWllZwd/fHwcOHFApP3DgAIKCgjTuExgYqFZ///79CAgIgKVl6TMjCCFQUFDw9EETGTm2+FUvikGKpZVzkCIRVRcG7dIQGRmJ9evXIy4uDqmpqYiIiEB6erpyCp3Zs2dj9OjRyvqTJk3C1atXERkZidTUVMTFxWHDhg2YOXOmss7ixYtx4MABXL58GX/88QdWrFiBTZs2YeTIkZV+fURVDVv8qh9PT0+EhYUpk1tJkhAWFsaxCkRUrRh0Ht5hw4bh9u3bWLhwITIyMuDn54e9e/fCy8sLAJCRkaHSr9DHxwd79+5FREQEYmNj4e7ujlWrVinn4AWA+/fvY/Lkybh+/TpsbGzQtGlTbNmyBcOGDav06yOqahQte4MGDdJYzhY/0xQeHo7du3fj1q1bcHZ2RlhYmKFDIiKqVAZfaW3y5MmYPHmyxm0bN25UKwsJCcHZs2dLPd67776Ld999V1fhEZkcxbRkj/5+cVoy02ZjY4M5c+Zg6dKliIqKgo2NjaFDIiKqVAafpYGIiIiISJ+Y8BJVM5yWrPrJy8vDnDlzkJGRgTlz5iAvL8/QIRERVSomvETVCKclq55Wr16NBw8eAAAePHhQ6vLtRESmigkvUTXCacmqn/T0dGzZskWljC36RFTdMOElqkY4LVn1IoTQOA8vAMyaNYst+kRUbTDhJapGuBBB9fL3338jNTVV47bU1FT8/ffflRwREZFhMOElqma4EEH1cePGjafaTkRkKpjwElVD4eHhcHJyAgAuRGDCgoODUbNmTY3b7O3tERwcXMkREREZBhNeompIsRCBm5sbZs+ezYUITJSZmRmWL1+ucdvy5cthZsZ/AURUPRh8pTUiUiWEQH5+vt7P0bx5c2zZsgUymUzv87LKZDL2DzaQdu3aoUWLFvj111+VZS1btkTbtm0NGBURUeViwktUxeTn5yMoKMjQYehUYmIiW5ENKCYmBt26dYMQApIkYeXKlYYOiYioUvH7LCIiE+fo6Ihx48bB3Nwc48aNg6Ojo6FDIiKqVGzhJapiZDIZEhMT9XqOvLw8dOvWDQBw8OBBvbe+ymQyvR6fnmzKlCmYMmWKocMgIjIIJrw6JsmLDB1CxQgBqaT44Y9mFoCR9rc02vv/CEmSKvXrfxsbG3Y3ICIik8aEV8dcTsQZOgQiMiKVNUhRcY7KGEDIQYpEVNUw4SUiMiAOUiQi0j8mvDpQGX0u9a2y+3RWBvYbJSJtCSGUPxeWGDAQUrn/j74uRBXBhFcHKrvPpb6xTydR5eEgxarl0e4lC887GTASelR+fj5sbW0NHQYZMSa8REQGxEGKRET6x4SXiIjofx5tnZ7rlwUrzlZvMIUl/9/KbszfGlDVwISXiIjofx6dXcLKDLAyN2AwpGSss35Uxiwsjy4Nr+9l4gHjnYWFCS8RERGRHlT2LCyKvvr6ZKyzsPDLGiIiIiIyaWzhJSIiItKDypiFxRALyxgjJrxEREREelBZs7BwyrYnY5cGIiIiIjJpTHiJiIiIyKQx4SUiIiIik8aEl4iIiIhMGhNeIiIiIjJpTHiJiIiIyKQx4SUiIiIik8aEl4iIiIhMGhNeIiIiIjJpTHiJiIiIyKRxaWEiIiINCkskAMLQYWhNCKDof2FbSoAkGTaeinp4/4l0gwkvERGRBgvP1zV0CESkI+zSQEREREQmjS28RERE/yOTyZCYmGjoMJ5KXl4eunXrBgA4ePAgbGxsDBzR05PJZIYOgYwcE14iIqL/kSTJJBJEBRsbG5O6HqKKYpcGIiIiIjJpTHiJiIiIyKQx4SUiIiIik8aEl4iIiIhMmsET3tWrV8PHxwcymQz+/v44evRomfUTEhLg7+8PmUyGBg0aYO3atSrb161bh+DgYDg6OsLR0RHdu3fHyZMn9XkJRERERFSFGTTh3bZtG6ZPn4633noLycnJCA4ORq9evZCenq6xflpaGnr37o3g4GAkJydjzpw5mDp1Knbu3Kmsc+TIEbz00ks4fPgwkpKS4OnpiZ49e+LGjRuVdVlEREREVIUYNOFdsWIFxo0bh/Hjx8PX1xcxMTHw8PDAmjVrNNZfu3YtPD09ERMTA19fX4wfPx7h4eF4//33lXU+//xzTJ48Ga1atULTpk2xbt06lJSU4ODBg5V1WURERERUhRgs4S0sLMSZM2fQs2dPlfKePXuWOul3UlKSWv3Q0FCcPn0aRUVFGvd58OABioqKULt2bd0ETkRERERGxWALT2RnZ0Mul8PFxUWl3MXFBZmZmRr3yczM1Fi/uLgY2dnZcHNzU9tn1qxZqFevHrp3715qLAUFBSgoKFA+z83N1eZSiIiIiKgKM/igNUmSVJ4LIdTKnlRfUzkALFu2DFu3bsWuXbvKXJZw8eLFcHBwUD48PDy0uQQiIiIiqsIMlvDWrVsX5ubmaq25t27dUmvFVXB1ddVY38LCAnXq1FEpf//99xEdHY39+/ejRYsWZcYye/Zs5OTkKB/Xrl2rwBURERERUVVksC4NVlZW8Pf3x4EDBzBw4EBl+YEDB/DCCy9o3CcwMBDfffedStn+/fsREBAAS0tLZdny5cvx7rvv4scff0RAQMATY7G2toa1tXUFr4SqGyEE8vPzDR3GU8nLy9P4szGTyWRlfjtERETVl8ESXgCIjIzEqFGjEBAQgMDAQHz66adIT0/HpEmTADxseb1x4wY2bdoEAJg0aRI+/vhjREZGYsKECUhKSsKGDRuwdetW5TGXLVuGd955B1988QW8vb2VLcJ2dnaws7Or/Iskk5Ofn4+goCBDh6Ez3bp1M3QIOpGYmAgbGxtDh0FERFWQQRPeYcOG4fbt21i4cCEyMjLg5+eHvXv3wsvLCwCQkZGhMievj48P9u7di4iICMTGxsLd3R2rVq3C4MGDlXVWr16NwsJCDBkyROVc8+bNw/z58yvluoiIiIio6jBowgsAkydPxuTJkzVu27hxo1pZSEgIzp49W+rxrly5oqPIiJ4sekAOrCyEocPQmhBAofzhz1bmgLH2BCgsljDnGwe9noNdWKomdmEhIm0YPOElMmZWFgLWRvpbJLN8cp2qT/8fNtiFpWpiFxYi0oaR/quuXiqjhamyW4DYOkNERESVhQmvEajsFqbKaAFi6wwZo4zBARAW5oYOQ3tCQJKXPPzR3Mxo+7BIxXK47Txt6DCIyAgx4SUiKidhYW6cCS8AYRJdWIiIKoYJrxGQyWRITEzU6zke7TZRGd0Nylr5joiIiEiXmPAaAUmSKuXrf1tbW72fg4iIiKiyGWxpYSIiIiKiysCEl4iIiIhMGhNeIiIiIjJpTHiJiIiIyKQx4SUiIiIik8aEl4iIiIhMGhNeIiIiIjJpTHiJiIiIyKRx4QkiojIIIZQ/S8VyA0ZCj97/R18XIqInYcJLRFQGxZLbAOC287QBI6FH5efnc3VIIio3JrxEWnq0Zamg2ICBkMr9Z4sfERGVhgkvkZYebfF765tahguEVOirxU8mkyl/zhgcAGFhrvNzUPlIxXJlK/ujr4sxEUKo/A3Rh7y8PI0/64tMJoMkSXo/D9HTYMJLRFSGR/+RCwtzJrxVhLEmWPn5+QgKCqq083Xr1k3v50hMTISNjY3ez0P0NJjwEmnp0Zal9wbchTV/iwymoPj/W9mNtcWPiIj0j/+qibT0aMuStQWY8FYRxtriR9WLTCZDYmKiXs/xaLeJyuhuwA+bZAz4r5qIiKiSSJJUKV//cwYLIlVceIKIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGQWtEROUkFcsNHULFCAFJXvLwR3MzwEhntDDa+09EBseEl4ionBSrfBERkXFhwkv0FAqLJQDC0GFoTQig8H+NZVbmRtvg97/7T0REVDYmvERPYc43DoYOgfSsMhYK0Le8vDzlErMHDx40iWVgudgBEWmDCS8RURkqa6GAymJjY2NS10NEVB5MeIm0xBa/qoktfkREVBomvERaYosfERGRceE8vERERERk0pjwEhEREZFJY8JLRERERCaNCS8RERERmTQmvERERERk0pjwEhEREZFJY8JLRERERCaNCS8RERERmTQmvERERERk0pjwEhEREZFJY8JLRERERCaNCS8RERERmTSDJ7yrV6+Gj48PZDIZ/P39cfTo0TLrJyQkwN/fHzKZDA0aNMDatWtVtv/+++8YPHgwvL29IUkSYmJi9Bg9EREREVV1Bk14t23bhunTp+Ott95CcnIygoOD0atXL6Snp2usn5aWht69eyM4OBjJycmYM2cOpk6dip07dyrrPHjwAA0aNMCSJUvg6upaWZdCRERERFWUQRPeFStWYNy4cRg/fjx8fX0RExMDDw8PrFmzRmP9tWvXwtPTEzExMfD19cX48eMRHh6O999/X1nnueeew/LlyzF8+HBYW1tX1qUQERERURVlsIS3sLAQZ86cQc+ePVXKe/bsicTERI37JCUlqdUPDQ3F6dOnUVRUVOFYCgoKkJubq/IgIiIiItNgsIQ3OzsbcrkcLi4uKuUuLi7IzMzUuE9mZqbG+sXFxcjOzq5wLIsXL4aDg4Py4eHhUeFjEREREVHVYvBBa5IkqTwXQqiVPam+pnJtzJ49Gzk5OcrHtWvXKnwsIiIiIqpaLAx14rp168Lc3FytNffWrVtqrbgKrq6uGutbWFigTp06FY7F2tqa/X2JiIiITJTBWnitrKzg7++PAwcOqJQfOHAAQUFBGvcJDAxUq79//34EBATA0tJSb7ESERERkfEyWAsvAERGRmLUqFEICAhAYGAgPv30U6Snp2PSpEkAHnY1uHHjBjZt2gQAmDRpEj7++GNERkZiwoQJSEpKwoYNG7B161blMQsLC3HhwgXlzzdu3EBKSgrs7OzQqFGjyr9IIi0JIZCfn6/Xc+Tl5Wn8WV9kMtlTdTsiIiJ6GgZNeIcNG4bbt29j4cKFyMjIgJ+fH/bu3QsvLy8AQEZGhsqcvD4+Pti7dy8iIiIQGxsLd3d3rFq1CoMHD1bWuXnzJlq3bq18/v777+P9999HSEgIjhw5UmnXRlRR+fn5pX7LoQ/dunXT+zkSExNhY2Oj9/MYI37AISLSP0koRn2RUm5uLhwcHJCTkwN7e3tDh0PVTF5eXqUmvJWBCW/p+HoTEVWMNvmaQVt4iUidTCYrdS5qXXm0VbEyWuNkMplej09ERFQWJrxEVYwkSZXSOmZra6v3c9CT8QMOEZH+MeElIjIgfsAhItI/gy88QURERESkT0x4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIiIiITBoTXiIiIiIyaRaGDqAqEkIAAHJzcw0cCRERERFposjTFHlbWZjwanDv3j0AgIeHh4EjISIiIqKy3Lt3Dw4ODmXWkUR50uJqpqSkBDdv3kTNmjUhSZKhw6k0ubm58PDwwLVr12Bvb2/ocEjP+HpXL3y9qxe+3tVLdX29hRC4d+8e3N3dYWZWdi9dtvBqYGZmhvr16xs6DIOxt7evVr8w1R1f7+qFr3f1wte7eqmOr/eTWnYVOGiNiIiIiEwaE14iIiIiMmlMeEnJ2toa8+bNg7W1taFDoUrA17t64etdvfD1rl74ej8ZB60RERERkUljCy8RERERmTQmvERERERk0pjwEhEREZFJY8JLRERERCaNCS+RkcvMzMTrr7+OBg0awNraGh4eHujXrx8OHjwIAPD29oYkSZAkCTY2NvD29sbQoUNx6NAhleNcuXJFWe/Rx8iRI9G5c2eN2xQPb29vA1x59TB27FgMGDAAAHDr1i288sor8PT0hLW1NVxdXREaGoqkpCRlfW9vb8TExKg8lyQJX375pdqxn332WUiShI0bNz5xf0mSYGtrCz8/P3zyySe6vkyqgGvXrmHcuHFwd3eHlZUVvLy8MG3aNNy+fVtZR/G7u2TJErX9e/fuDUmSMH/+/EqMmp5Em9dVkiRYW1ujXr166NevH3bt2qV2PEW948ePq5QXFBSgTp06kCQJR44c0fdlGRwT3mqosLDQ0CGQjly5cgX+/v44dOgQli1bht9++w379u1Dly5dMGXKFGW9hQsXIiMjAxcvXsSmTZtQq1YtdO/eHe+9957aMX/66SdkZGQoH7Gxsdi1a5fy+cmTJ9XqnTp1qtKuuTobPHgwzp07h88++wx//vkndu/ejc6dO+POnTtl7ufh4YH4+HiVsuPHjyMzMxM1atR44nkV759ff/0VAwYMwKRJk7Bt27anuhZ6OpcvX0ZAQAD+/PNPbN26FX/99RfWrl2LgwcPIjAwUOU9oen1v3nzJg4dOgQ3N7fKDp3KoM3rOmHCBGRkZOCvv/7Czp070axZMwwfPhwTJ05UO66m98DXX38NOzs7vV9TlSHI5IWEhIgpU6aIiIgIUadOHdGpUyfxwQcfCD8/P2Frayvq168vXn31VXHv3j0hhBAlJSWibt264quvvlIeo2XLlsLJyUn5PDExUVhYWCj3IcPo1auXqFevnvjvv//Utv37779CCCG8vLzEypUr1bbPnTtXmJmZiT/++EMIIURaWpoAIJKTk8s8Z3nrkW6MGTNGvPDCC+Lff/8VAMSRI0fKrP/46+3l5SVmzZolrK2tRXp6urJ8woQJ4vXXXxcODg4iPj6+zP0ff/80btxYDB8+/Gkui57S888/L+rXry8ePHigUp6RkSFsbW3FpEmThBAP//6/+uqrok6dOuLYsWPKeu+9957o16+faNmypZg3b15lhk5l0OZ1nTZtmtr+cXFxAoA4cOCAsgyAePvtt4W9vb3KcXv06CHeeecdAUAcPnxYL9dTlbCFt5r47LPPYGFhgV9++QWffPIJzMzMsGrVKpw/fx6fffYZDh06hDfffBPAw68/OnXqpPyK499//8WFCxdQVFSECxcuAACOHDkCf3//6vXpsIq5c+cO9u3bhylTpmhspatVq1aZ+0+bNg1CCHz77bd6ipB0yc7ODnZ2dvjmm29QUFCg1b4uLi4IDQ3FZ599BgB48OABtm3bhvDw8ArFIpPJUFRUVKF96enduXMHP/74IyZPngwbGxuVba6urnj55Zexbds2iP9Ns29lZYWXX35ZpYVv48aNFX79ST+0fV01GTNmDBwdHdW6Nvj7+8PHxwc7d+4E8LDbxM8//4xRo0bp/kKqKCa81USjRo2wbNkyNGnSBE2bNsX06dPRpUsX+Pj4oGvXrli0aBG2b9+urN+5c2dlwvvzzz+jZcuW6Nq1q7LsyJEj6Ny5c+VfCCn99ddfEEKgadOmFdq/du3acHZ2xpUrV1TKg4KClMmVnZ0dkpOTdRAtPS0LCwts3LgRn332GWrVqoUOHTpgzpw5+PXXX8u1f3h4ODZu3AghBL766is0bNgQrVq10iqG4uJibNy4Eb/99hu6detWgasgXbh06RKEEPD19dW43dfXF//++y+ysrKUZePGjcP27dtx//59/Pzzz8jJyUGfPn0qK2Qqh4q8ro8zMzPDM888o/Z3HQDCwsIQFxcHAIiPj0fv3r3h5OSkk9iNARPeaiIgIEDl+eHDh9GjRw/Uq1cPNWvWxOjRo3H79m3cv38fwMOE9/fff0d2djYSEhLQuXNndO7cGQkJCSguLkZiYiJCQkIMcSn0P4pP+ZIkPdUxHt9/27ZtSElJUT6aNWv2VHGS7gwePBg3b97E7t27ERoaiiNHjqBNmzYqg85K06dPH/z333/4+eefERcXp1XrXlRUFOzs7GBjY4MpU6bgjTfewCuvvPIUV0L69GjLrkKLFi3QuHFjfPXVV4iLi8OoUaNgaWlpqBCpAjS9rqXV0/R/YeTIkUhKSsLly5erZQs/E95q4tGvvK9evYrevXvDz88PO3fuxJkzZxAbGwsAyq8p/fz8UKdOHSQkJCgT3pCQECQkJODUqVPIy8tDx44dDXIt9FDjxo0hSRJSU1MrtP/t27eRlZUFHx8flXIPDw80atRI+eDa7FWLTCZDjx49MHfuXCQmJmLs2LGYN2/eE/ezsLDAqFGjMG/ePJw4cQIvv/xyuc/5xhtvICUlBVevXsV///2HZcuWwcyM/z4MpVGjRpAkSdnF7HF//PEHnJyc1Lo1hYeHIzY2Fl999VW1S3aMQUVf10fJ5XJcunRJ7e86ANSpUwd9+/bFuHHjkJ+fj169eukqdKPAv1jV0OnTp1FcXIwPPvgA7du3xzPPPIObN2+q1FH04/32229x/vx5BAcHo3nz5igqKsLatWvRpk0b1KxZ00BXQMDDLgmhoaGIjY1Vtsw/6u7du2Xu/+GHH8LMzEw55RUZp2bNmml8/TUJDw9HQkICXnjhBTg6Opb7HHXr1kWjRo3g7u7+VN8okG7UqVMHPXr0wOrVq5GXl6eyLTMzE59//jnGjh2rtt+IESPw22+/wc/Pj9/cVEEVfV0f9dlnn+Hff//F4MGDNW4PDw/HkSNHMHr0aJibm+sqdKPAhLcaatiwIYqLi/HRRx/h8uXL2Lx5M9auXatWr3Pnzvjiiy/QokUL2NvbK5Pgzz//nP13q4jVq1dDLpejbdu22LlzJy5duoTU1FSsWrUKgYGBynr37t1DZmamcqDCxIkT8e677+K9995Do0aNDHgFVF63b99G165dsWXLFvz6669IS0vDjh07sGzZMrzwwgvlOoavry+ys7PVpici4/Pxxx+joKAAoaGh+Pnnn3Ht2jXs27cPPXr0wDPPPIO5c+eq7ePo6IiMjAzlHN1U9Wjzuj548ACZmZm4fv06Tpw4gaioKEyaNAmvvvoqunTpovH4zz//PLKysrBw4cLKuqQqgwlvNdSqVSusWLECS5cuhZ+fHz7//HMsXrxYrV6XLl0gl8tVktuQkBDI5XL2360ifHx8cPbsWXTp0gUzZsyAn58fevTogYMHD2LNmjXKenPnzoWbmxsaNWqEUaNGIScnBwcPHkRUVJQBoydt2NnZoV27dli5ciU6deoEPz8/vPPOO5gwYQI+/vjjch+nTp06aiPAyfg0btwYp06dQoMGDTB06FB4eXmhV69eeOaZZ/DLL7+UOoNOrVq1yjX3MhmGNq/runXr4ObmhoYNG2LgwIG4cOECtm3bhtWrV5d6fEmSULdu3Sf2AzZFkihrfgsiIiIyCvPmzcOKFSuwf/9+lW94yLjxddUNJrxEREQmIj4+Hjk5OZg6dSoHFpoQvq5PjwkvEREREZk0fkwgIiIiIpPGhJeIiIiITBoTXiIiIiIyaUx4iYiIiMikMeElIiIiIpPGhJeIyEh5e3sjJibG0GHoxMaNG1GrVi2dH/fKlSuQJAkpKSk6PzYRGQ8mvERU7WRmZuL1119HgwYNYG1tDQ8PD/Tr16/KLrlaWjJ46tQpTJw4Ua/nPnLkCCRJgqOjI/Lz81W2nTx5EpIkQZIkrY5pSok6ERkHJrxEVK1cuXIF/v7+OHToEJYtW4bffvsN+/btQ5cuXTBlypQKH7eoqEircl1wcnKCra2t3o7/qJo1a+Lrr79WKYuLi4Onp2elnJ+I6Gkw4SWiamXy5MmQJAknT57EkCFD8Mwzz+DZZ59FZGQkjh8/rqyXnp6OF154AXZ2drC3t8fQoUPxzz//KLfPnz8frVq1QlxcnLKlWAgBSZKwdu1avPDCC6hRowbeffddAMB3330Hf39/yGQyNGjQAAsWLEBxcbHyeHfv3sXEiRPh4uICmUwGPz8/7NmzB0eOHEFYWBhycnKUranz588HoN5SWt6YN2/eDG9vbzg4OGD48OG4d+/eE+/bmDFjEBcXp3yel5eHL7/8EmPGjFGrm5iYiE6dOsHGxgYeHh6YOnUq7t+/DwDo3Lkzrl69ioiICI2twz/++CN8fX1hZ2eH559/HhkZGcptJSUlWLhwIerXrw9ra2u0atUK+/btU9n/5MmTaN26NWQyGQICApCcnPzEayOiakAQEVUTt2/fFpIkiejo6DLrlZSUiNatW4uOHTuK06dPi+PHj4s2bdqIkJAQZZ158+aJGjVqiNDQUHH27Flx7tw5UVJSIgAIZ2dnsWHDBvH333+LK1euiH379gl7e3uxceNG8ffff4v9+/cLb29vMX/+fCGEEHK5XLRv3148++yzYv/+/eLvv/8W3333ndi7d68oKCgQMTExwt7eXmRkZIiMjAxx7949IYQQXl5eYuXKlVrFbGdnJwYNGiR+++038fPPPwtXV1cxZ86cUu/F4cOHBQBx8eJFYW1tLa5evSqEEGLz5s2iZcuW4uuvvxaP/iv59ddfhZ2dnVi5cqX4888/xS+//CJat24txo4dq3wN6tevLxYuXKi8HiGEiI+PF5aWlqJ79+7i1KlT4syZM8LX11eMGDFCeewVK1YIe3t7sXXrVvHHH3+IN998U1haWoo///xTCCHEf//9J5ycnMSwYcPE+fPnxXfffScaNGggAIjk5OQyX3MiMm1MeImo2jhx4oQAIHbt2lVmvf379wtzc3ORnp6uLPv9998FAHHy5EkhxMPk0dLSUty6dUtlXwBi+vTpKmXBwcFqSfbmzZuFm5ubEEKIH3/8UZiZmYmLFy9qjCc+Pl44ODiolT+a8JY3ZltbW5Gbm6us88Ybb4h27dqVei8UCe+///4rBgwYIBYsWCCEEKJLly7iww8/VEt4R40aJSZOnKhyjKNHjwozMzORl5enFvej1whA/PXXX8qy2NhY4eLionzu7u4u3nvvPZX9nnvuOTF58mQhhBCffPKJqF27trh//75y+5o1a5jwEpFglwYiqjaEEADwxEFWqamp8PDwgIeHh7KsWbNmqFWrFlJTU5VlXl5ecHJyUts/ICBA5fmZM2ewcOFC2NnZKR8TJkxARkYGHjx4gJSUFNSvXx/PPPNMha+tvDF7e3ujZs2ayudubm64detWuc4RHh6OjRs34vLly0hKSsLLL7+sVufMmTPYuHGjyrWGhoaipKQEaWlpZR7f1tYWDRs21Bhbbm4ubt68iQ4dOqjs06FDB+X1paamomXLlir9mgMDA8t1bURk2iwMHQARUWVp3LgxJElCamoqBgwYUGo98b++uE8qr1Gjhsb9Hy8vKSnBggULMGjQILW6MpkMNjY25byC0pU3ZktLS5XtkiShpKSkXOfo3bs3XnnlFYwbNw79+vVDnTp11OqUlJTglVdewdSpU9W2PWmAm6bYFB9SHi171KPX93hdIiIFtvASUbVRu3ZthIaGIjY2VjmI6lF3794F8LBlND09HdeuXVNuu3DhAnJycuDr66v1edu0aYOLFy+iUaNGag8zMzO0aNEC169fx59//qlxfysrK8jl8jLPoeuYNTE3N8eoUaNw5MgRhIeHa6zTpk0b/P777xqv1crKqtzX8zh7e3u4u7vj2LFjKuWJiYnK62vWrBnOnTuHvLw85fZHByISUfXFhJeIqpXVq1dDLpejbdu22LlzJy5duoTU1FSsWrVK+fV39+7d0aJFC7z88ss4e/YsTp48idGjRyMkJEStu0J5zJ07F5s2bcL8+fPx+++/IzU1Fdu2bcPbb78NAAgJCUGnTp0wePBgHDhwAGlpafjhhx+UMxB4e3vjv//+w8GDB5GdnY0HDx6onUPXMZdm0aJFyMrKQmhoqMbtUVFRSEpKwpQpU5CSkoJLly5h9+7deP3115V1vL298fPPP+PGjRvIzs4u97nfeOMNLF26FNu2bcPFixcxa9YspKSkYNq0aQCAESNGwMzMDOPGjcOFCxewd+9evP/++093wURkEpjwElG14uPjg7Nnz6JLly6YMWMG/Pz80KNHDxw8eBBr1qwB8PBr82+++QaOjo7o1KkTunfvjgYNGmDbtm0VOmdoaCj27NmDAwcO4LnnnkP79u2xYsUKeHl5Kevs3LkTzz33HF566SU0a9YMb775prIVNCgoCJMmTcKwYcPg5OSEZcuWqZ1D1zGXxsrKCnXr1i21H3SLFi2QkJCAS5cuITg4GK1bt8Y777wDNzc3ZZ2FCxfiypUraNiwocY+0KWZOnUqZsyYgRkzZqB58+bYt28fdu/ejcaNGwMA7Ozs8N133+HChQto3br1/7VrxzQAxDAQBPO4jcCoPyDSrWYYuFudfGbm7O7bwUDC93t6AgAgzMILAECa4AUAIE3wAgCQJngBAEgTvAAApAleAADSBC8AAGmCFwCANMELAECa4AUAIE3wAgCQJngBAEi7NIEMoLxE7kIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_rmsd_spatial = correlation.rmse_spatial_correlation_distribution(variable = 'tas', \n",
    "                                                                     obs_data = tas_obs_validate, raw = tas_cm_future,\n",
    "                                                                     CDFT = tas_val_debiased_CDFT,\n",
    "                                                                     ISIMIP = tas_val_debiased_ISIMIP,\n",
    "                                                                     QM = tas_val_debiased_QM, \n",
    "                                                                     QDM = tas_val_debiased_QDM\n",
    "                                                                     )\n",
    "tas_rmsd_spatial_plot = correlation.rmse_spatial_correlation_boxplot(variable = 'tas', dataset = tas_rmsd_spatial)\n",
    "tas_rmsd_spatial_plot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d5d0288",
   "metadata": {},
   "source": [
    "### Multivariate structure\n",
    "\n",
    "**Conditional joint threshold exceedance of threshold metrics**\n",
    "\n",
    "To investigate the compounding of different threshold metrics, ibicus so far enables the user to calculate their conditional joint threshold exceedance:\n",
    "\n",
    "$$ p (\\text{Metric1} | \\text{Metric2}) = \\frac {p(\\text{Metric1} , \\text{Metric2})}{p(\\text{Metric2})} $$\n",
    "\n",
    "In this example, we calculate the probability of warm days, given that these days are also wet (see definitions of threshold metrics above). The plot shows the mean conditional exceedance probability at each location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8a5a0f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "warm_wet = multivariate.calculate_conditional_joint_threshold_exceedance(warm_days, wet_days,\n",
    "                                                             obs = [tas_obs_validate, pr_obs_validate], \n",
    "                                                             raw = [tas_cm_validate, pr_cm_validate],\n",
    "                                                            ISIMIP = [tas_val_debiased_ISIMIP, pr_val_debiased_ISIMIP],\n",
    "                                                                         QM = [tas_val_debiased_QM, pr_val_debiased_QM],\n",
    "                                                                         CDFT = [tas_val_debiased_CDFT, pr_val_debiased_CDFT])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "11cb9b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wQC3S4CLQYIBalFaDh99hDU6fBgPUIg0uAg0GqEUafKCK7xD1/vvvxymnnBLXXHNNXHbZZYPut2rVqnjxxRejra1tRAcIQAr2u73hiMaowAMPPBAREXPnzu33+iOPPBJf/OIXIyJi+/btsXXr1r7P7d69O2655Zb49a9/HYcddlicdNJJ8cMf/jAuuuiiER16UWgwQA3S4ELQYIAalEaDe8cZJg1OnwYD1CANLgQNBqhBGlym4gVR8+fPj/nz5w+5z69//eu4/vrr48knn4zPfvazh3xwANSuJEkOus/y5cv7fXzrrbfGrbfeWqUjqn0aDMBwaHD6NBiA4dDg9GkwAMOhwenTYACGI+8NrnhB1MHs3bs3FixYEH/3d38XJ5100kH337VrV+zatavv466urrQPCYAM7k7B6Ku0wRE6DFB1GlwXNBgghzL4l7GMPg0GyCENrgsaDJBDGlymIe0Bv/GNb0RjY2PceOONw9p/2bJl0dLS0rdNnTo17UMCYJSfF0s2Km1whA4DVJ8G1wMNBsijtBqsw3mmwQB5pMH1QIMB8kiDD5TqgqiXXnop/umf/imWL18epWGuGluyZEl0dnb2bdu2bUvzkACgLhxKgyN0GABGSoMBIBsaDADZ0GAAakWqC6KeffbZeOedd2LatGnR2NgYjY2NsWXLlvjKV74Sxx577IDvaWpqiubm5n4bACnrvUXiSDdy61AaHKHDAFWnwYWnwQA5lVaDdTi3NBggpzS48DQYIKc0uExjmoMtWLAg/uiP/qjfaxdeeGEsWLAgrrnmmjR/KQAqkcbdDYvTvkLSYICc0uDC02CAnErrLv86nFsaDJBTGlx4GgyQUxpcpuIFUe+99168+eabfR+3t7fHyy+/HJMmTYpp06bFEUcc0W//sWPHRmtra8yYMWPkRwsAdUyDASAbGgwA2dBgAMiGBgNQBBUviFq/fn2cf/75fR8vWrQoIiIWLlwYy5cvT+3AAEiT21MUgQYD1CINLgINBqhF/mlsEWgwQC3S4CLQYIBapMEHqnhB1Ny5cyNJkmHv//bbb1f6SwCQtjSe91qg58XWKg0GqEEaXAgaDFCD0mhw7zhkRoMBapAGF4IGA9QgDS7TkPUBAAAAAAAAAAAApKXiO0QBUINKkcLdKVI5EgCoLxoMANlIo8G94wAAw6fBAJANDS6TuwVRvbdf7Nq5M+MjoWgqubVn1ko1chu6ZE93dQYuVeHmddU6/ymfq95rX/r/v6bxzNja+P+SkdFhoF5pMFnTYKgdyZ491Rq5CkPm/2ch+W5w7zgUmQYD9UqDyZoGA8nevekPuvej9MeMiOj+fWpDde18LyI0eDTkbkHUzv+N3tT/c1LGRwKQnZ07d0ZLS0vWh0Ed0mGg3mkwWdFgoN5pMFnRYKDeaTBZ0WCg3mlw9eVuQVRbW1ts27YtJk6ceNC75HR1dcXUqVNj27Zt0dzcPEpHWH1FnFcR5xRRzHmZU7aSJImdO3dGW1tbugOXSik8rqc4q4EZ3HA7XEtfV5Uo4ryKOKeIYs7LnLKlwWRNg4s3ryLOKaKY8zKnbOW6wb3jUGgaXLx5FXFOEcWcVxHnFFE789JgsqbBxZtXEecUUcx5FXFOEbUzLw0ePblbENXQ0BBHH310Re9pbm7O9f/Qh6qI8yrinCKKOS9zyk51VgJ7XA/DU2mHa+XrqlJFnFcR5xRRzHmZU3Y0mCxpcI8izquIc4oo5rzMKTv5bXDvOBSZBvco4ryKOKeIYs6riHOKqI15aTBZ0uAeRZxXEecUUcx5FXFOEbUxLw0eHQ1ZHwAAAAAAAAAAAEBacneHKACqwON6ACAbGgwA2fCoAADIhgYDQDY0uExNL4hqamqKO+64I5qamrI+lFQVcV5FnFNEMedlTgXlL2NJWVG/roo4ryLOKaKY8zKngtJgUlbUr6sizquIc4oo5rzMqaD8IJiUFfXrqojzKuKcIoo5ryLOKaK48xo2DSZlRf2aKuK8ijiniGLOq4hziijuvIZNg8uUkiRJsj4IAKqjq6srWlpa4ne/eiWaJ04c2Vg7d8bhx8+Mzs7O3D93FwCypsEAkI00GxyhwwAwXBoMANnQ4MHV9B2iABiu0v9uIx0DAKiMBgNANtJocO84AMDwaTAAZEODD2RBFEAdKJVKURrh7Q1H+n4AqEcaDADZSKPBveMAAMOnwQCQDQ0u15D1AQAAAAAAAAAAAKTFHaIA6kGp1LONdAwAoDIaDADZSKPBveMAAMOnwQCQDQ0uY0EUQF1I45mxxYkfAIweDQaAbKTR4N5xAIDh02AAyIYGHyj3j8y7//77Y/r06TF+/PiYNWtWPPvss0Puv27dupg1a1aMHz8+jjvuuHjwwQdH6UiHZ9myZXHGGWfExIkT46ijjopLL700Xn/99SHfs3bt2r7nPe6//fKXvxylox7a0qVLy46ttbV1yPfk/TxFRBx77LED/r5fd911A+6fx/P0zDPPxMUXXxxtbW1RKpVi1apV/T6fJEksXbo02tra4rDDDou5c+fGq6++etBxV6xYEZ/61KeiqakpPvWpT8Vjjz1WpRmUG2pO3d3dcdttt8XMmTNjwoQJ0dbWFldffXX85je/GXLM5cuXD3juPvzwwyrPBvJNg/N5bd+fBvfI43kqYoMjdBhGiwbn89q+Pw3ukcfzpMEaDCOhwfm8tu9Pg3vk8TxpsAbDSGhwPq/t+9PgHnk8TxqswQwu1wuifvCDH8RNN90Ut99+e2zcuDHOOeecmD9/fmzdunXA/dvb2+Oiiy6Kc845JzZu3Bhf/epX48Ybb4wVK1aM8pEPbt26dXHdddfFCy+8EKtXr449e/bEvHnz4v333z/oe19//fXYvn1733bCCSeMwhEPz0knndTv2F555ZVB962F8xQR8bOf/azfnFavXh0REX/xF38x5PvydJ7ef//9OOWUU+Lee+8d8PP/8A//EN/85jfj3nvvjZ/97GfR2toaf/zHfxw7d+4cdMznn38+rrjiiliwYEH8/Oc/jwULFsTll18eL774YrWm0c9Qc/rggw9iw4YN8bWvfS02bNgQK1eujDfeeCMuueSSg47b3Nzc77xt3749xo8fX40pZKS07zaJh7oVaDUwB6fB/eXp2n4gDd4nT+epiA2O0OFDo8FURoP7y9O1/UAavE+ezpMGa/A+KTRYh+uKBveXp2v7gTR4nzydJw3W4H00mMpocH95urYfSIP3ydN50mAN3keDyyQ59ulPfzq59tpr+7124oknJosXLx5w/1tvvTU58cQT+732pS99KZk9e3bVjnGk3nnnnSQiknXr1g26z5o1a5KISH7729+O3oFV4I477khOOeWUYe9fi+cpSZLkb//2b5Pjjz8+2bt374Cfz/t5iojkscce6/t47969SWtra3LnnXf2vfbhhx8mLS0tyYMPPjjoOJdffnnyJ3/yJ/1eu/DCC5Mrr7wy9WM+mAPnNJD/+q//SiIi2bJly6D7PPLII0lLS0u6B5cTnZ2dSUQknW+/niT/7zcj2jrffr1nrM7OrKfFKNDgHnm/tmtwj7yfpyI2OEl0+GA0mEOlwT3yfm3X4B55P08arMEjbbAO1xcN7pH3a7sG98j7edJgDdZgKqHBPfJ+bdfgHnk/TxqswRrcX27vELV79+546aWXYt68ef1enzdvXjz33HMDvuf5558v2//CCy+M9evXR3d3d9WOdSQ6OzsjImLSpEkH3ffUU0+NKVOmxAUXXBBr1qyp9qFVZPPmzdHW1hbTp0+PK6+8Mt56661B963F87R79+549NFH4y//8i+jVBp6RWSez9P+2tvbo6Ojo9+5aGpqivPOO2/Qr7GIwc/fUO/JUmdnZ5RKpTj88MOH3O+9996LY445Jo4++uj43Oc+Fxs3bhydA4Qc0uByeb62a/A+eT5P+6uXBkfoMFRKg8vl+dquwfvk+TztT4PLaTD00OByeb62a/A+eT5P+9PgchoMPTS4XJ6v7Rq8T57P0/40uJwG15fcLojasWNHfPTRRzF58uR+r0+ePDk6OjoGfE9HR8eA++/Zsyd27NhRtWM9VEmSxKJFi+Lss8+Ok08+edD9pkyZEt/+9rdjxYoVsXLlypgxY0ZccMEF8cwzz4zi0Q7uzDPPjO9973vx5JNPxne+853o6OiIs846K959990B96+18xQRsWrVqvjd734XX/ziFwfdJ+/n6UC9X0eVfI31vq/S92Tlww8/jMWLF8dVV10Vzc3Ng+534oknxvLly+Pxxx+Pf/3Xf43x48fHZz7zmdi8efMoHm21lVLaqAcavE/er+0a3CPv5+lA9dDgCB3eR4MZPg3eJ+/Xdg3ukffzdCAN7k+DdZh9NHifvF/bNbhH3s/TgTS4Pw3WYPbR4H3yfm3X4B55P08H0uD+NLj+GtyY9QEczIGrL5MkGXJF5kD7D/R6Hlx//fXxi1/8In7yk58Mud+MGTNixowZfR/PmTMntm3bFnfddVece+651T7Mg5o/f37ff8+cOTPmzJkTxx9/fHz3u9+NRYsWDfieWjpPEREPPfRQzJ8/P9ra2gbdJ+/naTCVfo0d6ntGW3d3d1x55ZWxd+/euP/++4fcd/bs2TF79uy+jz/zmc/EaaedFt/61rfinnvuqfahjo6+Z76OcAzqigbn/9quwT3yfp4GU9QGR+hwPxrMIdDg/F/bNbhH3s/TYDS4hwZXMA51Q4Pzf23X4B55P0+D0eAeGlzBONQNDc7/tV2De+T9PA1Gg3tocAXjFERu7xD1iU98IsaMGVO2yvCdd94pW43Yq7W1dcD9Gxsb44gjjqjasR6KG264IR5//PFYs2ZNHH300RW/f/bs2bldqThhwoSYOXPmoMdXS+cpImLLli3x9NNPx1/91V9V/N48n6fW1taIiIq+xnrfV+l7Rlt3d3dcfvnl0d7eHqtXrx5yJfBAGhoa4owzzsjtuYNq0+Ch5fnarsH75Pk8FbnBEToMI6HBQ8vztV2D98n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      "text/plain": [
       "<Figure size 3000x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "warm_wet_vis = multivariate.plot_conditional_probability_spatial(bias_df=warm_wet, plot_title =\"Conditional probability of warm days (>20°C) given wet days (>1mm)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96c11f88",
   "metadata": {},
   "source": [
    "**Correlation Structure**\n",
    "\n",
    "This section is currently in an experimental phase and will be expanded upon in the next releases. Currently, it is possible to calculate and plot the correlation between two variables at each grid point, as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a313940c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K/1G7dm0nFwkAyhePx9wDYS+U3JXIXgBwFLlbbpC7ABAGyN1yhXPNAFDGyN1yhTYvAJQxrvGWK+QuAIQBcjcoR9dqzJgx+vrrr7V48eKg80ycOFH79+/3P7Zs2eLkIgFAuWJZlrEHwl8ouSuRvQDgJHK3/CB3AaDskbvlC+eaAaBskbvlC21eAChbXOMtX8hdACh75G5wPqcK33DDDXrjjTf04Ycf6pRTTgk6X0xMjGJiYpxaDAAAyoVQc1ciewEAKC1yFwAAd3GuGQAA99DmBQDAPeQuACDcGe9gbNu2brjhBr366qtauXKl6tWrZ/olAAChsgwNwW9F5jD+kYDcBYAwYyJ7yd2wRe4CQJghdyMe2QsAYYTcjXjkLgCEEa7xRjxyFwDCDG3eoIx3ML7++uu1aNEivf7660pKStKOHTskSSkpKYqLizP9cgCA47A8lixP6YfgN1EDziB3ASC8mMhecjd8kbsAEF7I3chH9gJA+CB3Ix+5CwDhg2u8kY/cBYDwQps3OOPdpufMmaP9+/erU6dOqlGjhv+xZMkS0y8FAEC5R+4CAOAechcAAHeRvQAAuIfcBQDAPeQuAOBkYXwEY9u2TZcEAJSU5TEzBH+EDuMfCchdAAgzJrKX3A1b5C4AhBlyN+KRvQAQRsjdiEfuAkAY4RpvxCN3ASDM0OYNyngHYwBAGPFYRx4m6gAAgBMzkb3kLgAAoSF3AQBwD7kLAIB7uMYLAIC7aPMGFZndpgEAAAAAAAAAAAAAAAAAAACUCCMYA0AEsyyPLAND8JuoAQBAeWAie8ldAABCQ+4CAOAechcAAPdwjRcAAHfR5g2ODsYAEMm4fQ4AAO7i9jkAALiH3AUAwD3kLgAA7uEaLwAA7qLNG1TYdjBOPfSHkjwJZb0YJ+TNyzJeMyrjL+M1vX9uM14zP7Wq8ZqSlF61kQNVzX+B4w/uNF4zatMPxmtKUvbGjcZrpqRWNF6z2WktjdfcV7mh8ZqSdNCTYrSex5thtB5QEn+kJys+P9lYvbioPGO1juaxbOM1D2RGGa+ZFJtjvObBbGcO3aJ9+cZr1kw1XzM333yex0Wb354kKSvX/K8To73ml9XnNf85pcQ788vMSimxRusdzsg2Wg8orqTsPUrKMteezI9x5rsXbZtv81ba/ZPxmr7NPxqvWaVSdeM1JSkrtabxmofiKxuvGX9ot/GaMT99YbymJB381nxbOqZKqvGaDWr+bLymKpr/7CUpP9bsObmYjENG6wHFlW95lW95jdWLkvn2niTFeswfn6fmbDdeMzrroPGavkRn3tPUqBjjNfMcuKwSL/PvaZXtXxmvKUmeTPPnMXOTzedZhXjz568Px5g9J1wgw2PufJwkHcw5YLQeUBJb//QqNsNc9ibEGSsVWNfs6SZJ0q+/HTZes379eOM19+535rxodJT58xPNW9UyXjMry/z6pyQ7c/7e50typK5pTnz2Hbo70W9A+nJNorFaOVkH9Pm7xsoBJWLLkm2wT8yST8y3TSXpzKrmjg0KJOfvNV7zlMbNjNc8qfz8nfGSFX3mMyI5/Q/jNSVpS5L5z3/zL+bPX3vzzZ9HqXPqGcZrOiE9Pb2sF6HcCdsOxgCA0rM8HlkeA7fPMVADAIDywET2krsAAISG3AUAwD3kLgAA7uEaLwAA7qLNGxwdjAEgklnWkYeJOgAA4MRMZC+5CwBAaMhdAADcQ+4CAOAervECAOAu2rxBRWa3aQAAAAAAAAAAAAAAAAAAAAAlwgjGABDJPJZkYgh+T2T+ygYAAONMZC+5CwBAaMhdAADcQ+4CAOAervECAOAu2rxB0cEYACIZt88BAMBd3D4HAAD3kLsAALiH3AUAwD1c4wUAwF20eYMy8JMnAAAAAAAAAAAAAAAAAAAAAJGCEYwBIIJZHo8sA7fPMVEDAIDywET2krsAAISG3AUAwD3kLgAA7uEaLwAA7qLNG1xkrhUA4AjLY+5RTLNnz1a9evUUGxurVq1a6aOPPgo679KlS9W9e3dVqVJFycnJatu2rd59993SrDkAAGWD3AUAwD1llLsS2QsAKIfIXQAA3MM1XgAA3EWbNyg6GAMAjFuyZIluuukm3XnnnVq/fr06dOignj17avPmzUXO/+GHH6p79+5atmyZvvjiC3Xu3Fl9+vTR+vXrXV5yAABOPuQuAADuInsBAHAPuQsAgHvIXQAA3HUyZK/PscoAgLJnWZLHMlOnGB577DENHz5cI0aMkCTNmDFD7777rubMmaNp06YVmn/GjBkB/3/ggQf0+uuv680331TLli1LvNgAALjORPaSuwAAhKYMclciewEA5RS5CwCAe7jGCwCAu2jzBsUIxgAQwSzLY+wRquzsbH3xxRfq0aNHwPQePXrok08+CalGfn6+Dhw4oNTU1GKtLwAAZY3cBQDAPW7nrkT2AgDKL3IXAAD3cI0XAAB30eYNjhGMAQAhS09PD/h/TEyMYmJiAqbt3r1beXl5qlatWsD0atWqaceOHSG9zqOPPqqMjAxdeumlpVtgAABOYuQuAADuCSV3JbIXAAATyF0AANzFuWYAANwTaW1eRjAGgEjmscw9JNWuXVspKSn+R1HD8Rewjhn637btQtOKsnjxYk2ePFlLlixR1apVS7f+AAC4jdwFAMA9ZZS7EtkLACiHyF0AANzDNV4AANxFmzcoRjAGgEhmeY48TNSRtGXLFiUnJ/snF/ULm8qVK8vr9Rb6Nc2uXbsK/ermWEuWLNHw4cP18ssvq1u3bqVfbgAA3GYie8ldAABC43LuSmQvAKAcI3cBAHAP13gBAHAXbd6gGMEYABCy5OTkgEdRIRgdHa1WrVpp+fLlAdOXL1+udu3aBa29ePFiDRkyRIsWLdKFF15ofNkBADjZkLsAALgnlNyVyF4AAEwgdwEAcBfnmgEAcE+ktXkZwRgAIpllHXmYqFMM48aN01VXXaXWrVurbdu2evrpp7V582Zde+21kqSJEyfqjz/+0HPPPSfpSPgNHjxYjz/+uM455xz/r3Pi4uKUkpJS+uUHAMAtJrKX3AUAIDRlkLsS2QsAKKfIXQAA3MM1XgAA3EWbNyg6GANAJPN4jjxM1CmGgQMHas+ePZo6daq2b9+uZs2aadmyZapbt64kafv27dq8ebN//qeeekq5ubm6/vrrdf311/unX3311Vq4cGHplx8AALeYyF5yFwCA0JRB7kpkLwCgnCJ3AQBwD9d4AQBwF23eoOhgDABwxOjRozV69Ogi/3ZsqK1cudL5BQIAIIKRuwAAuIvsBQDAPeQuAADuIXcBAHBXuGcvHYwBIJJZniMPE3UAAMCJmchechcAgNCQuwAAuIfcBQDAPVzjBQDAXbR5g6KDMQBEMo915GGiDgAAODET2UvuAgAQGnIXAAD3kLsAALiHa7wAALiLNm9QYdvBOG73JsUfjjNWzzqcYazW0fL37DJeM3vnn8ZrHti9z3jNxAa1jdeUpJSsQ+aLeqOMl7QO7jNeM3vLFuM1JWnfL+br5mVvMF4z8deNxmtWbJhmvKYkVTz1TKP10jMc2O6BYkqNP6yEeHP7S5+Vb6zW0WyZPyhLiMo2X9N32HjNWE+m8ZqSlGubz8lc22u85qHcWOM1821nfkV4MCfaeM2k6CzjNb1WnvGa2flONTHMtQ0kKSrf/LoDxRGfvkMJefHG6iXmbjRW62jeA3uN18zdssl4zf2/mW/zeKO/N15TkhIa1zdeMyY+wXjNvD27jdfc8+0vxmtKUuY+8+2p/N+2G6+ZdMoO4zWjE83tR44WW6u62YKHzR/HAMVRadf3Ss4w932pbKxSINtj/ljak21+H2nt2Gq8ZnyM+faeJOVVrmG8Zn6U+WX15JrfT3q2/ma8piTlpacbr2nl/2S8ZkK1asZrxicmG68pSYmV6xitF3fQmethQHGcUTdDCYnmzw+adjDb/Dm8Tmebr5kSa/57HevLMV5Tkg7lxBivmZlrflvafcD85+SUzGzzx4ipSeav31iWbbxmVo4znXQ8fzN3jJh5KEH/MlYNKJlK6RuUnG/u/OD5sc60JeyD5vfne5LSjNfc+vO3xmtW2OPMe5rY9iLjNe2TZATWA4nm2/uS1Oin18wXdeCa5J5TOxivueXn74zXlKTajZs6UhfuCdsOxgAAAyzL0O1zIvNXNgAAGGcie8ldAABCQ+4CAOAechcAAPdwjRcAAHfR5g2KDsYAEMksy0yARWgIAgBgnInsJXcBAAgNuQsAgHvIXQAA3MM1XgAA3EWbN6iTY1xzAAAAAAAAAAAAAAAAAAAAAK5gBGMAiGQez5GHiToAAODETGQvuQsAQGjIXQAA3EPuAgDgHq7xAgDgLtq8QdHBGAAiGbfPAQDAXdw+BwAA95C7AAC4h9wFAMA9XOMFAMBdtHmDcrzb9LRp02RZlm666SanXwoAgHKP3AUAwD3kLgAA7iF3AQBwF9kLAIB7yF0AQLhydATjNWvW6Omnn9YZZ5zh5MsAAIKxPEceJuog7JG7ABAGTGQvuXtSIHcBIAyQu+UGuQsAYYDcLVfIXgAoY1zjLVfIXQAIA7R5g3JsrQ4ePKgrrrhCzzzzjCpWrOjUywAAjsfySB4DjwgNwUhC7gJAmDCRveRu2CN3ASBMkLvlArkLAGGC3C03yF4ACANc4y03yF0ACBO0eYNybK2uv/56XXjhherWrZtTLwEAAP4/chcAAPeQuwAAuIfcBQDAXWQvAADuIXcBAOHO50TRl156SevWrdOaNWtOOG9WVpaysrL8/09PT3dikQCgfLKsIw8TdRC2ipO7EtkLAI4ykb3kblgjdwEgjJC7EY/cBYAwQu6WC2QvAIQJrvGWC+QuAIQR2rxBGR/BeMuWLbrxxhv1wgsvKDY29oTzT5s2TSkpKf5H7dq1TS8SAJRflsfcA2GpuLkrkb0A4ChyN6KRuwAQZsjdiEbuAkCYIXcjHtkLAGGEa7wRj9wFgDBD7gZlfK2++OIL7dq1S61atZL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\n",
      "text/plain": [
       "<Figure size 3600x500 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_pr_corr = multivariate.calculate_and_spatialplot_multivariate_correlation(variables = ['tas', 'pr'], obs = [tas_obs_validate, pr_obs_validate], \n",
    "                         raw = [tas_cm_validate, pr_cm_validate], CDFT = [tas_val_debiased_CDFT, pr_val_debiased_CDFT],\n",
    "                                                               ISIMIP = [tas_val_debiased_ISIMIP, pr_val_debiased_ISIMIP],\n",
    "                                                               QM = [tas_val_debiased_QM, pr_val_debiased_QM],\n",
    "                                                                QDM = [tas_val_debiased_QDM, pr_val_debiased_QDM])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3dd8a16",
   "metadata": {},
   "source": [
    "It is also possible to plot the two variables as well as their correlation with each other at a single location:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bf21a550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_pr_obs, tas_pr_isimip = multivariate.create_multivariate_dataframes(variables = ['tas', 'pr'], datasets_obs = [tas_obs_validate, pr_obs_validate], \n",
    "                                                            datasets_bc = [tas_val_debiased_ISIMIP, pr_val_debiased_ISIMIP], gridpoint = (1,1))\n",
    "\n",
    "tas_pr_corr_11 = multivariate.plot_correlation_single_location(variables = ['tas', 'pr'], obs_df = tas_pr_obs, bc_df = tas_pr_isimip)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50fdef6f",
   "metadata": {},
   "source": [
    "At a single location, it is also possible to use bootstrapping to obtain a distribution of estimates of the correlation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bd689b27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KstestResult(statistic=0.916, pvalue=2.1994512990591277e-225, statistic_location=-0.13579449379210856, statistic_sign=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_pr_corr_bs = multivariate.plot_bootstrap_correlation_replicates(obs_df = tas_pr_obs, bc_df = tas_pr_isimip,\n",
    "                                                                    bc_name = 'ISIMIP', size = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c3cda1d",
   "metadata": {},
   "source": [
    "## Part 3: Evaluation of trend preservation\n",
    "\n",
    "\n",
    "This last section investigates whether the chosen bias adjustment method modify the trend in the climate model between the validation and the future period. The following steps are conducted in the calculation:\n",
    "- First, the trend in the chosen statistic (for example the mean) between the validation period and the future period in the *raw* climate model is calculated. \n",
    "- Then, the same calculation is performed for the debiased models of interest\n",
    "- Finally the percentage difference between these two trends is calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e275d972",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/evaluate/trend.py:171: RuntimeWarning: divide by zero encountered in divide\n",
      "  trend_bias = 100 * (bc_trend - raw_trend) / raw_trend\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/evaluate/trend.py:171: RuntimeWarning: invalid value encountered in divide\n",
      "  trend_bias = 100 * (bc_trend - raw_trend) / raw_trend\n",
      "WARNING:root:QM: Division by zero encountered in bias of ThresholdMetric(threshold_value=295, threshold_type='higher', threshold_scope='overall', threshold_locality='global', name='Mean warm days (K)', variable='tas') calculation, not showing results for this debiaser.\n",
      "WARNING:root:QM: Division by zero encountered in bias of ThresholdMetric(threshold_value=273.13, threshold_type='lower', threshold_scope='overall', threshold_locality='global', name='Mean frost days (tas<0°C)', variable='tas') calculation, not showing results for this debiaser.\n",
      "WARNING:root:QDM: Division by zero encountered in bias of ThresholdMetric(threshold_value=295, threshold_type='higher', threshold_scope='overall', threshold_locality='global', name='Mean warm days (K)', variable='tas') calculation, not showing results for this debiaser.\n",
      "WARNING:root:QDM: Division by zero encountered in bias of ThresholdMetric(threshold_value=273.13, threshold_type='lower', threshold_scope='overall', threshold_locality='global', name='Mean frost days (tas<0°C)', variable='tas') calculation, not showing results for this debiaser.\n",
      "WARNING:root:ISIMIP: Division by zero encountered in bias of ThresholdMetric(threshold_value=295, threshold_type='higher', threshold_scope='overall', threshold_locality='global', name='Mean warm days (K)', variable='tas') calculation, not showing results for this debiaser.\n",
      "WARNING:root:ISIMIP: Division by zero encountered in bias of ThresholdMetric(threshold_value=273.13, threshold_type='lower', threshold_scope='overall', threshold_locality='global', name='Mean frost days (tas<0°C)', variable='tas') calculation, not showing results for this debiaser.\n",
      "WARNING:root:CDFT: Division by zero encountered in bias of ThresholdMetric(threshold_value=295, threshold_type='higher', threshold_scope='overall', threshold_locality='global', name='Mean warm days (K)', variable='tas') calculation, not showing results for this debiaser.\n",
      "WARNING:root:CDFT: Division by zero encountered in bias of ThresholdMetric(threshold_value=273.13, threshold_type='lower', threshold_scope='overall', threshold_locality='global', name='Mean frost days (tas<0°C)', variable='tas') calculation, not showing results for this debiaser.\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/ibicus/utils/_utils.py:464: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for index, value in row.iteritems():\n"
     ]
    },
    {
     "data": {
      "image/png": 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yifO///6TJP30009KTk5Wx44dbebToEEDlS9f3tpEcdeuXbrvvvvk5eVlLfPggw/K3d3d+nnbtm0ymUxq3ry5Ne7U1FS1atVKp06d0v79++3GnpPKlSvLze1/p+5t27apTJkyqlmzpnVZFotFLVu21K+//mrTLCarfSa9SdvBgweVkJBgbSKXrl27dg7FVbduXZUvX976uVq1aqpQoYK1idT27dsVEhIis9lsjbN48eJq0KBBts2oPD09FRoaah2/bds2VaxYUe3atbP2ZBkbG6tLly6pZcuWDu8327ZtU/Xq1VW2bFlrGTc3NzVr1ixTLLk9zho2bKjy5cvrs88+k3S1R8CvvvpKDz/8sCTJy8tLb7/9th566CHFx8crNjZWK1as0Lfffisp+2Mio507d8rT01PNmjWzDitatKiaN29uUy4iIkJ9+vTRmTNnFBcXp9WrV+uTTz5xeDnS1SZv5cuXV/369W2GP/zww7py5YpNE7nAwECbY6FcuXKSZN3HruXIOTNdlSpVbDokKFu2rM28HTk2C5OM5/u4uDhdvnxZrVq1ynTOkJTj+a59+/by8PjfI+pt2rSRdHU/OXjwoE6cOJFp3g0bNlTx4sUzzdve75Aj+8JPP/2klJSUPJ9HgMKMjiCALBQrVkyBgYE2w5o2bapLly5p8eLFeuKJJ1S6dOlM033yySeaMWOG/v33X/n4+KhatWoym83W8SaTSUuWLNG8efP01Vdfac2aNfL09NQDDzygV155xZpwOMLb29vms5ubm92mPefOncsy5tzKqieljOt4rTNnzmjChAnauHGjTCaTKlasaP3RzS7eCxcuKC0tTYsWLdKiRYsyjS9SpEiuY7x2e2WUnlikJ4EZ+fn5WZOr8+fPq1SpUjbjPTw8bJqxnDt3ToZh2CSXGcXHx6t69ep247fn2hjPnTunU6dOqWbNmlmWP3XqlDWBtLfPpG+Da9evTJkyeYpLkkqXLm1Nns+dO6f169dn+WzXtcvMqHnz5po4caKSkpK0bds2hYSEKCQkRG+++ab+/vtvbd68WTVr1lTZsmV18uRJh/abc+fO6fDhw9lus4wX+bk9zkwmkzp27KgPP/xQ48aN0+bNm3XhwgVr0zxJ+v777zVlyhQdPHhQxYoVU0BAgIoVKybJsff+nD9/Xj4+PjbJs5T5u9qzZ48mTpyoPXv2yGw2q0qVKtbE1pHlpC8ru+NC+t/NESnrbSUp07NWGeV0znR03o4cm4VJxm2a3hlF//79sywbHx9vd1633367zef08/yFCxes8544caL1mTp7887qu06Xm30hr+cRoDAjaQJyoXr16lq5cqWOHTuWKQHZuXOnRo0apZ49e+qpp56y3mV94403bDpbKFu2rF555RVNmDBBe/fu1RdffKFFixapZMmSWf6o5ZcSJUpk+Y6MuLg4FS9e3NqNen57/vnn9ddff2np0qWqV6+evLy8dPnyZa1cuTLbaYoVKyaTyaQ+ffqoffv2mcbbS4DyIj2pOH36tCpXrmwz7tSpU9YaKh8fn0wdJxiGYVObU6JECRUtWlTvvvtulsuqWLFifoauEiVK6J577tGbb76Z5fgKFSo4NJ/0i8uEhASb4ekXXTnJeMGU7tSpUwoKCrLG2aRJE/Xt2zdTuYx3ya/VvHlzpaSkaNeuXdqxY4dGjRqlmjVrqnjx4oqNjdXmzZv10EMPSXJ8vylRooSCg4P14osvZrnMjLUVedGpUyfNnz9fO3bs0Keffqp69epZ96EjR45o8ODBuv/++7VgwQLdfffdkq4+q/b99987NH9fX1+dPXtWFovFpiYl43d18eJF9evXTwEBAfr000+tNZTfffedNmzY4PC6lCxZMsvnLU+dOmWNJa8cPWc6wpFjMzfSn5VKZ6928Xqnu+222yRdfQbtnnvuyTTeXiIjZT5G07dDqVKlrPN+8cUXFRwcnGnajDXyOcnNvpCQkGDTfbuj5xGgMKN5HpALcXFxcnd3t2nmlXFcWlqahg4dav3xt1gs1uY+aWlpiouLU5MmTfTLL7/IZDKpevXqGj58uPz9/XXixIkCjb1BgwY6evSo9u3bZx2WnJysIUOG6MMPPyyw5e7atUtt2rRRo0aNrBejmzdvlvS/u8TXNqEpXry4atSooYMHDyowMND6V7VqVUVFReX7C3fr1KkjLy+vTC9V3blzp44fP26tNWrcuLE2b95sUxPx/fff2zQlCg4O1qVLl2QYhk3s+/fv15w5c3LsDSvdtbUI2QkODta///6r0qVL2yxv27ZtWrx4scPNk+655x7dcccd+uKLL2yGpzcby0lcXJy1Rk6SfvnlF/3zzz9q1KiRNc4DBw6oevXq1hhr1aqlZcuW6auvvpKUeT+Qrt6hrlGjht5//32dOnVKwcHBcnd3V8OGDbVmzRodPHhQLVu2lOT4fhMcHKxDhw6pUqVKNuU++eQTrVy58rqbdN17770KDAzUZ599pk2bNtnUMv3666+6cuWKBgwYYE2YJFkTJkdqgBo3bqzU1FRt3LjROiw5OdmmqdXBgwd17tw5PfHEE6patap1f7r22MtpP2vYsKH++eefTEnMJ598Ik9PT9WuXTvHeLPjyDnTUY4cm44qXrx4pvPx7t27bT5ntd0cmS4rderUkaenp06ePGmzP3p6eioiIiLHF8J+8803Np83bNggk8mkRo0a6d5771Xp0qV17Ngxm3mXK1dOERERNj1G5sSRfSEoKEhmsznP5xGgMKOmCcjCxYsX9dNPP1k/p6Sk6Ouvv9a6devUvXv3LJsTpV88vPrqq+rSpYsuXLig6Oho7d27V9LVO441atSQ2WzWiy++qCFDhsjPz09bt27VH3/8oSeeeKJA1yksLEzLly/XwIED9dxzz6lUqVKKiYlRUlKSTbfp+a127dpat26datasqXLlyikuLk4LFiyQyWSyXuCUKFFC0tVnTSpXrqw6depoxIgR6t+/v0aOHKmHH35YFotFS5Ys0c8//2ztuSq/+Pj4qH///oqKipKnp6fuv/9+HTt2TDNnzlSVKlUUFhYmSRo8eLA2btyop556Sv369dPZs2cVGRkpT09P67yaN2+uhg0batCgQRo0aJAqV66sX375RbNnz1bTpk3tNkXLKP0O8VdffaVmzZplqgFLFxYWpujoaPXt21fPPPOM7rjjDm3dulWLFi1Sz549bWKzx2Qy6fnnn9fIkSP18ssvq23btvrpp5/0/vvvOzR9Wlqa+vfvr2eeeUZnz55VRESE/P39rc/yDBo0SD169NCAAQP02GOPqUiRIlqxYoU2btyoWbNmScp6P5Cu9lY2Z84cVapUyfosS0hIiKZNm6bbb7/dppmdI/tNnz59tHbtWvXp00dPPvmkfH19tX79en344YfWrqSvV+fOnTVlyhS5ubnZPM9Rs2ZNeXh4aPr06XryySeVnJys1atXa9OmTZIcq5lo3LixmjZtqpdfflkJCQkqX7683n33XZ05c8ZaA16pUiUVL15c8+fPl4eHhzw8PLRhwwZr99Dpx15O+1lYWJjee+89Pfvssxo6dKjuuusuffPNN1q1apWeffZZ6/R54cg509EXqzpybDqqZcuWWrBggebPn6+6detq06ZNmXqizGq7OTJdVnx9fdWvXz/NnDlTFy9eVEhIiE6ePKmZM2fKZDLZPIealV9++UXPP/+8OnXqpH379mnWrFl69NFHrTf3hg8frvHjx8vd3V0tW7bUhQsXNHfuXJ08eTLbJqpZcXRfGDRokN566y15e3urUaNG+u6770iacFMgaQKy8Pvvv6t79+7Wz0WKFNHdd9+t4cOH66mnnspympCQEI0fP15Lly7VF198IT8/P4WEhCgqKkqDBw/Wrl271Lx5cy1ZskQRERGaPHmyLly4oHvuuUevvvqq9cK8oBQvXlzR0dF64403NHnyZKWmpqpOnTpavny5zR3v/DZt2jRNmjRJkyZNknS1RmPixIn65JNPtHPnTmtsffv21YoVK7Rp0yZt2bJFTZs21dtvv62oqCgNHTpUnp6eqlmzppYuXVogL91NT2Kjo6O1cuVK+fj4qG3btho2bJi1Wdc999yj6OhoTZs2TcOHD1fp0qU1atQoTZs2zTofNzc3LVy4UDNnztSCBQuUkJCgsmXLqk+fPho8eLDD8YSEhKhJkyaKiIjQtm3brJ1nXKto0aKKiYlRRESEpk+frv/++0/ly5fXyJEj9eSTT+ZqG3To0EFubm6aO3eu1q5dK39/f7366qs5dt0sXb3QvPvuu/XCCy8oNTVVLVu21NixY63PEVWrVk0xMTGKjIzUiy++KMMw5O/vrzlz5lgfGs9qP/Dy8rImTRmbF4WEhEi6mlBlfHeaI/tN2bJl9cEHHygiIkKvvPKKrly5onvuuUeTJ09W165dc7XNsvPQQw9p2rRpatGihU0TqIoVKyoiIkJRUVEaOHCgSpYsqbp162r58uXq1auXdu7cqYCAgBznHxUVpTfffFOzZs3SlStX9NBDD+nRRx/V119/LelqAjp37ly98cYbeu6551SsWDFVr15d0dHRevrpp7Vz5061atUqx/3M29tby5cvV0REhGbNmqWLFy/q3nvvzZdt5eg50xGOHJuOGjBggM6cOaMlS5YoJSVFLVq00OTJk21u1mS13RyZLjvDhg1TmTJl9N5772nx4sUqWbKkGjdurBEjRlhvJmSnd+/eOnnypJ599ln5+vrqmWee0YABA6zju3XrpmLFimnx4sVasWKFihYtqnr16unNN9/MstVEdhzdFwYMGKCiRYvqnXfe0TvvvKOgoCCNGjVKr7zyisPLAgojk+Ho06AAAAAoNAICAvTss89qyJAhrg4FuOnxTBMAAAAA2EHSBAAAAAB20DwPAAAAAOygpgkAAAAA7CBpAgCgELlVG4DcqusN4MZA0gQgXxw7dkwBAQE2f7Vq1VKTJk00cOBAxcXF5Wm+AQEBmj17tiRpx44dCggIyPeX26Jw+/PPP/XII4+oVq1aeuihh1wdjkN69eqVp/ef7dq1y6a76FvBiRMnNGDAAP3zzz+uDkWSNGnSJEVGRkqSVq9erYCAgCxfMLts2TIFBAToueeeU0pKig4cOKBWrVrpwoULzg4ZgBPwniYA+WrgwIFq0aKFJOnKlSs6ceKE3nnnHYWHh2vWrFl64IEHcjW/FStWqFy5cgUQKW4UUVFR+ueffxQVFWV9eWthN2HChDxNt3LlSh04cCCfoynctm7dqk2bNmncuHGuDkXbt2/Xl19+qQ0bNtgt984772jq1Knq2LGjXn/9dbm7u6tKlSpq1aqVJk+erNdff91JEQNwFpImAPnq7rvvzvTy2Xbt2unxxx/X2LFj1ahRIxUvXtzh+RXEi2xxYzl79qz8/f2tyfiNoEqVKq4OAXkwdepUPfHEEypatGi2Zd59911NmTJFXbp00WuvvSY3t/812unfv79atGihJ554QjVr1nRGyACchOZ5AAqcl5eXhgwZonPnzunzzz+3Dt+7d6+effZZNWrUSDVr1tR9992n1157TUlJSdYyGZvnZbR//34FBARoxYoVNsNPnjyp6tWra82aNVnGMnv2bLVt21YbN25Uhw4dFBgYqE6dOikuLk4//fSTunXrptq1a6tDhw7atm2bzbR//vmnBgwYoHr16qlevXoaPHiwjh49alPG0XWKiYnR2LFjFRwcrKCgIA0dOlSnT5/OdhumN3/8/PPPNXToUAUFBalhw4YaO3asEhMTbcquXLlS7du3V61atdSiRQvNnj1bqampmcqEhYWpbt26ql27tjp16qT169dbx69evVo1atTQypUr1bRpUzVr1kz79+/PNr7ly5erbdu2CgwM1H333adXXnlFFy9etFnna7/H2bNnKyAgwPp59OjR6t27tyZMmKAGDRrokUceUUBAgH788UfFxsYqICBAq1evliTFxsbqqaeeUsOGDVWrVi21atVKs2fPVlpamnV+iYmJmjp1qpo1a6a6desqLCxM33zzTa631bXOnDmjiRMnqmXLlqpVq5aCg4M1ePBgmyZc1zbPCwgIUFRUlLp06aL69etr7ty5meY7evRorVmzRv/884/Nul65ckVvvPGGmjdvrlq1aqljx44235UktWrVSlFRUZo6dapCQkIUFBSkkSNHKjExUQsXLlSzZs1Uv359DRkyRGfPnrWZLjIyUlOnTlVwcLCCg4P1wgsv2JSRpJ07d6pnz56qU6eOgoODNWrUKJ05c8Y6Prv9xWKxaOHCherQoYNq166tunXrqkePHtZja/Xq1RozZowk6f7779fo0aOtcaX/P+MyMjaVmz17tlq3bq2oqCiFhITogQcesMadl+9106ZN2rdvnzp06JBtmXfffVeTJ0/W448/rsmTJ9skTJJ0++23q1GjRlq4cKHdZQG48VDTBMApQkND5ebmpt27d6tbt26Kj49XeHi46tatq2nTpsnLy0ubNm3SO++8Iz8/Pz3zzDN251e1alXVqVNHa9euVffu3a3D165dK7PZrDZt2mQ77YkTJzR16lQNHz5c3t7emjRpkoYOHSpPT08NHDhQvr6+mjFjhoYPH65NmzbJbDbr0KFD6tGjh+69915NmzZNFotF8+bN02OPPaa1a9eqdOnSuVqnyMhItW7dWjNmzNDRo0c1depUeXh4aMaMGXbXe8KECerSpYvmzp2rX375RZGRkSpVqpRGjhwpSVqwYIEiIyPVs2dPjRkzRn/88Ydmz56tf//9V1OmTJEkxcTE6LXXXtOzzz6rUaNG6dy5c1q0aJFeeOEF1a1bV3feeackyWKxaP78+Xrttdd05syZbGtPPvvsM73++usaNWqUAgICdPDgQb3++utKSkrStGnT7K7PtXbu3CmTyaTZs2crMTFRfn5+mjhxonXd7777bu3du1d9+vRR27ZtFRkZKcMwtHbtWkVFRemee+5Rx44dlZaWpn79+umvv/7S0KFDVblyZa1du1bPPvusli5dqpCQEIe21bUMw9CAAQN0/vx5jRw5UmXKlNEff/yhmTNnavz48VqyZEm26zZv3jw999xzCggIyLLJ6aBBg3TmzBn9/vvvioqK0t133y3DMDR48GDt3r3buh5fffWVhg8fruTkZHXu3Nk6/dKlS9WkSRNFRkZqz549mjFjhn777TeVLVtWkyZN0qFDh/TGG2/Iz8/Ppvnge++9p4oVK2rKlCk6c+aMIiIidPDgQa1cuVJubm6KjY1V37591ahRI7311ls6f/68Zs6cqSeeeEIfffSRzGazpKz3lzfeeEPvvfeenn/+eQUEBOjEiROaM2eOnnvuOW3atEktWrTQwIEDNW/ePEVFRdkk0Y44fvy4vvrqK82YMUNnz56Vr69vnr5XSfrkk09Ut25d3XHHHVmOX758uSZPnqxevXrp5ZdfznY+7dq108SJE5WYmKhixYrlan0AFF4kTQCcwsPDQz4+Pjp16pSkq7U21atX18yZM63N9Zo0aaJt27YpNjY2x6RJkrp06aLx48fr6NGjuuuuuyRJH3/8sdq1a2e3ec3ly5c1YcIENWvWTJL0119/KSIiQpMnT1bXrl0lXb0AHDp0qA4dOqTq1asrKipKZrNZy5Yts8bbuHFjPfDAA1q8eLFGjRqVq3Xy9/fX1KlTrZ9/+eUXffHFFzmuc/PmzTVq1Cjr8rds2aJNmzZp5MiR+u+//zRv3jx1797delHXtGlT+fj46OWXX1bfvn1VtWpVHT16VE8++aQGDx5snW+FChUUFham3bt3W5MmSXrmmWdybBa3Y8cOlS9fXuHh4XJzc1NwcLCKFi2aqbbCEampqZo4caIqVqxoHZa+LdObam7evFlNmjTR9OnTrXf6Q0NDtWnTJsXGxqpjx47avHmzdu/erblz5+r++++XJDVq1EiHDx/W9u3bVaNGDYe21bXi4+Pl7e2tUaNGqUGDBpKkkJAQHTt2TB988IHddatdu7b69++f7fi7775bpUqVkpeXl3Vdt2zZou+//16RkZHWTjDuu+8+Xb58WW+++aY6dOggD4+rP+XFihVTZGSkPDw81KRJE61Zs0bx8fFauXKlSpQooebNm2v79u3avXu3zXJNJpOWLl2qEiVKSJJKlSqlwYMHa/PmzWrRooUiIiJUqVIlLViwQO7u7pKkOnXqqH379lq1apXCw8Ot87p2f4mPj9fw4cNtat3MZrOGDBmiffv2KSgoSHfffbckqXr16qpQoYLdbXit1NRUjRo1Sk2aNJEkh4+BrGzfvl3t27fPclxMTIyWLFkik8lkU8OWlcDAQKWkpGjnzp1q3rx5rtYHQOFF0gTAqUwmk6SrFzJNmzZVSkqKDh06pL///lv79u3TmTNn5OPj49C82rdvr6lTp1prEH755Rf99ddfevXVV3Octl69etb/+/n5SbJ9fio9hvSesLZv366QkBCZzWZrM5/ixYurQYMG2rp1a67X6dpntcqVK6fLly/nGHdW06X3OhYXF6fLly+rVatWNk2RWrVqJenqBXjVqlWtzZ7+++8//f333/r777+tzaVSUlJs5u/v72/9v2EYslgsNuM9PDzUqFEjrVixQmFhYXrwwQfVokULdezY0fpd54bZbLZeRGenc+fO6ty5s65cuaIjR47o8OHD+u2332SxWKzx79y5U56enmrZsqV1OpPJpPfff1/S1cTLkW11rbJly+rdd9+VdLWW4/Dhw/rrr7+0e/fuTNvuWhm3paO2bdsmk8mk5s2bZ4rzk08+0f79+1W9enVJV5Oy9ARKksqUKaPixYtbkyHp6n79559/2iyjZcuWNmVatWolT09P7dy5UyEhIfr555/11FNPyTAMawx33XWXKleurC1bttgkTdeuY0REhKSrTRoPHz6sQ4cOWZtI5rS9HJVxmY4eA9e6fPmyEhISsk3alixZoqFDhyo5OVnz589X48aN1a1btyzLli9fXpKy7HEPwI2LpAmAUyQlJen8+fPWZklpaWmaMWOGYmJidOnSJd1xxx2qXbu2ihQp4vA8ixcvrrZt2+qTTz7Rs88+qzVr1qhixYrWGoCcpr1WejOjrJw7d07r16/P9CyJdPXOfG7Xydvb2+azm5ubQ++psTfduXPnJCnb2oz4+HhJ0pEjRzR+/Hht375dHh4euvfee63Noq6NIWNvdWvWrLE+f5Lu66+/1kMPPaS0tDS99957ioqK0syZM1W+fHmNHDky2zv32SldunSOyVZSUpImTZqktWvXKjU1VRUqVFBQUJA8PDxstoWPj0+mZ07SObqtsvLJJ59oxowZ+vfff+Xj46Nq1arZ3XfSpSfnuXHu3DkZhmGT5F8bZ3rSlNU+fe3+kpXbb7/d5rObm5t8fHx04cIFXbhwQWlpaVq0aJEWLVqUadpr9+1rezfcs2ePJk6cqD179shsNqtKlSrWpCK/3suUcbvm9XtNvzmSXQ31c889p0GDBiklJUXff/+9Jk+erHr16qly5cqZyqZv84zP9AG48ZE0AXCKHTt2yGKxqGHDhpKkhQsXatmyZXrllVfUpk0b653u9OZxjurSpYvWrFmjX375RRs2bMjTu3EcUaJECTVp0kR9+/bNNC797n5+rVNe3XbbbZKkN998U/fcc0+m8X5+fkpLS1P//v3l6empDz/8UDVq1JCHh4cOHDigTz75xO78W7ZsqY8++shmWPoFd4cOHdShQwf9999/+uGHH6zPSDVo0EBly5aVpEy1VJcuXcrTek6ePFkbNmzQW2+9pSZNmlgvdBs3bmwtU6JECZ07d05paWk2idMff/yh1NRUh7ZVVnbu3KlRo0apZ8+eeuqpp6w3Ad544w3t2rUrT+tjT4kSJVS0aFFr7da1MjZjzKv0RCOdxWLR2bNnVapUKRUrVkwmk0l9+vTJMgG2l5RdvHhR/fr1U0BAgD799FNVrlxZbm5u+u6773Ls0js9jowc2V/y+r36+vpKUrbvWHr44YclSZ6enpo+fbrCwsI0bNgwffTRR5kSx/R5pM8TwM2B3vMAFLjU1FTNmzdPfn5+at26taSrL/GsUqWKunbtak0uTp48qT///NOmB7ScNGzYUPfcc4+mT5+us2fP2jwYn5+Cg4N14MABVa9eXYGBgQoMDFStWrW0bNkyffXVV5Lyb53yqk6dOvL09NTJkyetMQYGBsrT01MRERE6duyYzp49q0OHDqlr1642zbk2b94sSXbj9PX1tZlvYGCgvLy8NGzYMD377LOSrl7kt2vXToMGDZLFYrHe2S9evLhOnDhhM79rn61x1K5du6y9paUnTL/++qvOnDljjb9BgwZKSUnRd999Z53OMAyNHTtW8+bNc2hbZSUuLk5paWkaOnSoNWGyWCzWJprX+z1fWzMWHBysS5cuyTAMmzj379+vOXPm5NgjnCO+//57JScnWz9//fXXSk1NVePGjVW8eHHVqFFDBw8etFl+1apVFRUVZfdF0wcPHtS5c+f0xBNPqGrVqtZ1u3Zfy6o2MK/7S16/Vy8vL5UpU0b//vtvjsuoXLmyXnjhBf355582zyWmS59HxmcDAdz4qGkCkK+OHDmin376SdLVZxbSH5D/7bffNGfOHOud6dq1a2vu3LlauHCh6tatq8OHD2vBggVKTk526NmejLp06aKIiAiFhoZm2/PV9Ro0aJB69OihAQMG6LHHHlORIkW0YsUKbdy4UbNmzZKUv+uUF76+vurXr59mzpypixcvKiQkRCdPntTMmTNlMplUrVo1lShRQuXLl1dMTIzKlSun2267TT/88IPeeecdScpTnI0aNdKECRP0+uuvq1mzZrpw4YK1J7tq1apJklq0aKHPPvtMtWvXVqVKlbRmzRodPnw4T+tZu3Ztff7553r//fdVuXJl7d27V/PmzZPJZLLG36JFCwUFBWnMmDF67rnnVLFiRa1bt05//vmnxo0b59C2ym7ZkvTqq6+qS5cuunDhgqKjo7V3715JV2tDcvMesmvddtttOn36tL777jtVr15dzZs3V8OGDTVo0CANGjRIlStX1i+//KLZs2eradOm1qah1+PEiRMaOHCgnnjiCf3777+aMWOGmjZtqpCQEEnSiBEj1L9/f40cOVIPP/ywLBaLlixZop9//lkDBw7Mdr6VKlVS8eLFNX/+fHl4eMjDw0MbNmyw1lamf1fptUNfffWVmjVrpsqVK6tly5ZasGCB5s+fr7p162rTpk2ZXgGQlbx+r9LVzkQcTeR79uypb7/9Vu+//76aNGmiBx980Dpu165d8vb2dqiZMIAbB0kTgHw1b948zZs3T9LV5x3Kli2rBg0aaOLEiTYXLAMGDNDZs2f17rvvas6cObrjjjvUqVMnmUwmLViwQOfPn1fJkiUdWmZ6D19hYWEFsk6SVK1aNcXExCgyMlIvvviiDMOQv7+/5syZY+2dLT/XKa+GDRumMmXK6L333tPixYtVsmRJNW7cWCNGjLDWfs2dO1eTJ0/W6NGj5eXlpSpVqmjevHmaMmWKdu7cmesmjj169FBKSoo++OADvffeezKbzWrcuLFeeOEFeXp6SpLGjBmj1NRUTZ8+XR4eHnrooYc0cuRIu103Z2f06NFKSUnRW2+9peTkZFWoUEEDBw7UgQMH9M0338hiscjd3V2LFi1SRESEZs+erUuXLqlatWpavHixgoKCHN5W1woJCdH48eO1dOlSffHFF/Lz81NISIiioqI0ePBg7dq167p6TAsLC9N3332nwYMHa+jQoerfv78WLlyomTNnasGCBUpISFDZsmXVp08fm94Pr0f79u112223adiwYSpatKgeeeQRDR8+3Dq+adOmevvttxUVFWXtmr9mzZpaunSp3ZdPlyhRQnPnztUbb7yh5557TsWKFVP16tUVHR2tp59+Wjt37lSrVq0UEhKiJk2aKCIiQtu2bdPChQs1YMAAnTlzRkuWLFFKSopatGihyZMn203S0uXle5WkNm3aaN26dYqPj8/0nFdWpk6dqo4dO+rll19WzZo1rc9qpfc66MhzbgBuHCYjv57EBAAXWbRokRYvXqzvv/9eXl5erg4HuGG0atVKwcHBuX6f1s3IMAx16tRJbdq0yXNCeuzYMT344IP66KOPVKNGjXyOEIAr8UwTgBvWmjVr9PrrrysqKkpPPPEECROAPDOZTHr++ef1/vvv57nnu8WLF6tt27YkTMBNiKQJwA1r7969eu+99/TAAw/o6aefdnU4AG5wzZo10/33368FCxbketoDBw5o06ZNGjduXAFEBsDVaJ4HAAAAAHZQ0wQAAAAAdpA0AQAAAIAdJE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBAAAAgB0kTQAAAABgB0kTAAAAANhB0gQAAAAAdpA0AQAAAIAdJE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBAAAAgB0kTQAAAABgB0kTAAAAANhB0gQAAAAAdni4OgBnS0tLU3x8vIoVKyaTyeTqcAAAAAC4iGEYSkxM1O233y43t+zrk265pCk+Pl7Nmzd3dRgAAAAAConvvvtO5cqVy3b8LZc0FStWTNLVDVO8eHEXRwM4X2Jiou68805J0vHjx63HBHCr4BgAOA4A6epxUL58eVWuXDnHY+CWS5rSm+QVL16cpAm3JJPJpLS0NElXjwN+KHGr4RgAOA4A6epxYBiG9f/20BEEAAAAANhB0gQAAAAAdpA0AQAAAIAdt9wzTY4wDEOpqamyWCyuDgUFzNPTU+7u7q4OAwAAAIUYSdM1kpOT9e+//+rSpUuuDgVOYDKZVKFCBToFAQAAQLZImjJIS0vToUOH5O7urjvvvFNeXl68APcmZhiGTp06pWPHjqlq1arUOAEAACBLJE0ZJCcnKy0tTXfddZeKFi3q6nDgBGXKlNHff/+tlJQUkiYAAABkiY4gsuDmxma5VVCTCAAAgJyQHQAAAACAHSRNN5n4+PgbshOLv//+29UhAAAAAFkiacqFQ4cOadSoUWrWrJmCgoL0wAMP6M0331RiYqKrQ5MknT59Wm3atNGZM2ckSfPnz1e/fv0KZFmtWrVSQECAvv/++0zjvvzySwUEBGj06NEOzSsmJkbjxo2zmffq1avzLdZevXpp9uzZ+TY/AAAA3FpImhy0e/duPfLIIypfvrw+/vhjxcXFadGiRfr555/15JNPFop3OiUlJdnUMj3zzDNavHhxgS3P19dXa9asyTR81apVuerCOz3JAwAAAAojkiYHjR8/Xp07d9bQoUNVqlQpSVKlSpUUGRmp0qVL6+jRo5Kkf/75R8OGDVPjxo0VGhqqkSNHKj4+XpK0Y8cONW/eXCNHjlSDBg20cOFCjR49WkOHDlW7du3UqFEjHTlyRKdPn9bzzz+v0NBQNW3aVOPHj9fFixetsWzZskVdu3ZVUFCQWrVqpejoaFksFnXo0EGS1KFDB61fv16zZ89Wr169rNNt3LhRYWFhqlevntq0aaNly5YpLS1NkjR69GiNHz9ezzzzjIKCgnT//ffr3XfftbtNOnbsqI0bN+q///6zDouPj9dPP/2k++67z6bsZ599po4dO6p+/foKCwvTDz/8IElas2aNFixYoJ07d6pBgwbW8r/99pt69OihevXqqX379vrxxx+t4/bt26enn35awcHBatasmV555RWbGFauXKn7779fQUFBGjVqlC5fvpzT1wsAAABki6TJAUeOHNH+/futSUlGfn5+mjt3ru655x6lpKToySeflLu7u7788kt9/vnnkq7W+KSmpkqSTpw4oXvvvVfbtm3T448/Lkn6/vvvNXPmTH355ZeqUKGCBg0aJDc3N23YsEHr1q1TfHy8xo8fL+lqE8FnnnlGPXr0UGxsrGbNmqUZM2Zo69at+vTTTyVJn376qR566CGbOLdv365hw4apX79++vHHHzVjxgwtXbrUJjFavXq1evXqpdjYWD399NOaNm2aTp48me12qV69uipVqqT169dbh3388cdq166dzGazddh3332nCRMmaPz48frxxx81ZMgQDRkyRPv379cjjzyiAQMGqEGDBtq5c6d1mh9++EFvvPGGfvzxRwUFBVmb7509e1ZPPPGEqlSpos2bN2vVqlU6dOiQXnzxRUnStm3b9Oqrr+q1115TbGys6tSpoz179uT0FQMAAADZImlyQHrzMT8/P7vldu7cqaNHj2rixIkqUaKEbrvtNk2cOFF79+7Vr7/+ai3XtWtXeXp6Wpuw1a1bV/7+/rrtttv066+/6rffftOECRNUvHhx+fr6atSoUfrss8909uxZffbZZ6pZs6a6du0qDw8P1apVS++9955q1qxpN7bVq1fr/vvv10MPPSQPDw/VrFlT/fv31wcffGAtExISotDQUHl4eKhLly6yWCw6cuSI3fmGhYXZNNFbtWqVunTpYlMmOjpajz32mBo2bCh3d3e1bNlSrVq1sln2tbp37667775bHh4eatu2rbUm7+uvv5anp6eef/55mc1mlSlTRuPGjdM333yjU6dO6ZNPPtGDDz6oxo0by8PDQ48//rhq1Khhdx0AAAAAe3i5rQPKlCkjSTp16pTuueeeTONPnz4tPz8/JSQkyNfX1+Z5nuLFi8vHx0f//POPNem6/fbbbabP+PnYsWOyWCxq3ry5TRkvLy8dPXpU8fHxuvPOO23GVatWTZLs9pqXkJCg6tWr2wyrUKGC/vnnn0zrKUmenp6SZG2+l52OHTtq+vTpOnTokBISElSkSBEFBgbalPnnn3/0448/6v3337cOs1gsatSoUbbz9fHxsYkl/ZmxhIQE3XnnnTYvoq1QoYJ1OSdPnsyUQN5111121wEAAACwh6TJAeXLl5e/v7/Wr1+vhg0b2oxLSEhQy5YtNXXqVJUvX15nz57VxYsXrYnTf//9p7Nnz6pMmTIyDENS5heqZvxcrlw5mc1m7dixw5oYJCcn6+jRo6pYsaLuuOMOfffddzbTr1q1SqVLl1aVKlXsrsO1tUZHjx61SZTyolSpUmrRooU+/vhjxcfHq2vXrpnKlCtXTp07d1b//v2tw44fP27ThM9R5cuX1/Hjx2WxWKzbJ329ypQpo3LlyllrpdKdOHFCVatWzfWyAAAACgPDMJSUlJRjGSnzdWZWzGazQ+XwPyRNDho3bpz69eun0qVLKzw8XD4+Ptq7d6/Gjx+vmjVrqk2bNjKZTKpSpYomTJigV155RZL0yiuv6O6771a9evW0a9euHJdTu3ZtVaxYUdOmTdOwYcPk7u6uadOm6euvv9bGjRvVvn17zZs3Tx9//LE6duyoP/74Q9OmTVNkZKSKFCkiSTadRqTr0qWLwsPD9fnnn+vBBx/Uvn37tGjRIj366KPXvW3CwsL02muv6dKlS9ZnizJ69NFH9dprr6lRo0aqXbu29uzZo6efflqDBg3SE088oSJFiujixYsyDCPHA7h58+aaNm2a3nzzTT333HP677//NHnyZDVq1Ejly5dXly5d1Lt3b3377be67777tG7dOv3888+ZOqYAAAC4ERiGocGDB9s86nG9AgMDFRUVReKUCzzT5KDg4GBFR0fr999/V/v27VWvXj0NHTpUjRo10uLFi+Xp6SkPDw8tWLBAqampatOmjVq2bKmUlBQtXbpUHh6O5afp8zh9+rQefPBBNW3aVEeOHNHSpUtVpEgR3X333Vq4cKFiYmIUHBysESNGaPTo0WratKn8/PzUunVrde/e3aYpnCTVqVNHM2fO1KJFi9SgQQM9++yzeuyxx/TMM89c97Zp1qyZrly5okaNGsnX1zfT+LZt22rEiBF66aWXVK9ePT333HPq06ePtWe/li1b6ty5c6pfv74uXLhgd1klSpTQ0qVL9eeff6p58+bq0KGDypcvr5kzZ0qS6tevrzfeeEPTpk1TgwYNtGHDBoWGhl73OgIAALgKyY3rmYz0urxbxMWLF1W/fn3t2rUr07uEkpKSdOjQIVWqVClPTcdw47kVv/PExETrvn/x4kUVK1bMxREBzsUxAHAc3Ghyap6XlJSkTp06SZLWrl2b4zUNzfOuSkxM1G233aYqVapkmRtkRPM8AAAAoBAzmUzy9vZ2qKzZbHa4LBxH8zwAAAAAsIOkCQAAAADsIGkCAAAAADtImgAAAADADpImAAAAALCDpAkAAAAA7KDLcSeyWCxy1muxTCaT3N3dnbIsAAAA4GZG0uQkFotFj4R11bmzCU5Zno9vaa1Z/RGJEwAAAHCdSJqcxDAMnTuboMQGfSRTAbeKNNKkncucVqsFAAAA3Mx4psnZTG6SWwH/XUdStnfvXo0cOVJNmzZVUFCQWrdurddff13nzp2TJB07dkwBAQGqXbu2/vvvv0zTv/baawoICNDq1avzHAMAAABQmJA0weqHH37QY489prvuukurVq3S7t27NX/+fB09elSdO3fWyZMnrWWLFi2qzz77zGb65ORkffbZZypatKizQwcAAAAKDEkTJEmpqal66aWX1LNnTw0bNkxly5aVyWRS5cqVNWvWLJUrV05Tpkyxlu/YsaM+/vhjm3ls3LhRNWrUkK+vr5OjBwAAAAoOSRMkSXFxcTp58qQeeeSRTOPc3NzUtWtXff3117JYLJKuJk2//vqrDh06ZC23atUqdenSxWkxAwAAAM5A0gRJUnx8vCTpzjvvzHJ8uXLllJKSojNnzkiSSpUqpebNm2vNmjWSpH///Ve///67HnjgAecEDAAAADgJSRMkSbfffrsk6Z9//slyfHx8vDw9PVWyZEnrsLCwMK1du1ZpaWlavXq12rdvLy8vL6fECwAAADgLSRMkSUFBQSpbtqxWrlxpHfbLL7/oyy+/lMVi0erVq9W8eXObpKh58+ZKSUnRtm3btGbNGnXt2tUVoQMAAAAFivc0OZuRJqU5YRm55OHhoWnTpmnQoEHy8vJSeHi4kpOT9eabb2ry5MmyWCz64IMPMk3z8MMPa9q0aSpZsqSqVauWX2sAAAAAFBokTU5iMpnk41ta2rnMKcvz8S0tk8mUq2maNGmiFStWaP78+QoLC1NiYqL8/PzUrFkzbdu2TXPnzlWPHj1spgkLC9PSpUs1YcKE/AwfAAAAKDRImpzE3d1da1Z/JMMwnLI8k8kkd3f3XE8XEBCgyMjITMMvX76sdevWyd/fX/v27bMOv/azJH3zzTe5DxgAAAAopEianCgvSUxh4e3trUcffdTVYQAAAABOR0cQAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBAAAAgB0kTQAAAABgh0uSpr1796pv374KDg5WaGioXnzxRZ05c0aSNGHCBNWqVUtBQUHWvxUrVlinXbNmjVq3bq26desqLCxMcXFxrlgFAAAAALcIpydNSUlJ6tevn4KCgvTDDz/o008/1blz5/TSSy9Jkvbs2aNJkyYpLi7O+te9e3dJ0o4dOzRp0iRNmzZNsbGxevjhhzVw4EBdvnzZ2auRJxaLRampqU75s1gsrl5dAAAA4Kbg9Pc0HT9+XNWqVdPgwYPl7u4uLy8vde/eXS+++KKSk5P1559/qlatWllOu3LlSrVv317169eXJPXp00crVqzQ+vXr1aVLF2euRq5ZLBZ169JZp8+cd8ry/EqV1MpVH9/Q74YCAAAACgOnJ0333nuvFi9ebDNsw4YNqlmzpvbu3avU1FTNmjVLu3btUokSJdSlSxf169dPbm5uOnDgQKbkqEqVKtq7d68zVyFPDMPQ6TPn9XbLs3I3FeyyLIb01LdXl5lbe/fu1aJFi7Rjxw4lJibKz89PDzzwgAYMGCAfHx9JUkBAgIoUKSJ3d3cZhiGTySR/f389/fTTeuCBB6zzCggIkCR9/vnnuvfee22Ws3TpUk2bNk3PPvushgwZkveVBQAAAAqYSzuCMAxDkZGR+vbbbzV27Fj9999/Cg4OVq9evfTdd99p+vTpWr58uZYsWSJJSkxMlLe3t808zGazLl265Irw88TdJHm4FexfXpOyH374QY899pjuuusurVq1Srt379b8+fN19OhRde7cWSdPnrSWXbRokeLi4vTTTz9p69at6tKli4YNG6bPPvvMZp6+vr5as2ZNpmWtXr1axYsXz1ugAAAAgBO5LGm6ePGihg4dqnXr1ik6OloBAQEKDQ3Vu+++q+DgYHl6eqp27drq3bu31q9fL0ny9vZWUlKSzXySkpJUrFgxV6zCTSU1NVUvvfSSevbsqWHDhqls2bIymUyqXLmyZs2apXLlymnKlClZTuvt7a1HH31UTz31lF5//XWbGq6OHTtq7dq1SktLsw775ZdflJycrBo1ahT4egEAAADXyyVJ05EjR9SlSxddvHhRH330kbUZ18aNG/XBBx/YlE1OTpbZbJYkVa1aVfv377cZf+DAAVWtWtU5gd/E4uLidPLkST3yyCOZxrm5ualr1676+uuvlZqamu08WrRooZMnT+rgwYM2w1JSUrR161brsI8++khdu3bN3xUAAAAACojTk6bz58+rd+/eqlevnt5++22VKlXKOs4wDE2dOlXbtm2TYRiKi4vTu+++a+09r2vXrlq3bp22b9+ulJQULVu2TAkJCWrdurWzV+OmEx8fL0m68847sxxfrlw5paSkWLuGz4qvr68k6dy5c9ZhHh4e6tixo7WJXlJSkjZs2KDOnTvnT+AAAABAAXN6RxCrV6/W8ePH9fnnn+uLL76wGRcXF6cxY8bolVde0cmTJ+Xn56chQ4aoU6dOkqTGjRtrwoQJ1vFVqlTRokWLrB0UIO9uv/12SdI///yjypUrZxofHx8vT09Pu9s6ISFBklS6dGmb4WFhYerevbsuXryojRs3ql69eipTpkz+BQ8AAAAUIKcnTX379lXfvn2zHd+jRw/16NEj2/GdOnWyJlHIP0FBQSpbtqxWrlyp0aNHS7r67NGJEyd0//33a/Xq1WrevLm8vLyynce3336r8uXLq2LFijbDq1WrpnvvvVeff/651q1bp969exfougAAAAD5yelJEwonDw8PTZs2TYMGDZKXl5fCw8OVnJysN998U5MnT5bFYsn0vFm6S5cu6eOPP9by5cv15ptvymTK3H1fWFiYli1bpgsXLqh58+YFvToAAABAvnFpl+O3IoshpaYV7J8l969nkiQ1adJEK1as0NGjRxUWFqZ+/fopLS1NzZo1k9ls1ty5c61N8J5++mkFBQUpKChIDzzwgL7++mvNmzcv2+fLOnTooMOHD+vhhx+Whwe5OgAAAG4cXL06iclkkl+pknrqW+csz69UySxrfHISEBCgyMjITMMvX76sdevWqVixYtq3b59D88pYztfXV7/++qvN+OXLl+c6PgAAAMDZSJqcxN3dXStXfWzzDqOCZDKZ5O7unm/zS38XEwAAAHCrIWlyovxMYgAAAAA4B880AQAAAIAdJE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHXY47kcViuWHf0wQAAADcqkianMRiseiRLo/o3JlzTlmeTykfrVm1hsQJAAAAuE4kTU5iGIbOnTknyyOWgm8UmSadW3Mu17VavXr1UnBwsIYMGaL9+/frrbfe0s6dO5WcnKwyZcqoXbt2Gjx4sLy8vDKV37Fjh5544gmVLVtWmzZtkpub7Uo+88wz+vbbb/Xuu+8qJCREs2fP1o8//qjly5dLklq1aqVTp07Jw+PqLmkYhooXL66OHTvqhRdeyDQ/AAAAwFm4EnU2Nyf9XYeLFy+qV69eqlOnjjZt2qTdu3drzpw5+uabbzRx4kS70yYnJ2vLli02w06fPq24uLgclztx4kTFxcUpLi5OP/30k95++219/PHHioqKuq71AQAAAK4HSRMyOXjwoM6ePavOnTvL29tbJpNJVatW1dixY3XbbbfZnbZjx476+OOPbYatWbNGbdq0yXUcAQEBatiwoX7//fdcTwsAAADkF5ImZFKtWjVVrlxZjz32mCIjI7V582adO3dOjRo10qhRo+xO26VLF23cuFH//fefddjq1avVtWvXXMWQkpKiHTt2aPv27QoNDc3TegAAAAD5gWeakImXl5c+/PBDvffee/rmm2/09ttvKzU1VfXq1dPo0aNVu3btbKetVq2aKlWqpPXr16t79+7atWuX3N3d7U6TbuLEiZoyZYr1c7ly5dS3b1/17NkzX9YLAAAAyAuSJmSpePHi6t+/v/r376/k5GT9+uuvWrRokfr27atvvvlGJUuWzHbasLAwrVmzRt27d9eqVascrmWaMGGCwsLC8msVAAAAgHxB8zxkEhkZqW7dulk/e3l5qV69epo+fbouXryoI0eO2J2+Y8eO+vXXX/XHH3/o66+/1sMPP1zQIQMAAAAFhqTJ2dKc9Hcd2rVrp3379umtt97SP//8I8MwdPr0ac2ZM0cVK1ZUQECA3el9fX3VsmVLvfjiiwoJCVGpUqWuLyAAAADAhWie5yQmk0k+pXx0bs05pyzPp5SPTCZTnqatVq2aoqOjNX/+fHXt2lWJiYm67bbbdN9992n58uXW9zTZExYWpmeeeUbPP/98nmIAAAAACguTkds3oN7gLl68qPr162vXrl0qXry4zbikpCQdOnRIlSpVktlszvdlWyyWXL9wNq9MJpPc3d2dsqwbWUF/54VRYmKidd+/ePGiihUr5uKIAOfiGAA4Dm42ly9ftr7eZcOGDfL29nZxRDeG9IqBKlWqZJkbZERNkxORxAAAAAA3Hp5pAgAAAAA7SJoAAAAAwA6SJgAAAACwg6QJAAAAAOwgaQIAAAAAO0iaAAAAAMAOuhx3It7TBAAAANx4SJqcxGKxqNsjj+j0uXNOWZ6fj49WrllD4gQAAABcJ5ImJzEMQ6fPndM4SQWdxlgkTTp3Lk+1WocOHdL8+fO1bds2/ffffypdurTatm2rgQMHqlixYurVq5fi4uLk6ekpSUpLS9Odd96pRx55RP369ZOb29UWn6NHj9a6devk5eWVaRk7d+5UgwYNrJ+TkpLk7u5uneedd96pzz77LA9rDgAAAOQ/kiYnc5fkLlMBLyVvTQB3796tJ598Uk8++aQ+/vhjlSpVSocOHdL48eP15JNP6r333pMkDRgwQEOGDJF0tQbtxx9/1OjRo/XPP/9o4sSJ1vl17NhR06ZNy3JZcXFx1v/36tVLwcHB1nkCAAAAhQkdQcBq/Pjx6ty5s4YOHapSpUpJkipVqqTIyEiVLl1aR48ezTSNu7u7GjdurClTpmjFihU6ePCgs8MGAAAAChRJEyRJR44c0f79+9WhQ4dM4/z8/DR37lzdc8892U7fpEkTeXl5aceOHQUYJQAAAOB8NM+DJOnMmTOSriZIeWEymVSyZEmdy9DRxaeffqqNGzfalIuMjNR9992X5zgBAAAAZyNpgiSpTJkykqRTp05lWaN0+vRpuwlVamqqLly4oNKlS1uHdejQIdtnmgAAAIAbBc3zIEkqX768/P39tX79+kzjEhIS1LJlS3366afZTr9161ZduXJFjRs3LsgwAQAAAKcjaYLVuHHjtGrVKkVFRens2bMyDEN//PGHnnnmGdWsWVNt2rTJNE1qaqo2b96s8ePHq3fv3rrrrrtcEDkAAABQcGie52QWSXntEjx3y8i94OBgRUdHa/78+Wrfvr0uX74sPz8/tW3bVgMGDLC+R2nBggVasmSJJMnT01MVKlRQ//799fjjj+fTGgAAAACFB0mTk5hMJvn5+GhSho4SCpKfj49Mpty/D6p27dqaO3dutuOXL1/u0Hxy8yyTo/MEAAAAXIGkyUnc3d21cs0aGUbB1jKlM5lMcnd3d8qyAAAAgJsZSZMTkcQAAAAANx46ggAAAAAAO0iaAAAAAMAOkiYAAAAAsIOkKQtpaWmuDgFO4qyOOQAAAHDjoiOIDLy8vOTm5qbjx4+rTJky8vLyylO33bgxGIahU6dOyWQyWd9BBQAAAFyLpCkDNzc3VapUSf/++6+OHz/u6nDgBCaTSRUqVKBnQwAAAGSLpOkaXl5euvvuu5WamiqLxeLqcFDAPD09SZgAAABgF0lTFtKba9FkCwAAAIBLOoLYu3ev+vbtq+DgYIWGhurFF1/UmTNnJEk///yzunXrpqCgILVq1UorV660mXbNmjVq3bq16tatq7CwMMXFxbliFQAAAADcIpyeNCUlJalfv34KCgrSDz/8oE8//VTnzp3TSy+9pPPnz6t///7q3LmzYmNjNXnyZE2dOlW//PKLJGnHjh2aNGmSpk2bptjYWD388MMaOHCgLl++7OzVAAAAAHCLcHrSdPz4cVWrVk2DBw+Wl5eXfH191b17d8XGxurLL7+Uj4+PwsPD5eHhocaNG6tjx46KiYmRJK1cuVLt27dX/fr15enpqT59+sjX11fr16939moAAAAAuEU4PWm69957tXjxYpuH7zds2KCaNWtq//798vf3tylfpUoV7d27V5J04MABu+MBAAAAIL+59OW2hmEoMjJS3377rcaOHavExER5e3vblDGbzbp06ZIk5TgeAAAAAPKby3rPu3jxosaMGaPffvtN0dHRCggIkLe3t/777z+bcklJSSpWrJgkydvbW0lJSZnG+/r6Oi1uAAAAALcWl9Q0HTlyRF26dNHFixf10UcfKSAgQJLk7++v/fv325Q9cOCAqlatKkmqWrWq3fEAAAAAkN+cnjSdP39evXv3Vr169fT222+rVKlS1nGtW7fW6dOntWzZMqWkpGj79u1at26dunTpIknq2rWr1q1bp+3btyslJUXLli1TQkKCWrdu7ezVAAAAAHCLcHrzvNWrV+v48eP6/PPP9cUXX9iMi4uL05IlSzR58mTNmjVLpUqV0ssvv6xGjRpJkho3bqwJEybolVde0cmTJ1WlShUtWrRIPj4+zl4NAAAAALcIpydNffv2Vd++fbMdHxgYqA8++CDb8Z06dVKnTp0KIjQAAAAAyMSlvecBAAAAQGFH0gQAAAAAdpA0AQAAAIAdJE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBAAAAgB0kTQAAAABgB0kTAAAAANhB0gQAAAAAdpA0AQAAAIAdJE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBAAAAgB0kTQAAAABgB0kTAAAAANhB0gQAAAAAdpA0AQAAAIAdJE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBAAAAgB0kTQAAAABgB0kTAAAAANhB0gQAAAAAdpA0AQAAAIAdJE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBAAAAgB0kTQAAAABgB0kTAAAAANhB0gQAAAAAdpA0AQAAAIAdJE0AAAAAYIdLk6YzZ86odevW2rFjh3XYhAkTVKtWLQUFBVn/VqxYYR2/Zs0atW7dWnXr1lVYWJji4uJcEToAAACAW4SHqxa8a9cujR49WkeOHLEZvmfPHk2aNEmPPPJIpml27NihSZMmadGiRapdu7ZiYmI0cOBAffvtt/L29nZW6AAAAABuIS6paVqzZo2ef/55DR8+3GZ4cnKy/vzzT9WqVSvL6VauXKn27durfv368vT0VJ8+feTr66v169c7I2wAAAAAtyCXJE1NmzbVV199pYceeshm+N69e5WamqpZs2apSZMmatOmjRYuXKi0tDRJ0oEDB+Tv728zTZUqVbR3716nxQ4AAADg1uKS5nllypTJcvh///2n4OBg9erVSzNmzNAff/yhwYMHy83NTf369VNiYmKmZnhms1mXLl1yRtgAAAAAbkGFqve80NBQvfvuuwoODpanp6dq166t3r17W5vfeXt7KykpyWaapKQkFStWzBXhAgAAALgFFKqkaePGjfrggw9shiUnJ8tsNkuSqlatqv3799uMP3DggKpWreq0GAEAAADcWgpV0mQYhqZOnapt27bJMAzFxcXp3XffVffu3SVJXbt21bp167R9+3alpKRo2bJlSkhIUOvWrV0cOQAAAICblcu6HM9K69atNWbMGL3yyis6efKk/Pz8NGTIEHXq1EmS1LhxY02YMME6vkqVKlq0aJF8fHxcGzgAAACAm5bLk6Z9+/bZfO7Ro4d69OiRbflOnTpZkygAAAAAKGiFqnkeAAAAABQ2JE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAKBQ2bJli7p166YtW7a4OhRAEkkTAAAACpGkpCRFRETo5MmTioiIUFJSkqtDAkiaAAAAUHhER0crISFBkpSQkKCYmBgXRwSQNAEAAKCQOHbsmGJiYmQYhiTJMAzFxMTo2LFjLo4MtzqSJgAAALicYRiKjIzMdnh6IgW4AkkTAAAAXO7w4cOKjY2VxWKxGW6xWBQbG6vDhw+7KDKApAkAAACFQMWKFdWwYUO5u7vbDHd3d1dwcLAqVqzoosgAkiYAAAAUAiaTScOHD892uMlkckFUwFUkTQAAACgUKlSooPDwcGuCZDKZFB4ervLly7s4MtzqSJoAAABQaPTs2VOlS5eWJPn5+Sk8PNzFEQEkTQAAAChEzGazRo4cqbJly2rEiBEym82uDgmQh6sDAAAAADIKDQ1VaGioq8MArEiaAAAAACczDENJSUn5Mq+M88mveUpXa/3ogOMqkiYAAADAyZKSktSmTZt8n2+nTp3ybV4bNmyQt7d3vs3vRsYzTQAAAABgBzVNAAAAgAtZOlqu/6rc+P9/r7c1Xarkvs4953K3GJImAAAAwJU8xFV5IUfzPAAAAACwg6QJAAAAAOygIhAAAABAnjjSdbphXH3gypHuywtrN+ckTQAAAAByzTAMDR48WL/++mu+zTMwMFBRUVGFLnGieR4AAACAPClsyU1BoaYJAAAAQK6ZTCZFRUXZbZ6XlJRkfeHu2rVrZTab7c6T5nkAAAAAbiomk0ne3t4OlTWbzQ6XLWxongcAAAAAdpA0AQAAAIAdJE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2MHLbQEAAABkkpSUlK/zyI/5pTObzTKZTPk2v5zkKWk6ffq0/Pz8lJycrI8++ki+vr5q165dfscGAAAAwEU6depUaOe3YcMGeXt759v8cpLrpGnlypWaPHmyfvrpJ02fPl3r16+XyWTSoUOHNGjQoIKIEQAAAABcJtdJU3R0tObMmSOLxaLVq1dr0aJFKlOmjHr16kXSBAAAANxERknyus55GP//7/U2pkuW9Pp1ziOvcp00/fvvvwoNDdXu3bvl4eGhevXqSZIuXLiQ78EBAAAAcB0vSV7Xne7kFyPnIgUk173nlSxZUocPH9aGDRsUHBwsSdq+fbvKlCmT78EBAAAAgKvluqapb9++6tixoyRp+fLl2rVrlwYMGKAJEybke3AAAAAA4Gq5Tpoef/xx3XffffLw8NAdd9yhM2fOKCYmRrVq1SqI+AAAAADApfL0cls/Pz+ZTCYdP35cSUlJKlGihL766qv8jg0AAAAAXC7XNU2rVq3SpEmTdOXKFZvhpUuXVuvWrfMtMAAAAAAoDHKdNM2fP1/Dhg1TsWLFFBsbq969e2v69OkKDQ0tiPgAAAAAwKVy3Tzv1KlT6t27txo3bqwjR46oZs2amjJlilauXFkQ8QEAAACAS+U6aSpdurRSUlJ0xx136NChQ5KkO++8UwkJCfkeHAAAAAC4Wq6Tptq1a2v8+PFKSkrSPffco/fff19r1qyRj49PAYQHAAAAAK6V62eaxowZo5dfflmJiYl64YUX9MwzzygpKUlTp04tiPgAAAAAwKVynTTdfvvtWrhwofX/27dvV0pKiry9vfM9OAAAAABwNYeb53366aeSpI8//tjm79NPP9WGDRv08ccf53rhZ86cUevWrbVjxw7rsJ9//lndunVTUFCQWrVqlamDiTVr1qh169aqW7euwsLCFBcXl+vlAgAAAICjHK5pmj9/vjp06KBZs2ZlOd5kMqlz584OL3jXrl0aPXq0jhw5Yh12/vx59e/fX0OHDlX37t0VGxurwYMHKyAgQLVr19aOHTs0adIkLVq0SLVr11ZMTIwGDhyob7/9lpouAAAAAAXC4aQpvabpm2++ue6FrlmzRrNmzdILL7yg4cOHW4d/+eWX8vHxUXh4uCSpcePG6tixo2JiYlS7dm2tXLlS7du3V/369SVJffr00YoVK7R+/Xp16dLluuMCAAAAgGvluve8jP744w+9/fbb+vnnn3M1XdOmTfXVV1/poYceshm+f/9++fv72wyrUqWK9u7dK0k6cOCA3fEAAAAAkN8cTppOnDihXr16KSgoSGPGjFFcXJy6deumhQsX6vHHH9eXX37p8ELLlCkjD4/MlVyJiYmZmtmZzWZdunTJofEAAAAAkN8cTppeffVVFS9eXDNmzFBycrIGDBigESNGaMeOHXr11VetPepdD29vbyUlJdkMS0pKUrFixRwaDwAAAAD5zeFnmnbt2qVvvvlGxYoVU7169RQSEqKePXtKkjp37pwv72ny9/fXli1bbIYdOHBAVatWlSRVrVpV+/fvzzS+WbNm171sAAAAAMiKwzVNycnJ1hqdkiVLqnjx4vLy8pIkubu7yzCM6w6mdevWOn36tJYtW6aUlBRt375d69ats3by0LVrV61bt876bqhly5YpISFBrVu3vu5lAwAAAEBWHK5pMplMNp/d3GzzrfxImnx9fbVkyRJNnjxZs2bNUqlSpfTyyy+rUaNGkq72pjdhwgS98sorOnnypKpUqaJFixbJx8fnupcNAAAAAFlxOGlKS0vTzp07rclRamqqzee0tLQ8BbBv3z6bz4GBgfrggw+yLd+pUyd16tQpT8sCAAAAgNxyOGlKSkqyPsOULuPna2uiAAAAgJvNli1b9NZbb2nYsGEKDQ11dThwEoeTJt6FBAAAgFtZUlKSIiIidPr0aUVERKh+/foym82uDgtOcF0vtwUAAABuFdHR0UpISJAkJSQkKCYmxsURwVlImgAAAIAcHDt2TDExMdbn+Q3DUExMjI4dO+biyOAMJE0AAACAHYZhKDIyMtvh+dGLNAo3kiYAAADAjsOHDys2NlYWi8VmuMViUWxsrA4fPuyiyOAsJE0AAACAHRUrVlTDhg3l7u5uM9zd3V3BwcGqWLGiiyKDs5A0AQAAAHaYTCYNHz482+G8eufmR9IEAAAA5KBChQoKDw+3Jkgmk0nh4eEqX768iyODM5A0AQAAAA7o2bOnSpcuLUny8/NTeHi4iyOCs5A0AQAAAA4wm80aOXKkypYtqxEjRvBi21uIh6sDAAAAAG4UoaGhCg0NdXUYcDJqmgAAAADADpImAAAAALCDpAkAAAAA7CBpAgAAAAA7SJoAAAAAwA6SJgAAAACwg6QJAAAAAOwgaQIAAAAAO0iaAAAAAMAOkiYAAAAAsIOkCQAAAADsIGkCAAAAADtImgAAAADADpImAAAAALCDpAkAAAAA7CBpAgAAAAA7SJoAAAAAwA6SJgAAAACwg6QJAAAAAOwgaQIAAAAAO0iaAAAAAMAOkiYAAAAAsIOkCQAAAADsIGkCAAAAADtImgAAAADADpImAAAAALCDpAkAAAAA7CBpAgAAAAA7SJoAAAAAwA6SJgAAAACwg6QJAAAAAOwgaQIAAAAAO0iaAAAAAMAOkiYAAAAAsIOkCQAAAADsIGkCAAAAADtImgAAAADADpImAAAAALDDw9UBAAAAACickiVJhoujuCrZhcsmaQIAAACQpdddHUA2DMO5iVyhbJ63fv161ahRQ0FBQda/F154QZL0888/q1u3bgoKClKrVq20cuVKF0cLAAAA4GZWKGua9uzZo06dOmnq1Kk2w8+fP6/+/ftr6NCh6t69u2JjYzV48GAFBASodu3aLooWAAAAuDmNkuTl6iD+X7L+V/NlMpmcuuxCWdO0Z88e1apVK9PwL7/8Uj4+PgoPD5eHh4caN26sjh07KiYmxgVRAgAAAMiJocLyVFTeFbqaprS0NP3222/y9vbW4sWLZbFY1Lx5cz3//PPav3+//P39bcpXqVJFH330kYuiBQAAAG5ehfWZJmcrdDVNZ86cUY0aNdSmTRutX79eH3zwgf7++2+98MILSkxMlLe3t015s9msS5cuuShaAAAAADe7QlfT5OfnZ9PcztvbWy+88IIeffRRhYWFKSkpyaZ8UlKSihUr5uwwAQAAgJva2rVrZTabr2seSUlJ6tSpU77NL11+zcdRhS5p2rt3rz799FONHDnS+oBXcnKy3NzcVLt2bb3zzjs25Q8cOKCqVau6IlQAAADgpmU2mzO18ipM83OmQtc8z8fHRzExMVq8eLFSU1N1/PhxTZ8+XY888ojatGmj06dPa9myZUpJSdH27du1bt06denSxdVhAwAAALhJFbqkqVy5clqwYIG+/vprBQcHq0uXLgoMDNT48ePl6+urJUuW6IsvvlBISIhefvllvfzyy2rUqJGrwwYAAABwkyp0zfMkKTg4WB988EGW4wIDA7MdBwAAAAD5rdDVNAEAAABAYULSBAAAAAB2kDQBAAAAgB0kTQAAAABgB0kTAAAAANhB0gQAAAAAdpA0AQAAAIAdJE0AAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2OHh6gAAAACAW1qqqwPIoDDFUoiQNAEAAABOZhiG9f/u69xdGEn2MsZ4q6N5HgAAAADYQU0TAAAA4GQmk8n6f0tHS+G5Kk/9X81XxhhvdYXl6wEAAABuTR7iqryQo3keAAAAANhB0gQAAAAAdpA0AQAAAIAdJE0AAAAAYAdJEwAAAADYQT8dAAAAuC6GYSgpKcmhcleuXMm35RYpUsThbrHNZjNdaCPPSJoAAABwXZKSktSmTRtXh2HXhg0b5O3t7eowspaaD/Mw/v/f680L8yOWmxBJEwAAAOBC6S+TReFF0gQAAIB8k1gvXHLL5hLTMKS0fKzKcPOQ7DW5S0tVsd0x+bc83LJImgAABWbLli166623NGzYMIWGhro6HADO4OYhuXvaKeDltFAKM7PZrA0bNuTLvJKSktSpUydJ0tq1a2U2m/Nlvvk1n5sBSRMAoEAkJSUpIiJCp0+fVkREhOrXr88PMAD8P5PJVCDPWJnN5sL77NYNjC7HAQAFIjo6WgkJCZKkhIQExcTQRAYAcGMiaQIA5Ltjx44pJiZGhnG1OyfDMBQTE6Njx465ODIAAHKPpAkAkK8Mw1BkZGS2w9MTKQAAbhQkTQCAfHX48GHFxsbKYrHYDLdYLIqNjdXhw4ddFBkAAHlDRxAAgHxVsWJFNWzYULt377ZJnNzd3VW/fn1VrFjRhdEBKHCWFFdH8D+FKRbc0EiaAAD5ymQyafjw4erVq1eWw0323qkC4IaUsdltsbj3XBhJ9mgajOtB8zwAQL6rUKGCatSoYTOsRo0aKl++vIsiAgAg76hpAgDku2PHjum3336zGfbbb7/p2LFjqlChgouiAlBQMtYgJwY9nsPLbZ3IkmKt+aKWG9eDmiYAQL5K7yUvqwsUes8DbgHunoXrD8gHJE0AgHxF73kAgJsNSRMAIF+l957n7u5uM9zd3V3BwcH0ngcAuOGQNAEA8lV6L3nZDee5AgDAjYakCQCQ7ypUqKDw8HBrgmQymRQeHk7veQCAGxJJEwCgQPTs2VOlS5eWJPn5+Sk8PNzFEQEAkDckTQCAAmE2mzVy5EiVLVtWI0aMkNlsdnVIAADkCe9pAoBCyjAMJSUlOVROyvkdJGaz2enPE4WGhio0NNSpywQAIL+RNAG45TmSnDiamEj5k5wYhqHBgwfr119/va75ZBQYGKioqCg6YgBQsNJSr38e6e9zu97zVX7EArty+g3NOM6RG4GuuMHnCJImALe0wpycFMYfDQDISbHdMa4OAU6S29/QTp065VimsN7gI2kCcMsrbCdm6WpMUVFROd6VS0pKsv4IrV271u5zQ4X17h1woyqMtdTIniPf1ZUrV/JteUWKFHH4+7yRv/sbNe7cImkCUGhs2bJFb731loYNG+a052AcSU5yk5hI+ffjZzKZ5O3t7XB5s9mcq/LX42Z43gq4HoW5ltoVzGazNmzYkGM5RxKTpKQkde/eXZK0YsUKu+dcRxKT9HO4I7UcrrJhwwannb/zk6M3+G6GmwckTQAKhaSkJEVEROj06dOKiIhQ/fr1ndbbWm6SE2cmJoUVF4twpsKcoLO//o8j59G8nDvSk6fscO5wvdze4LtRkTQBKBSio6OVkJAgSUpISFBMTIyeeuopF0eF7HCBAmdwRYLuaJImSdOnT7dba5KbGhPpaq3JjfygvCNcHfecZudUxN3IcpxhSMlp+bcsLzf7/VhcsZg0eLNP/i0QBYqkqRBZvHixoqOj1bNnT/Xr18/V4QBOc+zYMcXExFjvFBuGoZiYGLVp00YVKlRwcXT5LzcXZTnJba9EjrJ3UcbzVnnHMzC55+z1S0pKUps2bfJ9vjnVmOTGzd6USyq4msMi7obM7tmPd+5WzTp5Q+F0QyZNCQkJGjdunH788Ue5u7vr4Ycf1qhRo+ThcUOujiTp3Llzio6OVlpamqKjo9W1a1f5+Pi4OqxCzxXPwBTmpiI3IsMwFBkZme3wN99886bbPgV1UZaf7fVzuigrzM9bFVY0a8w9EvSbz63SlAs3nxsyyxg2bJjKli2r77//XqdPn9bAgQO1bNmyG7p2ZuzYsUpLu1onnJaWppdffllRUVEujqpwc8UzMFz05F12yeaRI0cUGxubabjFYlFsbKz+/PNP3X333ZnGc+GDGxH77P/kZ41rbjiSgKUbLsnrOpeXXpdwvd98sqTMt5eQW1csro7gfwpTLMjZDZc0HT58WD/++KM2b94sb29v3XXXXRo0aJCmT5+eq6QpMTExyx8vd3d3mwvvxMTEbOfh5uYms9msc+fO2S1rGIZSUlJs7qxcunTJWhPx22+/6aeffrKZ5pdfftGXX36pwMBAm7KSbU8xJpNJRYsWtY67fPmyNfny8fHJtI7FihWzKWuxWLL9AUkvm/7Dlj5fe2XNZrOuXLkiiyX7M0HRokWtcV25ckWpqVm/eM4wDLm5uWVbNiYmRvHx8TIMQ/Hx8VqyZIn69u0rSUpOTlZKSorN/DJeZHt7e8vNzc2mbHY/4GazWe7u7tYYkpOTs12/jPGmpaXZfG/ZlU1LS9OFCxcyxZtRkSJF5OHhIbPZLIvFYrcNvZeXlzw9PSVJqampmcpm3E8zLtPeviBJnp6e8vLysilrGIYuX76caRkZy6alpenSpUsaP368/vzzz0zzNZlM1u/CMIxM+9mTTz6ZZdlq1app3Lhx2V6Euru7q0iRItbj+dKlS9muW07HfVJSkvU7Tz/esyubkZubm81xn1424/wsD1kkd0kmyd3rf21GLMmW7FtuXFv2yv+XzWpT5Ga+Fslrg5c1xoznk6xkPJ9kXKeshl3bjPB6zhEZj2VHzyeS7XGfLrvvL6tzRHaKFCliHZ9T2WnTplljTElJUXJyss34K1eu6IknnpAkffDBBypevHi2ZdOXnZCQYD1HSFkf9xm3WU7niIwyls3LOcJeWYvFojZt2mR53GeU0zni2rLpNU7XM9/083NhTVIMw8h0bZDRtdcGWZXNbt/PzXF/veeIvJbNzXFftGhRm3UfvNk327KuYhiGLl68qLS0NJtrjuyO+3QZj/uUlBTrNktMTMz0veR0jsiosJwjMl5HXL58OV/Kenh4qEiRIpKubnd7v+GZpnW4ZCGxf/9++fj4qGzZstZhlStX1vHjx3XhwgXddtttDs3nzjvvzPJAf+ihh/TZZ59ZP99+++3ZXmw1a9ZMNWvWzJdah/QDJKPXXnvtuuZ5/vx5m9hKly6tffv2WT936tRJW7ZsyXJaNzc3NW7c2Pr5t99+y3SxmFF607jq1asrPj5en3zySbZl//33X3l4eMhkMunZZ5/VBx98kG3Zhg0bWg+Ev/76SydOnMi2bHJysnVehw4d0vHjx7Mt+/3336t69eqSpNdff13Tp0+XdPVgu/aiqnbt2ipRooQk6Z9//tHff/+d7Xxr1qxpbVZ58uRJHTx4MNuy1atXV6lSpfTbb78pODhYBw4cyLasv7+/ypQpow0bNujTTz/Vo48+mm3ZpUuXqk+fPpKuNrHq0KFDtmWXLFmiESNGSLq6TVq2bJlt2TfeeEMvvPCCJGn37t0KDg6WdHX7lCxZMtvpMspqP8/IZDLlWCbd3r17FR4e7lBZi8WirVu3Zjvez8/Puj9IV7dFdmrWrKlatWpZP2/ZsiXbi4aSJUuqdu3a1s/btm2z+XF3c3OTtl/9f7G7iinwuUDruF+m/6IrZ7P+ofIu6606L9Sxfv511q+6fDLrH4kivkUUNDbI+vn3eb8r8WjWPxIexTwUUjdE0tXzwy+//KLz589nWdbNzc2mSeyvv/6qs2fPZlk2fX7p/vjjD50+fTrbsk2aNLHuB/v27VN8fHy2ZePj41WmTBlJ0ogRIzR37txsyx46dEj33HOPzbCMvyUZ/frrr6pZs6YkacqUKZo4cWK28928ebPGjh0r6erzeYcOHcq2bGBgoPUccfz4cf3111/Zlm3Xrp1KlSol6er5JKubDumqVatm3Q6nTp3S3r17sy2bm3NEVFSUBg8eLCnv54isTJgwQaNGjZJ09YL+2huHGd15552qVKmSpKsXyLt27cq2bLly5VS5cmVJVy8is6rFTlemTBn5+/tLunru3759u3VcYGCgw9cTrlKjRg0dPnw423G//fab9XPDhg31+++/OzTfZs2aaefOnVmO8/Pz06lTp6yf27Vrp++++y7LskWLFrW5IO3SpYvWr1+f7XIzJja9evXSRx99lG3ZixcvWpOsAQMG6J133sm2bHx8vE1CVhhduHBBt99+uyTp22+/VYsWLSRJCxcu1LPPPpvtdJ9++qnat28v6epNlvTfufRzQUYffvihunXrJklas2ZNvl1HFOQ54pVXXpF09Tcj4+/utZ5//nnrddyRI0es54usDBo0SHPmzJEknT59WmXLls103ZedGy5pSkxMzNQWNv3zpUuXnH6SK8xNLUqWLJnpOZ9rn3lw9Dmg9IuHnPzxxx85zrdLly42283RGCpXrmz9McxJpUqV7B40o0ePtvmcHkNqaqrdZ+PKly+v8uXLOxTDHXfcoTvuuMOhsmXLls324i0jezVXuLFkmWyu+d9/G9RoYH8GGcrWq1JPquJY2bp315Uyt3ZEPslYe5GfZSXluay9mgBXSj+fFStWzOHfAbPZ7HBZLy8vh8u6u7tnKpvxxmPT0qX1crVq1nFts7nhKEnBPr56tWYN6+fO27YpKZvvIPC22zQ98H83Sx7d8aMupGZdU1mleHGVrXP1ZklhvvYojNK3V043XzcOrqLyvldv1E7feFJLtidkW3bdgMqqUuZqjUXUd/Ga8332N4I+fLKSAu+8eq369tbTevMb2xtBhfUYhS2TcYNdhX311Vd6+eWXtWPHDuuwffv26eGHH9bOnTutNQLZuXjxourXr6/Nmzdbmz1klN/N8wzDsDZLyngH3V61c3oc2ZX19/fXq6++KpPJlGXzvMTERD355JOF9qRqGAax5dEXX3whLy+v626el56gnT171nrXOy/V6pcvX86y84HcNqcp6LJR9521W9bdTSri8b+LzEtZ9Dmbfqb0cM+5bDo3k2T2tC17xWLSiC0lbZpyFlbDLBa7d9a8M5ynktPSZLHzc5KbsuYM2ya9bIqkGf8/fu3atdabZXlpnpeYmGg9/588eTLLu9C5aZ6XlpZmvdtbGBmGodWrV1t/KwpL05vU1FS1bds21+vjLIZh6Lm0NHlJcjeZ5JUhEb1s5zc8N2XdJBXJcGzYK5sq6a3/L7thwwYZhnHdzfPSfwsy1tzcjM3z0jvfSUtLU9R9Z7PtPc/sYZKb2/+fe1LTlGonl8lN2SIeJrlnUTbJImuX4+nntbw2z7tw4YLatWtnM6/sytI873/N806dOqU77rhDVapU0a5du7LMDazTZjumkKpatarOnTun06dPy8/PT9LVOwflypXLMWHKqFixYg5V1zpSxtfX1+bfay1ZssTujrF7926NGTMm0/DXX39ddevWzTTc3gPwvr6+unz5cqG+GDOZTA69ryK7t4YbhqEpU6Zoz549Nj8CJpNJgYGBeumll7Jc/5zeGm4YhooUKWL3BGUYhsaMGWOtUcsP1atX19SpU3P8zooUKSJvb2+ZTCaHe4r08PCwWzb9RCddTdQdbcKQXrZo0aLauHFjjuUNw9DIkSNtmotkJTfN82rVqqWIiIhst1vG3rTMHpLZ3fG79GbvgiubZLnmpkhHS+E5E6dK7uv+F1sJd3d5Ofj4upebY99bXssmy1D6VMWKFcuy960iRYpYfwwd5chvgZeXl/UHOSv2npcrDEwmk/V4vVZO54iM8nKOsCenm4euZjKZNCu785GD56mCLJvV95kfZXPTs11ue9AsiLK5Oe7d3NxU1MvNbpfj6bw83BzuBCSvZd0y/B5kdV7z9PS0+Z22x9PT0+68MsrNce/Kc0Q6Nze3Aimbfm50VGH5qXbYPffco/r162vKlCl69dVXdfbsWc2dO1ddu3Z1dWjZyql7zdDQUAUGBmrPnj3WYbVr17Z5puhm4+Pjc11djo4ePVq9evWy+dF1d3fXmDFjdOedd15XbDkdQPPnz6fL8f+Xm65j586dm+V2S0tLU/fu3XXhwoVM42677TatWLEiy2ZJN/J2y3f51T0X8sTb21tffPGF3bux6bK7GZTuwoULevrppyVdvQk2c+ZMuxeEOd0MSi9DF88AcH1uuKRJkmbNmqVXX31V999/v9zc3NS5c2cNGjTI1WFdl8mTJ6tz587WjgiupxMIs9msDRs25FjO0bv/uVGzZk27d//TXW/X4BUqVFB4eLiWL19ubVIXHh7u8PNG14N3TORNdtvt77//zjJhkq5eQJ46dSrTw/s3k4w1O7gxpTeFyulufm5fWXD27FlrT3rZya9XFrji/XO5+a3KKSFNSkqyvjw2p5YMjrQ6SF9ebspeb1yOLC9dQb9eA0BmN2TS5Ofnp1mzZrk6jHzl4+Ojnj17Kjo6Wj179ryuF9vmx91/6WpPcU8//bRNbY6Hh4cWLlyYbXLizLv/PXv21Pr1661NNR3tSQ2FS8WKFdWwYUPt3LkzU3PLhg0bqmLFii6M7tZ2taFq4XjsNftGszeWwlg76qr3zznyW5WX2NKTlOuJzZHmbLmNLae4HI0NgGvckEnTzapfv35Of0Fvdj9ahmFo3rx52Q5/8803XX5SN5vNGjlypN566y0NGzaMO283KJPJpOHDh2fZ3HL48OH5sp8VphcIZoxl7dq1173fZnx2Kz/md/nyZXXu3FmS9Pp1zang3GD9F1mZTCZFRUVleaPKMAyNHTtWP/30U6bjoG7dupo8eXKWx0J+3ahy9fncHmJDfrpiMSm7m0GGIdnp2yfXvNwke7vI1VhwoyBpQpYOHz6c5fstLBaLYmNjdfjw4ULRZCo0NNThLmVReBVEc8vC/jJDKf+fNTGbzdc9vxs1IblR2GummtX7hywWi3bt2qX4+Pg8nXMdbXY3ffr0fG8C58hyJfuJn71E81rOfo7U0dgcjSs/Y0P20nurA3KLpAlZSm8ytXv37kx3PevXr0+TKeQ7mlsWDhkv2EZJDvcIVdCS9b+ar5vxorKgzrnpXS3nN0eamjlqw4YNdpP9wvwcaWGODUD+ImlCljI2mcpq+M140QLXyu/mlhn30TnNzqpIIelv4YrlfzVft8JxRMd+juGcCxSc/Ox0JDcc7dhDonOPGwFJE7Llyh7qcGsqqOaWRdzl0Hs5YKuwPtN0syroc+6cZudUxP36ml+mt9683hzuisVEMyk4TW5qBHPzTivcWhx/KyNuST179lTp0qUliSZTAFDACvace/3Pq5lM158wXcWzcwBuLNQ0wS56qANuLY42Y3FEfvfsl+5mPg/l9zn3RugQhc5HANwISJqQI3qoA1zDkZ7PMo7PqawjPXMV1IPt+dGz362Ccy4AFD4kTQBQCOXlpZ7ptTrZ4cWZtx46RAGA/EHSBACFFBeTyE90iAIAeUfSBOCmZ+8N8I7Kz17DHFGYX+qZvkxnNx0EAMBVSJoA3PRu1K6NC+uLM2k6CADOldONqtzcpJK4UZUXJE0AgFzjx/bGcyPWuALI/Y2qnG5SSdyoyguSJgA3JbrOLjiFvekgsnaj1rgC4EZVYUDSBOCmRNfZBauwNh0EgJuNozeqHL1JJXGjKi9ImgAAuElR4wrcHLhR5XokTQAA3KSocQWA/OHm6gAAAAAAoDCjpgnALY+uXHGr471bAGAfSROAWxpdueJWx3u3ACBnNM8DcMvjwg63Oo4BALCPmiYAtzS6csWtjvduAUDOSJoA3PLoyhW3Oo4BALCP5nkAAAAAYAdJEwAAAADYQdIEAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBAAAAgB0kTQAAAABgB0kTAAAAANhB0gQAAAA4aMuWLerWrZu2bNni6lDgRCRNAAAAgAOSkpIUERGhkydPKiIiQklJSa4OCU5C0gQAAAA4IDo6WgkJCZKkhIQExcTEuDgiOAtJEwAAAJCDY8eOKSYmRoZhSJIMw1BMTIyOHTvm4sjgDCRNAAAAgB2GYSgyMjLb4emJFG5eJE0AAACAHYcPH1ZsbKwsFovNcIvFotjYWB0+fNhFkcFZSJoAAAAAOypWrKiGDRvK3d3dZri7u7uCg4NVsWJFF0UGZyFpAgAAAOwwmUwaPnx4tsNNJpMLooIzkTQBAAAAOahQoYLCw8OtCZLJZFJ4eLjKly/v4sjgDCRNAAAAgAN69uyp0qVLS5L8/PwUHh7u4ojgLCRNAAAAgAPMZrNGjhypsmXLasSIETKbza4OCU7i4eoAAAAAgBtFaGioQkNDXR0GnIyaJgAAAACwg6QJAAA4ZMuWLerWrZu2bNni6lAAwKlImgAAQI6SkpIUERGhkydPKiIiQklJSa4OCQCchqQJAADkKDo6WgkJCZKkhIQExcTEuDgiAHAekiYAAGDXsWPHFBMTI8MwJEmGYSgmJkbHjh1zcWQA4BwkTQAAIFuGYSgyMjLb4emJFADczEiaAABAtg4fPqzY2FhZLBab4RaLRbGxsTp8+LCLIgMA5yl0SdPPP/+satWqKSgoyPqX8W3Lhw4dUu/evRUUFKSmTZtq/vz5LowWAICbW8WKFdWwYUO5u7vbDHd3d1dwcLAqVqzoosgAwHkK3ctt9+zZo4YNG2r58uWZxqWkpOiZZ55R69attWjRIh04cEADBgxQxYoV1a5dOxdECwDAzc1kMmn48OHq1atXlsNNJpOLIgMA5yl0NU179uxRrVq1shwXGxur+Ph4DR06VF5eXqpRo4Z69epFDz4AABSgChUqKDw83JogmUwmhYeHq3z58i6ODACcw+k1TUlJSTp58mSW48qUKaM9e/bIz89PDz74oC5evKjg4GCNHj1a5cqV0/79+1WpUiV5eXlZp6lSpYoWLlzorPABALgl9ezZU+vXr9fp06fl5+dn03QeAG52Tk+afv75Zz3xxBNZjps1a5Zuv/12NWnSRI899phSUlI0adIk9e/fX2vWrFFiYqK8vb1tpvH29talS5ecEToAALcss9mskSNH6q233tKwYcNkNptdHRIAOI3Tk6aQkBDt27cv2/Ft2rSx+Txu3Dg1btxYf/31l4oWLarLly/bjL98+bKKFStWILECAID/CQ0NVWhoqKvDAACnK1TPNP3777+aOnWqEhMTrcOSk5MlXb3DVbVqVf39999KTU21jj9w4ICqVq3q9FgBAAAA3BoKVdLk6+urzz77TJGRkbpy5YrOnDmjiRMnqnHjxrr77rsVEhIiX19fRURE6MqVK9q7d6+WL1+url27ujp0AAAAADepQpU0mc1mLV68WH/99ZeaNm2qNm3aqHjx4nrrrbckSR4eHlqyZIn+/PNPhYaGqn///urVq5fCwsJcGzgAAACAm1ahe09TtWrVtHTp0mzHV6xYUW+//bYTIwIAAABwKytUNU0AAAAAUNiQNAEAAACAHSRNAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBgAO2bNmibt26acuWLa4OBQAAOBlJEwDkICkpSRERETp58qQiIiKUlJTk6pAAAIATkTQBQA6io6OVkJAgSUpISFBMTIyLIwIAAM5E0gQAdhw7dkwxMTEyDEOSZBiGYmJidOzYMRdHBgAAnIWkCQCyYRiGIiMjsx2enkgBAICbG0kTAGTj8OHDio2NlcVisRlusVgUGxurw4cPuygyAADgTCRNAJCNihUrqmHDhnJ3d7cZ7u7uruDgYFWsWNFFkQEAAGciaQKAbJhMJg0fPjzb4SaTyQVRAQAAZyNpAgA7KlSooPDwcGuCZDKZFB4ervLly7s4MgAA4CwkTQCQg549e6p06dKSJD8/P4WHh7s4IgAA4EwkTQCQA7PZrJEjR6ps2bIaMWKEzGazq0MCAABO5OHqAADgRhAaGqrQ0FBXhwEAAFyAmiYAAAAAsIOkCQAAAADsIGkCAAAAADtImgAAAADADpImAAAAALCDpAkAAAAA7CBpAgAAAAA7SJoAAAAAwA6SJgAAAACwg6QJAAAAAOwgaQIAAAAAO0iaAAAAAMAOkiYAAAAAsMPD1QE4m2EYkqSLFy+6OBLANRITE+XmdvV+ycWLF63HBHCr4BgAOA4A6epxYDKZJCnHY8Bk3GJHyYkTJ9S8eXNXhwEAAACgkPjuu+9Urly5bMffcklTWlqa4uPjVaxYMWtmCQAAAODWYxiGEhMTdfvtt1trX7NyyyVNAAAAAJAbdAQBAAAAAHaQNAEAAACAHSRNAAAAAGAHSRMAAAAA2EHSBAAAAAB2kDQBAAAAgB0kTQAAAABgB0kTci0gIEABAQE6ePBgpnFLly5VQECAZs+e7YLIgPyXkJCgQYMGqUGDBgoJCdHkyZOVmpqabfnvvvtOHTt2VN26ddWuXTt9++231nFpaWkKCgpS3bp1FRQUZP27dOmSM1YFyLPcHgerV69W27ZtFRQUpO7duys2NtY6juMAjrgZrzVatWql1atXZznu2LFjCggI0LFjxxya1+zZs9WrV6/8DM8hKSkpeuyxx3T06NFsY54zZ47q1q2rjRs3KiUlRT169HB4vQozkibkia+vr9asWZNp+OrVq1W8eHEXRAQUjGHDhqlo0aL6/vvv9dFHH2nbtm1atmxZlmX//vtvDRkyRM8995x27typIUOG6P/au/OYqK72D+BfkUVoS1lcgFaioELFUtncFxwUIjBIB4QqGqVEQRS1tghq6tJXa7VWRBYDTUWpEImAAhVEUSG0UIdFQ9EahVIguEHBVowwLPP+4c+bdxycn2Bfgb7fT2Iizzn3zDkTHuc+c8+9bty4Effv3wcAVFVVoaOjA1KpFFevXhX+6OjovMYVEfVeb/Lg4sWL2LFjB8LCwlBaWoqAgACsWrVKOPllHtDL4rnGwBMTEwMHBweMHj1aqa27uxu7du3CiRMncPz4ccyfPx8aGhpYv349wsLC+mG2fy8WTdQnYrEYGRkZ6O7uFmIVFRWQyWSYOHGiEJPL5UhMTISLiwvs7e2xdOlSVFZWCu3V1dUIDAyEo6MjrK2t4erqKnwz/+wbjFOnTkEkEsHOzg7+/v64d+/e61so/U+rra2FVCpFaGgotLW1MXr0aAQHByMpKanH/qdPn4a9vT3mz58PdXV1uLq6wsHBASkpKQCAX375BRYWFtDU1Hyp1y8rK4OXlxcmT56MxYsX45tvvhG+WUxPT8eSJUuwe/duTJs2DdOnT8e2bdvQ0dHx9yye6P/0Ng9++OEHuLu7Y968eRg6dCicnZ1hb2+PtLQ0AMwDenkD9Vzj+PHjWLBgAWxsbCCRSFBcXAzgadEQHx+P+fPnw87ODt7e3igsLOxxjNbWVoSFhcHOzg6zZ89GRkaGyveivLxcyIPnr9zI5XLEx8dDLBbD3t4eDg4O+PTTT9HW1ob79+9j4sSJKC8vF/o3NTXBysoKdXV1uH37Nvz8/ODg4IB58+YhLCwMra2tPc6hubkZiYmJ8PPzU2qTyWTYuHEjioqKkJKSgg8++EBomzFjBpqbm1FQUKByjQMdiybqE0dHR3R0dKCoqEiIpaamwtvbW6FfcnIyEhISEBkZieLiYkgkEvj7+6OpqQkAEBISggkTJuDChQsoLS3FrFmzsHPnToUx8vPzcebMGeTm5qKpqQmxsbH/9fURAcDt27ehp6eHUaNGCTFzc3PcuXMHf/31l1L/qqoqTJgwQSE2btw43Lx5E8DTk8X29nZ4eXlh2rRp8PPzU/gg+0/Nzc0ICgqCi4sLSkpK8NlnnyE5OVmhT3l5OQwNDVFYWIi4uDhkZ2fj/Pnzr7psIgW9zYOuri6lq0ZqamrClSbmAb2sgXiukZ6ejtjYWOzfvx9lZWVYsmQJ1qxZg4cPHyImJgZJSUmIjIzElStX8PHHHyM4OBgVFRVK43zxxReora3F+fPnkZmZibKyshe+Dy0tLQgMDBTyIDQ0FHl5eUJ7Tk4OEhMTERUVhdLSUpw8eRI//vgjsrKyMGrUKMycOVOhKMvMzISNjQ1MTU2xa9cuTJ8+HVKpFGlpabhx4wZOnTr1wrW///77Cv8WAE8LwICAAFy9ehUnT56Eqamp0rFubm5KuTvYsGiiPlFXV4dYLBYum7e1tSE3Nxeenp4K/ZKSkhAYGAhLS0toaGjA29sb5ubmyMzMBADExcUhJCQEcrkcDQ0N0NXVFbYyPbNq1Sro6upi+PDhEIlE+P3331/HEonw+PFjaGtrK8Se/dzT/Rc99R82bJjQd9iwYbC2tkZsbCzy8/MhEokQEBCA+vp6pbEuX74MbW1trFq1ChoaGpg6dSq8vLyUxg4KCoKGhgasra1hYWGBmpqaV1oz0fN6mwcuLi44c+YMpFIpOjs7kZeXh+LiYrS3twNgHtDLG4jnGqdPn4avry9sbGygpqaGxYsX4+jRoxg2bBjS0tKwevVqWFlZCbsNRCIRUlNTFcaQyWTIyclBSEgIDA0Noa+vj82bN7/wfcjPz1fIAzs7O4U8mDNnDlJTUzFmzBg0NzejpaUFenp6whq9vLxw7tw5yGQyYQ3PjtfS0kJhYSHOnTsHNTU1ZGRkwN/fv8d5/Pzzz7CxsVGKr1mzBpqammhpacHFixd7PNbW1hZXrlyBXC5/4ToHOvX+ngANXhKJBL6+vmhtbUVeXh5sbW0xYsQIhT4NDQ3Yt28fDhw4IMQ6OzsxadIkAMDNmzcRHByMxsZGmJubw8DAQCmhhg8fLvxdXV19UCccDS46Ojp48uSJQuzZz2+88YZSf21tbbS1tSnE2trahL7h4eEKbQEBAUhPT0dBQQGWLVum0PbHH3/A2NgYQ4YMEWJjx47Fr7/+KvxsaGio0K6hocH8oL9db/PAzc0Nzc3N+Pzzz/Hnn39i7ty5cHd3F45hHlBvDLRzjcbGRpiYmCjEbG1tATzd9vb8vT7vvvuusNvgmZaWFshkMhgbGwuxnu4Reub+/ftKeWBqairkgVwuR0REBC5fvgwDAwO899576OjoENYgEomwY8cOFBQUwMTEBA0NDXBxcQEAHDp0CFFRUYiIiMCmTZtga2uLnTt3Yvz48UrzuHv3LpycnJTizs7OCA8PR3JyMnbt2oVx48Zh8uTJCn1GjRqFJ0+eoKWlBQYGBi9c60DGoon6zNLSEmZmZsjJyUFWVhZWrFih1MfIyAjr16+Hm5ubEKurqxO+AdmwYQOio6MhEokAALm5udxWQQPG+PHj8fDhQzQ1NQkfqNXV1TAyMsJbb72l1H/ChAm4fv26Qqyqqkr44I6IiICLi4vCXnyZTAYtLS2lsUaPHo2GhgZ0d3dDTe3ppgDez0f9obd50NjYiNmzZys82cvHxwfOzs4AmAfUOwPtXMPY2Bh3795ViEVERMDDwwPvvPOO0hXT+vp6jBw5UiGmr68PLS0t1NfXw8zMDIDq32sjIyOVeXDgwAHcuXMHly5dEh6QIRaLhXZNTU2IxWKcPXsWJiYmWLhwIXR0dNDd3Y0bN24gJCQEW7duxd27d7F3716Eh4cL9yD+JzU1NYX7y55Zvnw5hgwZAj8/P1y7dg3r1q1DWlqawja+rq4uAMDQoUNfuM6Bjtvz6JVIJBIcO3YMNTU1mDt3rlK7j48Pjhw5gurqagBAYWEh3NzcUFJSgsePH6Orq0vY5lFVVYWYmBgAEC4hE/WnMWPGwM7ODl9++SVaW1tRX1+P2NhYpf30z3h4eEAqlSI7OxudnZ3Izs6GVCrFokWLAAC3bt3Cnj170NjYCJlMhujoaLS2tmLBggVKYzk6OkJTUxOHDx+GTCbD9evXhQdKEL1Ovc2DkpISLF++HA0NDWhvbxc+Iz788EMAzAPqvYF0riGRSJCSkoKKigp0d3cjLS0NSUlJ0NfXx+LFixEfH4/r16+jq6sLOTk5uHTpkvC7/4ympiY8PT0RGRmJe/fu4dGjR/j6669f+JoikQhyuRxRUVGQyWSorKxUuO+otbUVWlpaGDp0KNrb23H06FHcunVL4YEozx5KceHCBUgkEgBPi6Ddu3fj0KFDaG9vh4GBAbS0tKCvr9/jPExMTPDgwQOV788XX3wBfX19hISEKLy/Dx48gI6ODt5++22Vxw9kLJrolbi7u6O2thYeHh5QV1e+cLly5Up4enoiODgYNjY22LNnD7Zv3w4nJyeYmZlh8+bNCA0NhZ2dHTZs2AAvLy9oaGjg1q1b/bAaImWHDx9GZ2cnnJyc4OPjg9mzZyM4OFhot7GxEfbNm5ubIyYmBnFxcXBwcEBsbCyioqIwduxYAMDevXthamqKRYsWYerUqZBKpUhISICenp7S62pra+Po0aOoqKjAzJkzsXPnTkybNu21rJnoeb3JA1dXV/j6+sLX1xfTp0/HxYsXcfz4cRgaGgJgHlDvDaRzDbFYjJCQEISGhsLe3h4pKSn49ttvYWBgAH9/f/j5+eGTTz6Bvb094uLicPDgQUyZMkVpnG3btsHa2hpisRjOzs4KT5t7nq6uLr777jsUFxdjypQp2LZtm7C9Dnj6XwK0tbVhxowZEIlEuHbtGhYtWqSwPktLS5iamkJNTQ12dnZC/NChQ6iursasWbMwY8YMPHr0CP/61796nMfMmTNVPrACeJqzUVFRqK6uxo4dO4R4WVkZZs+erfLYgW6InBt/iYgGhaioKEilUnz//ff9PRWifsM8IOqbdevWwdraGqtXr+7T8U1NTXBxcUF2drbSE/T+PwsXLsSWLVswZ86cPr32QMArTURERERE/1D19fW4cOECioqKhK15fTF8+HAsW7YMiYmJvTquoKAAhoaGg7pgAlg0ERERERH9Y0VHR2PLli3YunWrwlMC+2Lt2rUoLS1FXV3dS/Xv6OhAdHQ09u7d+0qvOxBwex4REREREZEKvNJERERERESkAosmIiIiIiIiFVg0ERERERERqcCiiYiI/ufV1tb29xSIiGgAY9FERESDjoWFBSwsLPDbb78ptSUkJMDCwgJRUVEvNda+fftw5MiRF7ZnZmbCzc2tz3MlIqLBj0UTERENSvr6+jh9+rRSPD09HW+++eZLj9PS0qKy3cPDA2fPnu31/IiI6J+DRRMREQ1KYrEYGRkZ6O7uFmIVFRWQyWSYOHGiEJPL5UhMTISLiwvs7e2xdOlSVFZWAgBiYmKQlZWFrKwseHh4AHh6FWv37t2YOnUqgoKCkJ6eDpFIJIz3008/wdvbGzY2NhCJRDhx4sRrWjEREfUXFk1ERDQoOTo6oqOjA0VFRUIsNTUV3t7eCv2Sk5ORkJCAyMhIFBcXQyKRwN/fH01NTVi7di3EYjHEYjEyMzOFY+rq6pCfn4/9+/crjFVTU4OgoCB89NFHKCkpweHDh3Hw4EEUFhb+dxdLRET9ikUTERENSurq6hCLxcIWvba2NuTm5sLT01OhX1JSEgIDA2FpaQkNDQ14e3vD3NxcoUh6nru7O7S1taGrq6sQP3v2LKysrODt7Q11dXVMmjQJycnJsLKy+tvXR0REA4d6f0+AiIioryQSCXx9fdHa2oq8vDzY2tpixIgRCn0aGhqwb98+HDhwQIh1dnZi0qRJLxx35MiRPcYfPHgAExMThZilpeUrrICIiAYDFk1ERDRoWVpawszMDDk5OcjKysKKFSuU+hgZGWH9+vUKT8Crq6uDnp7eC8cdMmRIj3FjY2MUFBQoxNLS0mBoaAhHR8c+rYGIiAY+bs8jIqJBTSKR4NixY6ipqcHcuXOV2n18fHDkyBFUV1cDAAoLC+Hm5oaSkhIAgKamJh49evRSr+Xm5oYbN27gzJkz6OrqQmVlJb766iuoq/M7SCKifzIWTURENKi5u7ujtrYWHh4ePRYvK1euhKenJ4KDg2FjY4M9e/Zg+/btcHJyAgC4urqivLz8pa4UmZqaIj4+HklJSZgyZQo2bdqE8PBwzJo16+9eFhERDSBD5HK5vL8nQURERERENFDxShMREREREZEKLJqIiIiIiIhUYNFERERERESkAosmIiIiIiIiFVg0ERERERERqcCiiYiIiIiISAUWTURERERERCqwaCIiIiIiIlKBRRMREREREZEKLJqIiIiIiIhUYNFERERERESkAosmIiIiIiIiFf4NUA2tbjpwGl8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_trend_bias_data = trend.calculate_future_trend_bias(statistics = [\"mean\", 0.05, 0.95], \n",
    "                                                        trend_type = 'additive',\n",
    "                                                  raw_validate = tas_cm_validate, raw_future = tas_cm_future,\n",
    "                                                        metrics = tas_metrics,\n",
    "                                                  QM = [tas_val_debiased_QM, tas_fut_debiased_QM],\n",
    "                                                        QDM = [tas_val_debiased_QM, tas_fut_debiased_QDM],\n",
    "                                                  ISIMIP = [tas_val_debiased_ISIMIP, tas_fut_debiased_ISIMIP],\n",
    "                                                  CDFT = [tas_val_debiased_CDFT, tas_fut_debiased_CDFT])\n",
    "\n",
    "tas_trend_plot = trend.plot_future_trend_bias_boxplot(variable ='tas', bias_df = tas_trend_bias_data,                                                         remove_outliers = True,\n",
    "                                                        outlier_threshold = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2f9846",
   "metadata": {},
   "source": [
    "Wee see that while the trend in mean and 95th percentile is decently preserved in delta-QM, ISIMIP and CDFt, it is modified more in the Quantile Delta Mapping method."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af251c6b",
   "metadata": {},
   "source": [
    "## Part 3: Testing assumptions\n",
    "\n",
    "A detailed overview of assumptions associated with a specific bias adjustment method can be found in the documentation of each bias adjustment method. For the sake of demonstration, we investigate the goodness of fit and seasonality here - this should be seen as an example of one possible assumption to investigate further that can be adapted further to the bias adjustment used.\n",
    "\n",
    "For all parametric methods, distributions are fitted to both the climate model and the observational data. Default distributions for each variable are specified in the individual methods. We assess the following two components:\n",
    "- Is the fit of the default distribution 'good enough' or should a different distribution be used? (1)\n",
    "- Is there any seasonality in the data that should be accounted for, for example by applying a 'running window mode' (meaning that the bias adjustment is fitted separately for different parts of the year, i.e. windows)? (2)\n",
    "\n",
    "**Calculating the AIC and plottinge the worst fit**\n",
    "\n",
    "The Akaike Information Criterion is a statistical method for comparative evaluation of different statistical models. The AIC weighs model complexity with goodness of fit, with a lower AIC indicating a 'better model', and is computed as follows: \n",
    "\n",
    "$$ AIC = 2 \\frac{k}{n} - 2 \\frac{l}{n}$$\n",
    "\n",
    "whereby $l$ is the log likelihood function calculated in the following way:\n",
    "\n",
    "$$ l = - \\frac{n}{2} (1+ \\ln(2 \\pi) + \\ln (\\frac{1}{n} \\sum_{i=1}^{n} (y_i - \\hat{y_i})^2)) $$\n",
    "\n",
    "To demonstrate, we test the fit of the normal and the beta distribution to the temperature data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "52c1c61d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:709: RuntimeWarning: invalid value encountered in sqrt\n",
      "  sk = 2*(b-a)*np.sqrt(a + b + 1) / (a + b + 2) / np.sqrt(a*b)\n",
      "/Users/fionaspuler/opt/anaconda3/lib/python3.9/site-packages/scipy/optimize/_minpack_py.py:178: RuntimeWarning: The iteration is not making good progress, as measured by the \n",
      "  improvement from the last ten iterations.\n",
      "  warnings.warn(msg, RuntimeWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Distribution of AIC values across locations \\n Daily mean near-surface air temperature')]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_obs_aic = assumptions.calculate_aic('tas', tas_obs_validate, scipy.stats.norm, scipy.stats.beta)\n",
    "\n",
    "assumptions.plot_aic(variable = 'tas', aic_values = tas_obs_aic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "25c4c353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_obs_worst_fit = assumptions.plot_fit_worst_aic(variable = 'tas', dataset = tas_obs,  \n",
    "                                                   data_type = 'Observed', distribution = scipy.stats.beta,\n",
    "                                                   nr_bins = 100, aic_values = tas_obs_aic)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4352be42",
   "metadata": {},
   "source": [
    "Given the plot we see above, and the strong bi-modal distribution of the temperature data, we make the assumption that might be seasonal variation at play here. This can be further investigated by using the following function: it plots the time-series and autocorrelation function of the quantile residuals. It also plot a QQ plot of the normalized quantile residuals to get a picture of the goodness of fit for different quantiles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1cfc1e31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tas_obs_plot_gof = assumptions.plot_quantile_residuals(variable = 'tas', dataset = tas_obs[:,0,0], data_type = 'observation data',\n",
    "                                                      distribution = scipy.stats.norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb2c189",
   "metadata": {},
   "source": [
    "Overall the distributions do not fit the tempereature data well due to the bi-modal distribution of temperature. Based on the quantile residuals, we can see that there is a strong seasonality at play. One option to address this issue is to build a statistical model that explicitely includes the season as covariate. An alternative approach is to use a so-called 'running-window-mode' (applied for example in the QDM and ISIMIP debiasers) where a separate mapping is conducted each day (or month) of the year, which also takes care of the seasonality. For more details on the running-window-mode, have a look at the documentation of different debiasers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7e2194",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf9008eb",
   "metadata": {},
   "source": [
    "This notebook provides an overview of the different types of evaluation metrics that can be applied as part of ibicus. For the variables and region studied, ISIMIP and CDFt perform best across metrics, although the choice of debiaser for a specific case-study will depend on the metrics the user is most interested in, as well as an understanding of the source of the climate model bias for the variable of interest.\n",
    "\n",
    "We hope this notebook provides helpful guidance for you to evaluate your own bias adjustment project and choose the methodology most suited to your application."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}