SeedOil_Models_Reg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IMPORT STATMENTS FOR ALL MODLES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: You are using pip version 20.0.2; however, version 20.3.3 is available.\r\n",
      "You should consider upgrading via the '/usr/bin/python -m pip install --upgrade pip' command.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "!TMPDIR=/home/mgill/ pip install --cache-dir=/home/mgill/ --build /home/mgill/ pandas numpy sklearn xgboost matplotlib tensorflow==2.1.0 keras==2.2.4 fastai python-dateutil scikit-optimize scikit-learn==0.21 seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n",
      "2.2.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/home/mgill/.local/lib/python3.6/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf; print(tf.__version__)\n",
    "import keras; print(keras.__version__)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
    "from keras.utils import np_utils\n",
    "from keras.regularizers import *\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.pipeline import Pipeline\n",
    "from fastai.tabular.all import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from random import randint\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Input\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import Conv1D\n",
    "from tensorflow.keras.layers import MaxPooling1D\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Embedding\n",
    "from tensorflow.keras.preprocessing import sequence\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers import Activation\n",
    "from math import sqrt\n",
    "from statistics import mean\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import xgboost as xgb\n",
    "from sklearn.impute import SimpleImputer\n",
    "from tensorflow.keras.wrappers.scikit_learn import KerasRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from keras.optimizers import Adam\n",
    "import skopt\n",
    "from skopt.searchcv import BayesSearchCV\n",
    "from skopt.space import Real, Categorical, Integer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import pickle\n",
    "from collections import OrderedDict\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from scipy import interp\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reads in data from the training testing data csv, and the holdout data csv\n",
    "#extracts the final column to be the label set for both training testing and holdout\n",
    "def new_prep_data(tt_file, ho_file):\n",
    "    imp = SimpleImputer(missing_values='./.', strategy='most_frequent')\n",
    "    my_list = []\n",
    "    x = 0 \n",
    "    for chunk in pd.read_csv(tt_file, chunksize=10000, index_col=\"Unnamed: 0\"):\n",
    "        x=x+10000\n",
    "        chunk = chunk.T\n",
    "        if 'Value' in chunk.columns:\n",
    "            #does the selecting of pheno array for application ML\n",
    "            chunk[\"Value\"] = pd.to_numeric(chunk[\"Value\"], downcast=\"float\")\n",
    "            tt_pheno = chunk[\"Value\"].to_numpy()\n",
    "            #reshapes it so its not a 1D array\n",
    "            print(tt_pheno.shape)\n",
    "            tt_pheno = np.reshape(tt_pheno,(len(tt_pheno),1))\n",
    "            print(tt_pheno.shape)\n",
    "            chunk = chunk.drop(columns=['Value'])\n",
    "        headers = chunk.columns\n",
    "        row_idx = chunk.index\n",
    "        chunk = imp.fit_transform(chunk) #SHOULD TURN ./. into the most common for each column\n",
    "        #since imputing makes a numpy array have to turn back into PD for label encoding\n",
    "        chunk = pd.DataFrame(data = chunk, index = row_idx, columns = headers)\n",
    "        my_list.append(chunk)\n",
    "        print(x)\n",
    "    tt_vcf = pd.concat(my_list, axis = 1)\n",
    "    my_list = []\n",
    "    x=0\n",
    "    for chunk in pd.read_csv(ho_file, chunksize=10000, index_col=\"Unnamed: 0\"):\n",
    "        x=x+10000\n",
    "        chunk = chunk.T\n",
    "        if 'Value' in chunk.columns:\n",
    "            #does the selecting of pheno array for application ML\n",
    "            chunk[\"Value\"] = pd.to_numeric(chunk[\"Value\"], downcast=\"float\")\n",
    "            ho_pheno = chunk[\"Value\"].to_numpy()\n",
    "            #reshapes it so its not a 1D array\n",
    "            print(ho_pheno.shape)\n",
    "            ho_pheno = np.reshape(ho_pheno,(len(ho_pheno),1))\n",
    "            print(ho_pheno.shape)\n",
    "            chunk = chunk.drop(columns=['Value'])\n",
    "        headers = chunk.columns\n",
    "        row_idx = chunk.index\n",
    "        chunk = imp.fit_transform(chunk) #SHOULD TURN ./. into the most common for each column\n",
    "        #since imputing makes a numpy array have to turn back into PD for label encoding\n",
    "        chunk = pd.DataFrame(data = chunk, index = row_idx, columns = headers)\n",
    "        my_list.append(chunk)\n",
    "        print(x)\n",
    "    ho_vcf = pd.concat(my_list, axis = 1)\n",
    "    print(tt_vcf.shape)\n",
    "    print(ho_vcf.shape)\n",
    "    return tt_vcf, ho_vcf, tt_pheno, ho_pheno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#variables used going forward\n",
    "tt_vcf, ho_vcf, tt_pheno, ho_pheno = new_prep_data(\"Oil_Merged_filtered.csv_train_test.csv_5pcnt.csv\", \"Oil_Merged_filtered.csv_holdout.csv_5pcnt.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fits a one hot encoder to the training set, then transforms the training set and holdout set\n",
    "ohe = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
    "ohe = ohe.fit(tt_vcf)\n",
    "print(tt_vcf.shape)\n",
    "tt_vcf = ohe.transform(tt_vcf)\n",
    "print(tt_vcf.shape)\n",
    "print(ho_vcf.shape)\n",
    "ho_vcf = ohe.transform(ho_vcf)\n",
    "print(ho_vcf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saves the ohe for later use in case data needs to be encoded in same format\n",
    "pickle.dump(ohe, open(\"Oil_ohe.dat\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#if need or have new holdout data etc.\n",
    "ohe = pickle.load(open(\"Oil_ohe.dat\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#goes through each category stored in the encoder to find the specific value of feature\n",
    "#works as category stores the value of each SNP linearly and features in XGBoost model are names f0 -> f99999 etc.\n",
    "def find_snp_from_header(ohe,snp_num):\n",
    "    count = 0\n",
    "    snp = \"Not found\"\n",
    "    found = False\n",
    "    i = 0\n",
    "    while i < len(ohe.categories_) and (found == False):\n",
    "        j = 0\n",
    "        while j < len(ohe.categories_[i]):\n",
    "            if(count == snp_num):\n",
    "                snp = ohe.categories_[i][j]\n",
    "                found = True\n",
    "                break\n",
    "            count = count + 1\n",
    "            j = j + 1\n",
    "        i = i + 1\n",
    "    return snp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A/A\n"
     ]
    }
   ],
   "source": [
    "## TESTING IF IT WORKS\n",
    "my_snp = find_snp_from_header(ohe, 108005)\n",
    "print(my_snp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost ML Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tt_vcf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-561ed1e399ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#FOR OPTIMISING MODEL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtt_vcf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtt_pheno\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'tt_vcf' is not defined"
     ]
    }
   ],
   "source": [
    "#FOR OPTIMISING MODEL\n",
    "#SKIP IF MODEL ALREADY OPTIMISED FOLLOWING IF ALREADY OPTIMISED\n",
    "print(tt_vcf.shape)\n",
    "print(tt_pheno.shape)\n",
    "seed = randint(0,5000)\n",
    "X_train, X_test, y_train, y_test = train_test_split(tt_vcf, tt_pheno, test_size=0.2, random_state=seed)\n",
    "print(X_test.shape)\n",
    "print(\"seed is \" + str(seed))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "space ={'learning_rate': Real(0.01, 1.0, 'log-uniform'),\n",
    "        'min_child_weight': Integer(0, 10),\n",
    "        'max_depth': Integer(0, 50),\n",
    "        'max_delta_step': Integer(0, 20),\n",
    "        'subsample': Real(0.01, 1.0, 'uniform'),\n",
    "        'colsample_bytree': Real(0.01, 1.0, 'uniform'),\n",
    "        'colsample_bylevel': Real(0.01, 1.0, 'uniform'),\n",
    "        'reg_lambda': Real(1e-9, 1000, 'log-uniform'),\n",
    "        'reg_alpha': Real(1e-9, 1.0, 'log-uniform'),\n",
    "        'gamma': Real(1e-9, 0.5, 'log-uniform'),\n",
    "        'min_child_weight': Integer(0, 5),\n",
    "        'n_estimators': Integer(50, 200),\n",
    "        'scale_pos_weight': Real(1e-6, 500, 'log-uniform')}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_step(optim_result):\n",
    "    \"\"\"\n",
    "    Callback meant to view scores after\n",
    "    each iteration while performing Bayesian\n",
    "    Optimization in Skopt\"\"\"\n",
    "    score = xgb_bayes_search.best_score_\n",
    "    print(\"best score: %s\" % score)\n",
    "    if score >= 0.98:\n",
    "        print('Interrupting!')\n",
    "        return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "xgbreg = xgb.XGBRegressor()\n",
    "xgb_bayes_search = BayesSearchCV(xgbreg, space, n_iter=16, # specify how many iterations\n",
    "                                    scoring=None, n_jobs=1, cv=5, verbose=3, random_state=42, n_points=12,\n",
    "                                 refit=True)\n",
    "xgb_bayes_search.fit(X_train, y_train.ravel(), callback = on_step)\n",
    "print(xgb_bayes_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check to make sure all the data has remained the same\n",
    "np.isnan(X_train).any()\n",
    "np.isnan(y_train).any()\n",
    "\n",
    "np.isinf(X_train).any()\n",
    "np.isinf(y_train).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_bayes_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##START HERE\n",
    "#EITHER THE XGBOOST MODEL will defined with the best estimator function or typed in if not open in same session\n",
    "\n",
    "#xgbreg = xgb_bayes_search.best_estimator_ #xgb.XGBRegressor(#n_estimators=100, nthread=-1) \n",
    "best_params = OrderedDict([('colsample_bylevel', 0.88542798001491),\n",
    "             ('colsample_bytree', 0.15107725561517718),\n",
    "             ('gamma', 0.09274068410384222),\n",
    "             ('learning_rate', 0.11437510345794359),\n",
    "             ('max_delta_step', 0),\n",
    "             ('max_depth', 6),\n",
    "             ('min_child_weight', 5),\n",
    "             ('n_estimators', 200),\n",
    "             ('reg_alpha', 1.2952579279714691e-08),\n",
    "             ('reg_lambda', 0.45707493844512304),\n",
    "             ('scale_pos_weight', 10.561837873121673),\n",
    "             ('subsample', 0.9105119208713889)])\n",
    "xgbreg = xgb.XGBRegressor(**best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(xgbreg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#one function which should print out how the model performs\n",
    "def eval_k_fold(m, x, y, k):\n",
    "    #model: xgboost model, should be with the best params available\n",
    "    #x: input data (eg. all samples and SNPS)\n",
    "    #y: labels\n",
    "    #k: number of folds for cross validation\n",
    "    cv = KFold(n_splits=k,shuffle=True)\n",
    "  #  fig1 = plt.figure(figsize=[12,12])\n",
    "\n",
    "   # tprs = []\n",
    "   # aucs = []\n",
    "    results = []\n",
    "   # mean_fpr = np.linspace(0,1,100)\n",
    "    low = 100\n",
    "    best = m\n",
    "    i = 1\n",
    "    for train,test in cv.split(x,y):\n",
    "        #print(y[test])\n",
    "        m.fit(x[train],y[train].ravel())\n",
    "        print(\"fitting done. Processing fold accuracy + checking best model\")\n",
    "        #predictions = [round(value) for value in y_pred]\n",
    "        #sees how accurate the model was when testing the test set\n",
    "        all_preds = [x for x in m.predict(x[test])]\n",
    "        ss = sqrt(mean_squared_error(all_preds, y[test]))\n",
    "        rr = r2_score(all_preds, y[test])\n",
    "        mm = np.mean(y[test])\n",
    "        error_mean = ((ss/mm)*100)\n",
    "        print(\"R^2 Value is: \" + str(rr))\n",
    "        print(\"RMSE for dataset is:\" +str(ss) + \"& mean of this fold is \" + str(mm))\n",
    "        print(\"this is \"+ str((ss/mm)*100) + \"% of the mean pheno data\")\n",
    "        if(error_mean < low):\n",
    "            low = error_mean\n",
    "            best = m\n",
    "        results.append(error_mean)\n",
    "        i= i+1\n",
    "    print(\"Training Testing Accuracy: %.2f%% (%.2f%%)\" % (np.mean(results), np.std(results)))\n",
    "    return best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = eval_k_fold(xgbreg, tt_vcf, tt_pheno, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#uses best model from kfold eval on the holdoutset to predict how it went\n",
    "all_preds = [x for x in best_model.predict(ho_vcf)]\n",
    "ss = sqrt(mean_squared_error(all_preds, ho_pheno))\n",
    "rr = r2_score(all_preds, ho_pheno)\n",
    "mm = np.mean(ho_pheno)\n",
    "error_mean = ((ss/mm)*100)\n",
    "print(\"R^2 Value of Holdout: %.2f\" % rr)\n",
    "print(\"RMSE of Holdout: %.2f\" % ss)\n",
    "print(\"Mean of Holdout: %.2f\" % mm)\n",
    "print(\"this is \"+ str((ss/mm)*100) + \"% of the mean pheno data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_x, plot_y = list(), list()\n",
    "\n",
    "ho_pheno = ho_pheno.ravel()\n",
    "\n",
    "for counter, i in enumerate(ho_pheno):\n",
    "    if counter <= 5:\n",
    "        print(counter, i, all_preds[counter])\n",
    "    #zoom in a bit closer\n",
    "    if(all_preds[counter] > 9):\n",
    "        plot_x.append(i)\n",
    "        plot_y.append(all_preds[counter])\n",
    "    \n",
    "#plt.plot(plot_x, plot_y, '.')\n",
    "thisplot = pd.DataFrame({'Oil':plot_x, 'preds':plot_y})\n",
    "#sns.jointplot(x=\"Oil\", y=\"preds\", data=thisplot, kind='reg' , joint_kws={'scatter_kws': {'alpha': 0.2}})\n",
    "sns.regplot(x=\"Oil\", y=\"preds\", data=thisplot)\n",
    "plt.xlabel('Real Seed Oil %')\n",
    "plt.ylabel('Predicted Seed Oil %')\n",
    "plt.title(\"XGBoost Seed Oil Prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save this model for later\n",
    "pickle.dump(best_model, open(\"Oil_kfold_10_XGB.pickle.dat\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#only load if not generated in same session\n",
    "best_model = pickle.load(open(\"Oil_kfold_10_XGB.pickle.dat\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#what the model initially generates for feature importance with inbuilt functions\n",
    "\n",
    "from xgboost import plot_importance\n",
    "from matplotlib import pyplot\n",
    "#best_model = pickle.load(open(\"Oil_kfold_10_tt_from_all.pickle.dat\", \"rb\"))\n",
    "plt.figure(figsize = (20, 20))\n",
    "plot_importance(best_model, max_num_features=15, importance_type='gain', height=0.3)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my X value is: 28591\n",
      "(753, 28591)\n",
      "my header list is: 28591\n",
      "my X value is: 57454\n",
      "(753, 28863)\n",
      "my header list is: 57454\n",
      "my X value is: 86547\n",
      "(753, 29093)\n",
      "my header list is: 86547\n",
      "my X value is: 115738\n",
      "(753, 29191)\n",
      "my header list is: 115738\n",
      "my X value is: 144631\n",
      "(753, 28893)\n",
      "my header list is: 144631\n",
      "my X value is: 173880\n",
      "(753, 29249)\n",
      "my header list is: 173880\n",
      "my X value is: 202979\n",
      "(753, 29099)\n",
      "my header list is: 202979\n",
      "my X value is: 232057\n",
      "(753, 29078)\n",
      "my header list is: 232057\n",
      "my X value is: 260926\n",
      "(753, 28869)\n",
      "my header list is: 260926\n",
      "my X value is: 289773\n",
      "(753, 28847)\n",
      "my header list is: 289773\n",
      "my X value is: 318745\n",
      "(753, 28972)\n",
      "my header list is: 318745\n",
      "my X value is: 347724\n",
      "(753, 28979)\n",
      "my header list is: 347724\n",
      "my X value is: 377090\n",
      "(753, 29366)\n",
      "my header list is: 377090\n",
      "my X value is: 406042\n",
      "(753, 28952)\n",
      "my header list is: 406042\n",
      "my X value is: 435460\n",
      "(753, 29418)\n",
      "my header list is: 435460\n",
      "my X value is: 464590\n",
      "(753, 29130)\n",
      "my header list is: 464590\n",
      "my X value is: 493708\n",
      "(753, 29118)\n",
      "my header list is: 493708\n",
      "my X value is: 522941\n",
      "(753, 29233)\n",
      "my header list is: 522941\n",
      "my X value is: 552102\n",
      "(753, 29161)\n",
      "my header list is: 552102\n",
      "my X value is: 581083\n",
      "(753, 28981)\n",
      "my header list is: 581083\n",
      "my X value is: 610027\n",
      "(753, 28944)\n",
      "my header list is: 610027\n",
      "dropping value so it doesn't include that in headers\n",
      "my X value is: 620940\n",
      "(753, 10913)\n",
      "my header list is: 620940\n",
      "620940\n"
     ]
    }
   ],
   "source": [
    "#This function essentially returns an array of dataframe headers the length of OHE'd input SNPs for training data\n",
    "#EG. It will be able to determine that feature 357310 is Gm13_17683957 but not what allele it is\n",
    "#eg. feature 357309 357310 and 357311 may all be one hot encoded versions of all possible values of Gm13_17683957\n",
    "#iterating through the saved OHE will by able to determine what specific allele the feature is but cannot determine\n",
    "#what SNP header it belongs to. Therefore combining these two methods you can determine both allele and SNP\n",
    "snp = []\n",
    "imp = SimpleImputer(missing_values='./.', strategy='most_frequent')\n",
    "fs_ohe = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
    "x = 0\n",
    "n_headers = []\n",
    "le = LabelEncoder()\n",
    "#while (i < 10):\n",
    "for chunk in pd.read_csv(\"Oil_Merged_filtered.csv_train_test.csv_5pcnt.csv\", chunksize=10000, index_col=\"Unnamed: 0\"):\n",
    "    chunk = chunk.T\n",
    "    if 'Value' in chunk.columns:\n",
    "        print(\"dropping value so it doesn't include that in headers\")\n",
    "        chunk = chunk.drop(columns=['Value'])\n",
    "    headers = chunk.columns\n",
    "    row_idx = chunk.index\n",
    "    chunk = imp.fit_transform(chunk) #SHOULD TURN ./. into the most common for each column\n",
    "    #since imputing makes a numpy array have to turn back into PD for label encoding\n",
    "    chunk = pd.DataFrame(data = chunk, index = row_idx, columns = headers)\n",
    "    chunk = chunk.apply(lambda col: le.fit_transform(col))\n",
    "    c_headers = chunk.columns\n",
    "    y = 0\n",
    "    for column in chunk:\n",
    "        d = (chunk[column].nunique())\n",
    "        n_headers.extend([c_headers[y] for i in range(d)])\n",
    "        #print(n_headers)\n",
    "        #print(l)\n",
    "        #n_headers.append(c_headers[y] * d)\n",
    "        #print(n_headers)\n",
    "        y = y + 1\n",
    "    #to double check that it would indeed be one hot encoded with this amount of columns\n",
    "    chunk = fs_ohe.fit_transform(chunk)\n",
    "    x = x + chunk.shape[1]\n",
    "    print(\"my X value is: \" + str(x))\n",
    "    print(chunk.shape)\n",
    "    print(\"my header list is: \" + str(len(n_headers)))\n",
    "print(len(n_headers))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##IGNORE THIS WAS OLD WORK IN WHICH THE SCORES HAD TO BE MANUALLY ENTERED\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "figure(num=None, figsize=(10, 10), dpi=100, facecolor='w', edgecolor='k')\n",
    "fs = [593927,29855,588433,594046,588431,154384,89664,238306,29856,133635,212368,97360,310115,214640,238315]\n",
    "scores = [3788.51,3012.53,2338.82,1817.61,1267.10,956.13,721.52,464.87,423.59,387.93,344.70,308.57,249.15,241.69,225.54]\n",
    "snp_label = []\n",
    "for jj in fs:\n",
    "    jj_allele = find_snp_from_header(ohe, jj)\n",
    "    this_snp = (n_headers[jj] + ' ('+str(jj_allele)+')')\n",
    "    print(this_snp)\n",
    "    snp_label.append(this_snp)\n",
    "snp_label.reverse()\n",
    "scores.reverse()\n",
    "print(len(scores))\n",
    "print(len(snp_label))\n",
    "plt.barh(snp_label,scores)\n",
    "plt.title('SNP Importance XGBoost Seed Oil')\n",
    "plt.ylabel('SNP Label')\n",
    "plt.xlabel('Relative F_Score (GAIN)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dict = best_model.get_booster().get_score(importance_type=\"gain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming headers to be cleaner and have the appropriate allele\n",
    "def rename_f_header(fn,n_headers,ohe):\n",
    "    fn = fn[1:]\n",
    "    fn = int(fn)\n",
    "    allele = find_snp_from_header(ohe, fn)\n",
    "    this_snp = (n_headers[fn] + ' ('+str(allele)+')')\n",
    "    return this_snp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "#convert feature to actual SNP name\n",
    "i = 0\n",
    "new_dict = {}\n",
    "for key in my_dict:\n",
    "    new_key = rename_f_header(key, n_headers, ohe)\n",
    "    new_dict[new_key] = my_dict[key]\n",
    "    i = i + 1\n",
    "    print(str(i))\n",
    "    if(my_dict):\n",
    "        continue\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gm20_8078669 (A/A)     3788.515620\n",
      "Gm08_9166228 (G/G)      241.692505\n",
      "Gm07_45821320 (A/A)     149.644531\n",
      "Gm16_32413893 (A/A)      79.890625\n",
      "Gm03_32211971 (G/G)      16.695312\n",
      "                          ...     \n",
      "Gm19_2899158 (C/C)        0.097059\n",
      "Gm03_8415236 (C/C)        0.048430\n",
      "Gm15_47314687 (C/C)       0.050762\n",
      "Gm20_41787717 (A/A)       0.072517\n",
      "Gm13_30712121 (G/G)       0.094101\n",
      "Length: 1893, dtype: float64\n",
      "                     F_Score(GAIN)\n",
      "Gm20_8078669 (A/A)     3788.515620\n",
      "Gm08_9166228 (G/G)      241.692505\n",
      "Gm07_45821320 (A/A)     149.644531\n",
      "Gm16_32413893 (A/A)      79.890625\n",
      "Gm03_32211971 (G/G)      16.695312\n",
      "...                            ...\n",
      "Gm19_2899158 (C/C)        0.097059\n",
      "Gm03_8415236 (C/C)        0.048430\n",
      "Gm15_47314687 (C/C)       0.050762\n",
      "Gm20_41787717 (A/A)       0.072517\n",
      "Gm13_30712121 (G/G)       0.094101\n",
      "\n",
      "[1893 rows x 1 columns]\n"
     ]
    }
   ],
   "source": [
    "#put into dataframe\n",
    "new_fi = pd.Series(new_dict)\n",
    "print(new_fi)\n",
    "df = new_fi.to_frame()\n",
    "df = df.rename(columns = {0:'F_Score(GAIN)'})\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot dataframe now with top 20 SNPS\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "figure(num=None, figsize=(10, 10), dpi=100, facecolor='w', edgecolor='k')\n",
    "indexes = df.nlargest(20, \"F_Score(GAIN)\").index\n",
    "values = df.nlargest(20, \"F_Score(GAIN)\").values.ravel()\n",
    "indexes = indexes[::-1]\n",
    "values = values[::-1]\n",
    "plt.barh(indexes, values)\n",
    "plt.title('SNP Importance XGBoost Seed Oil')\n",
    "plt.ylabel('SNP Label')\n",
    "plt.xlabel('Relative F_Score (GAIN)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IP/30E4qLi3Hz5k3ExcVh1apVmDp1Kho0aIDWrVvj7t27AICBAweWWUbpNd6/fx+CIGDx4sVYvHhxmXnT0tJgaGiIf//9V2aZR2l7Xqe0vuzs7ArzZWdnvzbIJyJiAE9ERPSeqKmpwd7eHvb29jA1NcV3332H/fv3y6whLzVs2DDs3r0bK1euRKdOncot99WA7G2VF/yXly68sqnZ+1CZtcWl7t+/j0GDBqFx48aYNGkSjIyMoKqqiiNHjmDdunUyG41VZWS/IiUlJTA3Nxc3ZntVvXr1XltGVlYWQkNDYWNjgw0bNuDTTz/F9OnTsWPHDsmsiMaNG+PatWsoLCyUpFtaWla53crKymjZsiU2bNiAe/fuSR6cVFbpLISKjoeFheHcuXOIjY3FsWPHMHnyZKxduxZbt25960BVRUVF3PjOwcEBfn5+iI6ORuvWrcXf57x586Cvr1/muQDE38XXX3+Ntm3bllnPqw8q3oSJiQlq1KiB69evl5unoKAAd+7ckcxkISIqCwN4IiKiD6D0L+ZPnjwpN4+JiQk+/fRTbN26tczRvurq3r17Mml3796Furq6uMmcuro67ty5I5Pv9u3bUFZWLnNU/FXlBY2HDx9GQUEBli9fLplNUNkp7GUxMTEpc8fyV/Ncu3YNrq6urw1oy/Pzzz8jJSUFy5Ytg5aWFqZOnYqhQ4di7dq1kpkY7du3x7lz5/DHH3+ge/fub1TXy4qLiwFAHNUv3SAuKytLMgp/+/Zt8XjpP0tKSnDv3j3JbvipqanIyMiQ2SHewcEBDg4OGDt2LKKjo/HNN99g7969+Pzzz9/4nr3q1T9bpcs06tSpg9atW5d7Xmk+VVXVCvMBL2aplPU7L+s3/SoNDQ20bNkSf/31F5KSksrcRX/v3r0oKChQqNf6EZF8cA08ERHRO/TXX3+VOUJduja4cePGFZ4/bNgwFBUVYdWqVe+lfe/D2bNncfnyZfH7o0eP8Oeff8LNzQ0qKipQUVGBm5sb/vzzT8mr7lJTUxETE4PmzZvLTN0uS+nrt15d0146ovryfc/MzMTvv//+xtfUpUsXXLt2Tdw1/WWl9XTr1g3JycmS9del8vLyypzy/rJLly7h119/ha+vrxiEenh4oHPnzli2bJk4HR148a7wunXr4scffywzaKzKrIjCwkKcOHECqqqqYhDu7u6O4uJibNq0SZJ33bp1UFJSgru7OwCgXbt2AID169dL8q1du1ZyPD09XaZNVlZWACC+bq60PzMyMirV7sTExDLX05f+2Sqdzt62bVtoaWkhMjKyzPylyzrq1KkDFxcXbN26tcwHay+/LrBdu3Y4d+4cLly4IDkeHR1dqbYPGzYMgiBg0qRJyMvLkxx78OAB5s+fD319ffTr169S5RHRx4sj8ERERO9QaGgocnNz0blzZzRu3BiFhYU4c+YM9u3bB2NjY3z22WcVnl86Cr9jx44P1OK3Z25ujsGDB0teIwcAwcHBYp4xY8YgPj4eX375Jb788kuoqKhg69atKCgowIQJEypVj42NDQBg0aJF6N69O1RVVeHh4QE3Nzeoqqpi6NCh8PHxQXZ2Nn777TfUqVMHKSkpb3RNgwcPxoEDBzB69Gj06dMHNjY2SE9Px+HDhzFjxgxYWlqiZ8+e2LdvH6ZNm4aTJ0/CyckJxcXFuH37Nvbv349Vq1aVu9ShuLgYISEhqFu3LsaMGSM59v3336N79+6YNWsWIiIiALx4ReCSJUswdOhQ9OzZE15eXrC1tYWqqioePXqE/fv3Ayh7ff/Ro0fFkfTSoPPu3bsIDAwUH5x06NABLVu2xKJFi5CUlAQLCwucOHECf/75JwYOHChOJbe0tETv3r2xdetWZGRkwNnZGRcvXsSOHTvQqVMncQO/HTt2YPPmzejUqRNMTEyQnZ2Nbdu2QUtLS3wYUKtWLTRp0gT79u2DqakpdHV10bRp03Jfv7hy5UpcvnwZnTt3Fl83d+XKFezcuRO6urrimnctLS1Mnz4dEydOxGeffYbu3btDT08P//77L44cOQInJyeEhIQAAKZNm4Yvv/wSPXr0wBdffIGGDRsiNTUV586dw+PHj7F7924AwJAhQ7Br1y4MGTIEfn5+4mvk6tevX+HU+FLOzs749ttv8eOPP+LTTz9F7969oa+vj9u3b+O3335DSUkJVqxYIW5mSERUHgbwRERE79DEiROxf/9+HDlyBFu3bkVhYSHq16+PL7/8EsOGDYO2tvZryyhdC186zbm6c3Z2hoODA5YuXYp///0XTZo0wY8//ihZn920aVNs2rQJCxYsQGRkJARBgL29PX766adKLxewt7fH6NGjsWXLFhw7dgwlJSX4888/0bhxY4SFheHnn3/G3LlzUbduXfTv3x96enqS95dXhaamJjZt2oTw8HD88ccf2LFjB+rUqQNXV1dxt39lZWUsXboU69atw65du/DHH39AXV0dDRo0wIABAyrc4Gzjxo24fPkywsLCZGYfGBkZITg4GHPnzsUff/wh7oTv6OiImJgYrF27FkeOHMHevXtRUlICQ0NDNG/eHLNmzZLZ/A4AwsLCxH+vWbMmGjdujOnTp8PHx0dMV1ZWxvLlyxEWFoa9e/di+/btMDY2xsSJE8Ud5kuFhoaiQYMG2LFjBw4dOoS6desiKChIsreDi4sLLl68iL179yI1NRW1a9eGvb095s+fL3kTQWhoKGbNmoUff/wRhYWFGDlyZLkBfFBQEGJiYnD69GlER0cjLy8P+vr68PLywvDhwyXl9ujRAwYGBlixYgVWr16NgoICGBoaokWLFpKHaE2aNMHvv/+OJUuWYMeOHXj+/Dn09PRgbW2NESNGiPkMDAywYcMGhIaGYsWKFdDV1YWPjw8MDAzw/fffl93Jrxg0aBBsbW2xZs0arF+/HllZWdDX10fXrl0xdOjQMqfWExG9Skn4EDvREBER0X+ShYUFfH19xRFNIiIien+4Bp6IiIiIiIhIATCAJyIiIiIiIlIADOCJiIiIiIiIFADXwBMREREREREpAI7AExERERERESkABvBERERERERECoDvgSeSg7Nnz0IQBKiqqsq7KUREREREJEeFhYVQUlKCo6Pja/NyBJ5IDgRBED8kP4IgoKCggP0gR+yD6oH9IH/sA/ljH1QP7Af5Yx98eFWJCzgCTyQHqqqqKCgoQJMmTaChoSHv5ny0cnJycPXqVfaDHLEPqgf2g/yxD+SPfVA9sB/kj33w4V28eLHSeTkCT0RERERERKQAGMATERERERERKQAG8EREREREREQKgAE8ERERERERkQJgAE9ERERERESkABjAExERERERESkABvBERERERERECoABPBEREREREZECYABPREREREREpAAYwBMREREREREpAAbwRERERERERAqAATwRERERERGRAmAAT0RERERERKQAGMATERERERERKQAG8EREREREREQKgAE8ERERERERkQJgAE9ERERERESkABjAExERERERESkABvBERERERERECoABPBEREREREZECYABPREREREREpAAYwBMREREREREpAAbwRERERERERAqAATwRERERERGRAmAAT0RERERERKQAGMATERERERERKQAG8EREREREREQKgAE8ERERERERkQJgAE9ERERERESkABjAExERERERESkABvBERERERERECoABPJEcKSkpybsJHzUlJSWoq6uzH+SIfVA9sB/kj31ARESVUUPeDSD6WKmpqUFdXV3ezfioqaurw9raWt7N+KixD6oH9oP8sQ9eUlwMqKjIuxVERNUSA3giefL1Ba5elXcriIiIqgcrK2DTJnm3goio2mIATyRPV68CZ8/KuxVERERERKQAuAaeiIiIiIiISAEwgCciIiIiIiJSAAzgiYiIiIiIiBQAA3giIiIiIiIiBcAAnoiIiIiIiEgBMIAnIiIiIiIiUgDVMoA/cuQIAgIC0KpVK9jY2KB169YIDAxETEwMSkpK3kkdJ06cwPjx49GpUydYWFhg5syZZeZLTk7GmDFj0Lx5czg6OmLo0KF48OBBleqaNGkSLCwsZD5Hjx4V89y+fRszZ85E9+7d0axZM3To0AHTpk3D06dPJWVt3769zLLmz58vyZebm4sFCxagY8eOaNasGTw9PREREYGioiJJvn/++QdBQUFo1aoVWrRoAV9fX/z111+SPOHh4WXWuXnzZkm+b775Bl26dIGDgwOcnZ3h6+uL48ePS/I8ePAAQUFBcHd3h52dHdq0aYNRo0bhzp07lbqX+fn5aNeuHeLi4iTphYWF2LRpE/r164fmzZvD1tYWHTp0wMSJE/H333+XWdb48eMxefLkKpeRlZUFFxeXcsslIiIiIiJ6H6rde+AXLlyIyMhIdO7cGSEhIdDX10dqaioOHTqECRMmQEdHB23btn3reo4dO4Zr167B2dkZ6enpZeYpLi7GkCFDkJubi1mzZkFNTQ1LlizBwIEDER0dDU1NzUrX17BhQ5kg28zMTPz3+Ph4JCYmol+/frC0tMS///6LsLAwnDp1Crt27YKamprk3FWrVqF27drid0NDQ8nxmTNn4uDBgxg3bhzMzMxw7tw5hIWFITc3F2PHjgUAPH36FIMGDULDhg0xe/ZsqKqqYuPGjQgICEBUVBQsLCzE8mrVqoX169fLXNPLCgsLMWjQIJiamiI/Px9RUVEIDAzEhg0b0KJFCwBAdnY26tati3HjxsHIyAgpKSmIjIyEn58fdu3aBT09vQrv4+bNm6GtrY327duLafn5+QgICMDZs2fRr18/DB06FJqamrh37x527tyJL7/8EhcvXpTcw6KiIhw7dgyzZ8+uchlaWlr46quvsGjRIvzyyy8VtpeIiIiIiOidEaqR2NhYwdzcXAgLCyvz+Pnz54XLly+/k7qKi4vFf/fw8BBmzJghkycmJkYwNzcXrl69KqY9fvxYsLW1FdauXVvpur799lvBy8urwjxPnz4VSkpKJGl///23YG5uLuzfv19M+/333wVzc3MhLS2t3LKKi4uFZs2aydzHiRMnCh07dhS/l17fgwcPxLTc3FzBzs5OWLJkiZgWFhYmODg4VHyRZSgqKhLatWsnTJkypcJ8d+7cEczNzYXdu3dXmK+kpETw8PCQufdz5swRLC0thfj4+DLPi4qKEvLz8yVpJ0+eFGxtbYWsrKw3KuPhw4cyv42quHDhgnDhwgVBcHQUBIAffvjhhx9++AFe/H9RTrKzs4XExEQhOztbbm0g9kN1wD748MTYoBKq1RT6tWvXQl9fH8OGDSvzuL29PaytrcXvAwYMQFBQEGJiYtClSxc0a9YMQ4cORXp6OpKSkjB48GA4OjrCy8sLJ0+elJSlrPz6S79y5Qr09fVhaWkpphkaGqJp06Y4fPjwG15l2T755BMoKSlJ0kqv9cmTJ1UqSxAEFBUVSUboAaB27doQBEH8XlhYKKaXqlmzJlRVVSX53pSKigpq164t1lMeXV1dSXvKc+rUKSQlJcHT01NMy8vLw5YtW9ClSxe4urqWeV6fPn1kZjDExsbCxcUFmpqab1SGsbEx7O3tsX379grbTERERERE9K5Umyn0RUVFOHPmDDw9PVGjRuWbdeXKFTx79gwTJ05EVlYWQkNDMXXqVCQlJaFXr17w9/dHZGQkgoODERsbW6Vp7/n5+TKBHwCoqanh9u3blS4HAO7du4fmzZsjPz8f5ubmGD58ODp16lThOaVrrF+eal/K29sbz549Q/369fHFF19gyJAhUFFRAfAicP7ss8/wyy+/wMnJCWZmZjh//jx27dqF4cOHi2V4eHigbt26mDNnDsaOHYsaNWpgzZo1UFJSQs+ePSX15eXloVWrVsjIyICpqSkGDRqEL774QqZdgiCguLgYmZmZ2L59O+7du1fm/gIlJSUoLi5GcnIyFi1aBCMjI3Tu3LnC+xEfHw8jIyMYGRmJaZcuXUJOTg7atGlT4bmvio2NxVdfffVWZTg6OiI+Pr5K5xAREdHr5ebmvpPBhKrW+fI/ST7YD/LHPvjwBEGQGcwtT7UJ4J8/f46CggJJcAb8X0BYSllZWTJ6npWVhYiICHHt9PXr17FmzRpMnz4d/fv3BwAYGBigR48eSEhIeG3Q/DJTU1M8fvwYycnJ4hrz7Oxs3Lx5E3l5eZUux8rKCnZ2dmjSpAkyMzOxefNmjBgxAosXL0bXrl3LPCc/Px9z586FtbW1ZFRYX18fwcHBaNasGZSUlHD48GH8/PPPSE5ORkhIiJhv2rRpmDZtGj7//HMxLSgoCP7+/uJ3HR0dbNq0CUFBQeK+Arq6uli5cqVkfbuJiQm++eYbWFtbIz8/H9HR0Zg6dSoyMzMxePBgSbujoqIwZcoUAICGhgYWLVoER0dHmeubOHEioqOjxfLXrl0rM2PgVZcuXZKsywf+b3ZCvXr1JOklJSWSDQ9VVFTEPxT379/HnTt3xHX0b1IGAFhaWmLDhg3IysqClpZWhW0nIiKiyrtz547cgoe7d+/KpV6SYj/IH/vgwypr4Lgs1SaAL/Xqk4cDBw5g9OjR4ndfX19JoGppaSnZ+MzU1BQA0Lp1a5m0x48fV6kt3t7eWLx4MSZPnozp06dDVVUVc+fORU5OjjjaXRkDBw6UfO/QoQN8fHwQFhZWbgA/bdo0PHz4EFu2bJHck7Zt20o28WvTpg1q1qyJ9evXY+jQoTAwMAAAzJ8/H3FxcQgNDYWpqSnOnTuHpUuXQltbG0OGDAEApKWlYeTIkTAxMcHkyZOhoqKCbdu2YdiwYdi0aZM48v/qaHz79u1RWFiI5cuXw8/PD6qqquKxjh07wtLSEs+ePcP+/fsxZswYLFmyBO3atZOUMXr0aPj5+eHRo0dYv349/P398euvv6J+/frl3scnT57A1ta2zGOv/m5CQ0OxadMm8fvLD0sOHz4Mc3NzNGjQ4I3LAF4sexAEAWlpaQzgiYiI3qFGjRrJZQT+7t27MDU1hbq6+getm/4P+0H+2Acf3s2bNyudt9oE8Lq6ulBTU5MJsl1dXREVFQUAZa6N19bWlnwvDSZfHs0tfZqRn59f5TYtXLgQkydPFkfunZ2d0atXL5lXrVWFsrIyunTpgp9++gl5eXmoVauW5PiiRYsQHR2NiIgImJubv7a8bt26Yc2aNbh69SoMDAzwzz//YM2aNVi+fDk6dOggtruoqAiLFy+Gj48PtLS0sGrVKqSnp2P79u3iPXJ1dYWXlxeWLVuGBQsWVFjngQMHcP/+fckUfz09PfGBiru7O9LT0/HTTz/JBPANGzZEw4YNYW9vD3d3d3Tp0gWrVq2SPJx5VUFBgeRhAQDxgcWrv5shQ4agd+/eSElJkfndxMbGSnaxf5MygP/7XVVlNgYRERG9njyDBnV1dWhoaMitfnqB/SB/7IMPp7LT54Fq9B74GjVqwMnJCQkJCZIp8zo6OrCzs4OdnV2lpxW8S23btkVcXBz27t2LQ4cO4ZdffkFqaiocHBzeS30bN25EZGQkZs+e/cavyyt9gmNlZSVJt7a2RkFBAZKTk8V8jRs3ltxXFRUVWFhY4P79+294BVI2Nja4d+9ehXnU1dVhZmb22nw6OjrIzMyUpNna2kJDQwMnTpyQpNevXx92dnYyD0CysrLw999/SwL4qpZRKiMjA8D/bcJHRERERET0PlWbAB4A/P398eTJE0RERMi7KRIqKiowMzNDw4YNcevWLcTHx0vWlldVSUkJ9u/fj6ZNm0pG32NiYjB79myMGzcOvXr1qnR5e/fuhYqKirhrvbGxMQDg8uXLknyXLl2CkpKSOE29fv36uHXrlmRmQnFxMa5duyaWUVGd2traMDExqTDf33//LfO++FdlZWXh+vXrr83XqFEjPHz4UJJWq1Yt+Pj44MCBAzJvGijLsWPHoKWlJVmXX9UySiUlJaF27drQ19ev9DlERERERERvqtpMoQderK0ODAxEWFgYrl27hm7dusHAwACZmZlITExESkpKlXaRr0hSUhIuXrwI4MU6j/v372P//v0AIFnn/NNPP8HBwQFaWlq4fv06li9fjl69epX7urGy6pk0aRK8vLzwv//9D+np6di8eTMuXbqE8PBwMd+pU6cwadIktGrVCi4uLjh37px4rF69euIGa4MHD0bLli3Fzdz+/PNPbNu2DX5+fmIgaWtrC1tbW0ybNg1paWkwMTHBhQsXsGLFCvTp00eclvb5558jKioKw4cPh6+vL1RUVLB161bcu3cPoaGhYv2fffYZevXqhcaNGyMvLw/R0dE4ePAgJk+eLE5pj4uLw86dO9G+fXsYGRkhPT0dMTExOH78OBYuXCiWFR4ejszMTDg5OUFPTw9JSUnYuHEjCgoKZPYKeJWTkxP27duHwsJCyVT6MWPG4PLlywgICEC/fv3g5uYGTU1NpKWl4cCBAwAgTv+JjY2Fu7u7zGsEq1JGqUuXLsHR0bFSryQkIiIiIiJ6W9UqgAeA8ePHo3nz5ti0aRNmzJiBrKws6OjowMbGBj/88AO8vLzeST0nT57Ed999J34/duwYjh07BuDFTvalHj9+jOnTpyM9PR0NGjTA0KFD4efnV+l6NDU1oaWlheXLlyMtLQ2qqqqwtbXFypUrJVPkT548icLCQiQkJCAhIUFSxsiRIxEcHAzgxSj077//jsePH6OkpASmpqaYPHkyBgwYIOZXUVFBREQEFi9ejMjISKSlpaFevXoYMmQIAgICxHy2trZYtWoVli1bhu+++w4lJSVo0qQJVqxYAWdnZzGfiYkJ1q1bh9TUVCgpKcHc3Bw//fQTPv30UzFPw4YNUVBQgAULFuDZs2f45JNPYGFhgY0bN8LFxUXMZ21tjXXr1mHXrl3IycmBoaEhnJ2dsXjx4teOwHfs2BEzZ87EqVOn4ObmJqbXrFkTq1evxtatW7F7925ERUWhsLAQBgYGaN68OX799Vc0b94cJSUlOHr0aJnr7CtbRqnSvpowYUKFbSYiIiIiInpXlIQPvcUn0VsIDg6GlpYWfvzxxyqfe+bMGfj5+SEhIeG1r6x7nbi4OIwfPx5Hjx59o1khpbM/7AYOBM6efau2EBER/Wc4OgJnzsil6pycHFy9ehVWVlbcuEuO2A/yxz748MTYwM7utXk595cUyvDhw7Fv3z6kpqZW+VwnJydcunTprYN3AFizZg38/f3f2ZIOIiIiIiKi16l2U+gVTVFRUbnHlJSUqvS+eHo9KysrTJ48GY8ePULdunXl0obs7Gy4uLhg0KBBcqmfiIiIiIg+Tgzg35KNjU25x4yNjXH48OEP2JqPwxdffCHX+jU1NTFy5Ei5toGIiIiIiD4+DODfUlRUVLnH5PHeeiIiIiIiIvpvYgD/liqz0QARERERERHR22IATyRPVlbybgEREVH1wf8vEhFViAE8kTxt2iTvFhAREVUvxcUANwEmIioTXyNHJCcFBQXIzc2VdzM+arm5ubhy5Qr7QY7YB9UD+0H+2AcvYfBORFQuBvBEciQIgryb8FETBAG5ubnsBzliH1QP7Af5Yx8QEVFlMIAnIiIiIiIiUgAM4ImIiIiIiIgUAAN4IiIiIiIiIgXAAJ5IjpSUlOTdhI+akpIS1NXV2Q9yxD4gIiIiqjy+Ro5ITtTU1KCuri7vZnzU1NXVYW1tLe9mfNT+k33AV2ARERHRe8IAnkiefH2Bq1fl3QoielesrIBNm+TdCiIiIvqPYgBPJE9XrwJnz8q7FUREREREpAC4Bp6IiIiIiIhIATCAJyIiIiIiIlIADOCJiIiIiIiIFAADeCIiIiIiIiIFwACeiIiIiIiISAFU+wD+yJEjCAgIQKtWrWBjY4PWrVsjMDAQMTExKCkpeSd1nDhxAuPHj0enTp1gYWGBmTNnlpkvOTkZY8aMQfPmzeHo6IihQ4fiwYMHVapr0qRJsLCwkPkcPXpUzHP79m3MnDkT3bt3R7NmzdChQwdMmzYNT58+lZS1ffv2MsuaP3++JF9ubi4WLFiAjh07olmzZvD09ERERASKiook+f755x8EBQWhVatWaNGiBXx9ffHXX39J8oSHh5dZ5+bNmyX5vvnmG3Tp0gUODg5wdnaGr68vjh8/Lsnz4MEDBAUFwd3dHXZ2dmjTpg1GjRqFO3fuVOpe5ufno127doiLi5M59vTpU9jY2MDR0RF5eXnllnHnzh1YWFjg33//laSHhobCwsICS5culTknKysLLi4u+PvvvyvVTiIiIiIionehWr9GbuHChYiMjETnzp0REhICfX19pKam4tChQ5gwYQJ0dHTQtm3bt67n2LFjuHbtGpydnZGenl5mnuLiYgwZMgS5ubmYNWsW1NTUsGTJEgwcOBDR0dHQ1NSsdH0NGzaUCbLNzMzEf4+Pj0diYiL69esHS0tL/PvvvwgLC8OpU6ewa9cuqKmpSc5dtWoVateuLX43NDSUHJ85cyYOHjyIcePGwczMDOfOnUNYWBhyc3MxduxYAC8C3kGDBqFhw4aYPXs2VFVVsXHjRgQEBCAqKgoWFhZiebVq1cL69etlrullhYWFGDRoEExNTZGfn4+oqCgEBgZiw4YNaNGiBQAgOzsbdevWxbhx42BkZISUlBRERkbCz88Pu3btgp6eXoX3cfPmzdDW1kb79u1lju3duxdFRUUoKirC4cOH0b179zLLiI2NhYWFBerXry+mFRcXY9++fQCAmJgYjBgxQnKOlpYWvvrqKyxatAi//PJLhW0kIiIiIiJ6V6ptAB8XF4fIyEiMHDkSwcHBkmPdunXDwIEDUaPGu2n+xIkTMWnSJADAyZMny8yzf/9+/PPPP9i1axcsLS0BAHZ2dujUqRN+++03DBo0qNL11apVCw4ODuUe9/Lygq+vL5SUlMS0//3vf+jfvz9iY2Ph6ekpyW9jY1NusFtSUoJ9+/Zh8ODB8PX1BQC0atUKd+7cwZ49e8QAPiEhAWlpadi2bRsaNGgAAHBxcYGLiwsOHTokCeCVlZUrbD8ALF68WPLd3d0dHTt2xK5du8QA3tLSErNnz5bks7W1haenJ06cOIEePXqUW74gCNiwYQP8/PzKPB4TEwMzMzNkZWVh9+7dFQbwHh4ekrSEhASkpqaidevWiI+Px+XLl2FjYyPJ06dPHyxduhTXrl0Tfw9ERERERETvU7WdQr927Vro6+tj2LBhZR63t7eHtbW1+H3AgAEICgpCTEwMunTpgmbNmmHo0KFIT09HUlISBg8eDEdHR3h5eckE6crKr78NV65cgb6+viRYMzQ0RNOmTXH48OE3vMqyffLJJ5LgHYB4rU+ePKlSWYIgoKioSDJCDwC1a9eGIAji98LCQjG9VM2aNaGqqirJ96ZUVFRQu3ZtsZ7y6OrqStpTnlOnTiEpKUnmYQbwYmr+2bNn0aNHD3h5eeH48eN4/vy5TL6MjAycOXNGJoCPiYmBpqYm5syZA1VVVURHR8uca2xsDHt7e2zfvr3CdhIREREREb0r1TKALyoqwpkzZ9CqVasqjbJfuXIFGzZswMSJEzFjxgwkJiZi6tSpGDVqFNq3b4/w8HDo6ekhODgY2dnZVWpTfn6+zNR1AFBTU8Pt27erVNa9e/fQvHlz2Nra4rPPPsOhQ4dee07peuuXp9qX8vb2hpWVFTp27IjIyEgUFxeLx1RUVPDZZ5/hl19+wYULF5CdnY34+Hjs2rULX331lZjPw8MDdevWxZw5c/DkyRM8ffoUCxYsgJKSEnr27CmpLy8vD61atYK1tTW6d++Obdu2ldnm0ocHz549w+rVq3Hv3j3069dPJl9JSQkKCwvx8OFDzJo1C0ZGRujcuXOF9yM+Ph5GRkYwMjKSORYTEyPeF29vbxQWFmL//v0y+Y4dOwYdHR3Y29uLafn5+Th48CA6d+4MQ0NDtGnTBnv27ClzvwVHR0fEx8dX2E4iIiIiIqJ3pVpOoX/+/DkKCgpkgjNBECTBqbKysmT0PCsrCxEREeJ08uvXr2PNmjWYPn06+vfvDwAwMDBAjx49kJCQgE6dOlW6Taampnj8+DGSk5PFNebZ2dm4efNmhZukvcrKygp2dnZo0qQJMjMzsXnzZowYMQKLFy9G165dyzwnPz8fc+fOhbW1NVxdXcV0fX19BAcHo1mzZlBSUsLhw4fx888/Izk5GSEhIWK+adOmYdq0afj888/FtKCgIPj7+4vfdXR0sGnTJgQFBYn7Cujq6mLlypWS9e0mJib45ptvYG1tjfz8fERHR2Pq1KnIzMzE4MGDJe2OiorClClTAAAaGhpYtGgRHB0dZa5v4sSJ4ii3iYkJ1q5dKzNj4FWXLl2STOt/2Z49e+Dg4CC2u3HjxoiOjoaPj48kX2xsLNzd3SW/ocOHDyM7Oxve3t4AgB49eiA2NhYnT56U3HvgxRKADRs2ICsrC1paWhW2l4g+Lrm5ue9k9tKHkpubK/knfXjsA/ljH1QP7Af5Yx98eIIgyMzALk+1DOBLvXoRBw4cwOjRo8Xvvr6+kkDV0tJSshbc1NQUANC6dWuZtMePH1epLd7e3li8eDEmT56M6dOnQ1VVFXPnzkVOTg5UVFQqXc7AgQMl3zt06AAfHx+EhYWVG8BPmzYNDx8+xJYtWyT3pG3btpJN/Nq0aYOaNWti/fr1GDp0KAwMDAAA8+fPR1xcHEJDQ2Fqaopz585h6dKl0NbWxpAhQwAAaWlpGDlyJExMTDB58mSoqKhg27ZtGDZsGDZt2iSO/L86Gt++fXsUFhZi+fLl8PPzg6qqqnisY8eOsLS0xLNnz7B//36MGTMGS5YsQbt27SRljB49Gn5+fnj06BHWr18Pf39//Prrr5KN5V715MkT2NrayqRfu3YNN27cEB8cAC/2FFiyZAn+/fdfsczi4mIcO3ZM5o0DMTExqFOnjvib6dChAzQ0NBAdHS0TwH/yyScQBAFpaWkM4IlI4s6dOwr5F5+7d+/KuwkfPfaB/LEPqgf2g/yxDz6ssmZ7l6VaBvC6urpQU1OTCbJdXV0RFRUFAGWujdfW1pZ8Lw0mXx7NLb0x+fn5VW7TwoULMXnyZHHk3tnZGb169ZJ51VpVKCsro0uXLvjpp5+Ql5eHWrVqSY4vWrQI0dHRiIiIgLm5+WvL69atG9asWYOrV6/CwMAA//zzD9asWYPly5ejQ4cOYruLioqwePFi+Pj4QEtLC6tWrUJ6ejq2b98u3iNXV1d4eXlh2bJlWLBgQYV1HjhwAPfv35dM8dfT0xMfqLi7uyM9PR0//fSTTADfsGFDNGzYEPb29nB3d0eXLl2watUqycOZVxUUFEgeFpTavXs3lJWV0aZNG2RkZAAA2rVrh/DwcMTExCAwMBAAcPbsWWRnZ8PNzU08NyMjA0eOHEHPnj0lSyzatm2LP/74A9OnT5f8wSr996rMwCCij0OjRo0UbgT+7t27MDU1hbq6uryb81FiH8gf+6B6YD/IH/vgw7t582al81bLAL5GjRpwcnJCQkICiouLxRFuHR0d2NnZAaj8E4p3qW3btoiLi8Pdu3ehpqaGhg0bIjAw8LU7sr+pjRs3IjIyEnPmzHnj1+WV/hisrKwk6dbW1igoKEBycjK0tLRw8+ZNNG7cWHJfVVRUYGFhgfv377/5RbzExsZG8r77sqirq8PMzAz37t2rMJ+Ojg4yMzMlaYIgYO/evSgpKSlzNkN0dLQYwMfGxsLZ2Vkycn7gwAEUFhYiKipKfFD0sri4OHTp0kX8XvqAoHTjPSKiUor6Fx51dXVoaGjIuxkfNfaB/LEPqgf2g/yxDz6cyk6fB6ppAA8A/v7+CAoKQkREhMx7uOVJRUVFHGW+desW4uPjsXLlyjcur6SkBPv370fTpk0lo+8xMTGYPXs2xo0bh169elW6vL1790JFRUXctd7Y2BgAcPnyZcmeApcuXYKSkpI4pbx+/fr4888/kZ+fj5o1awJ4Mc382rVrMsF/WXVqa2vDxMSkwnx///23zPviX5WVlYXr16+Xubv8yxo1aiSzeWBiYiIePXqE4OBgODs7S44dO3YMK1euxPXr12FhYYG4uDiZDfWio6NhbGyMH3/8Uaa+cePGITo6WhLAJyUloXbt2tDX16+wrURERERERO9CtQ3g27dvj8DAQISFheHatWvo1q0bDAwMkJmZicTERKSkpEBTU/Od1JWUlISLFy8CeDFl5P79++Ku5S+P5P70009wcHCAlpYWrl+/juXLl6NXr14ya6MrqmfSpEnw8vLC//73P6Snp2Pz5s24dOkSwsPDxXynTp3CpEmT0KpVK7i4uODcuXPisXr16qFevXoAgMGDB6Nly5biZm5//vkntm3bBj8/PzGotLW1ha2tLaZNm4a0tDSYmJjgwoULWLFiBfr06SOOEn3++eeIiorC8OHD4evrCxUVFWzduhX37t1DaGioWP9nn32GXr16oXHjxsjLy0N0dDQOHjyIyZMni1Pa4+LisHPnTrRv3x5GRkZIT09HTEwMjh8/joULF4plhYeHIzMzE05OTtDT00NSUhI2btyIgoICmb0CXuXk5IR9+/ahsLBQrDc6OhoaGhrw9/eX+W00bdoU69atQ0xMDDQ0NHDz5k3J6+OSk5Nx+vRpDBs2DC1btpSpz9vbG7/++isyMzPFJRmXLl2Co6NjpV5DSERERERE9LaqbQAPAOPHj0fz5s2xadMmzJgxA1lZWdDR0YGNjQ1++OEHeHl5vZN6Tp48ie+++078fuzYMRw7dgzAi53sSz1+/BjTp09Heno6GjRogKFDh8LPz6/S9WhqakJLSwvLly9HWloaVFVVYWtri5UrV0qmyJ88eRKFhYVISEhAQkKCpIyRI0ciODgYwItR6N9//x2PHz9GSUkJTE1NMXnyZAwYMEDMr6KigoiICCxevBiRkZFIS0tDvXr1MGTIEAQEBIj5bG1tsWrVKixbtgzfffcdSkpK0KRJE6xYsUIymm1iYoJ169YhNTUVSkpKMDc3x08//YRPP/1UzNOwYUMUFBRgwYIFePbsGT755BNYWFhg48aNcHFxEfNZW1tj3bp12LVrF3JycmBoaAhnZ2csXrz4tSP1HTt2xMyZM3Hq1Cm4ubmhsLAQBw4cQKdOncp8sKOnp4d27dohJiYGdevWhZmZmaSO0lfFlTfboXfv3li3bh0OHDiAvn37iv0zYcKECttJRERERET0rigJirTLDtFLgoODoaWlVeaU94p8/fXXsLS0xMSJE9+47ri4OIwfPx5Hjx59o5kgpTM+7AYOBM6efeN2EFE14+gInDkj71ZUWU5ODq5evQorKyuud5QT9oH8sQ+qB/aD/LEPPjwxNvj/+71VhHN/SWENHz4c+/btQ2pqapXOW7NmzVsF76VllDVVn4iIiIiI6H2p1lPoFU1RUVG5x5SUlKr0vnh6PSsrK0yePBmPHj1C3bp1P1i92dnZcHFxwaBBgz5YnURERERERAzg3yEbG5tyjxkbG+Pw4cMfsDUfhy+++OKD16mpqYmRI0d+8HqJiIiIiOjjxgD+HSrr3eGl5PHeeiIiIiIiIvrvYAD/DlVm0wEiIiIiIiKiN8FN7IiIiIiIiIgUAEfgieTJykreLSCid4l/pomIiOg9YgBPJE+bNsm7BUT0rhUXA3zrCBEREb0HnEJPJCcFBQXIzc2VdzM+arm5ubhy5Qr7QY7+k33A4J2IiIjeEwbwRHIkCIK8m/BREwQBubm57Ac5Yh8QERERVR4DeCIiIiIiIiIFwACeiIiIiIiISAEwgCciIiIiIiJSAAzgieRISUlJ3k34qCkpKUFdXZ39IEfsg+qB/SB/7AP5Yx9UD+wH+WMfVG9KAncOIvrgLl68CACws7OTc0uIiIiIiD4u1e2Nr1WJDfgeeCI58vUFrl6VdyuIiIiIiD4OVlbApk3ybsWbYwBPJEdXrwJnz8q7FUREREREpAi4Bp6IiIiIiIhIATCAJyIiIiIiIlIADOCJiIiIiIiIFAADeCIiIiIiIiIFwACeiIiIiIiISAH8JwL4I0eOICAgAK1atYKNjQ1at26NwMBAxMTEoKSk5K3LLy4uxsqVK+Hr64uWLVvCxcUFAwYMQGJiokzegoICzJ07F25ubnBwcIC/vz9u375dpfpyc3OxYMECdOzYEc2aNYOnpyciIiJQVFQkyZeZmYnJkyfDxcUFjo6OGDVqFJ48eSLJ06FDB1hYWJT5OXfunJjv2bNnCAkJQfv27eHg4ABvb29s3ry5zPbFxcXBx8cHDg4OcHZ2xoABA/D48WNJnpKSEqxbtw5du3aFra0t3NzcMH78eJl79dNPP6FNmzawt7dH3759kZCQUOn7NHfuXIwaNarMY8OGDYOFhQV27txZYRk+Pj5YunSpJO3KlSuwsLBA586dyzxnypQpmDJlSqXbSURERERE9C4o/GvkFi5ciMjISHTu3BkhISHQ19dHamoqDh06hAkTJkBHRwdt27Z9qzry8vKwYsUK9O7dGwEBAVBWVsa2bdvg5+eH1atXw9XVVcwbGhqKvXv3YtKkSTA0NERERAQGDRqEPXv2oHbt2pWqb+bMmTh48CDGjRsHMzMznDt3DmFhYcjNzcXYsWPFfGPGjMHNmzcxffp01KxZEz///DMCAgLw+++/o0aNF127ZMkSFBQUSMqfP38+bt26BVtbWzFt9OjRuH37NsaNGwcjIyMcPXoU06dPh4qKCr744gsx365du/D999/j66+/xpgxY5CdnY3ExETk5+dL6ggJCUFsbCyGDx+Opk2bIiUlBX///bckzw8//IBdu3ZhzJgxaNSoEbZv346AgABs3boVNjY2Fd6j5ORk/Prrr9hUxkscnz9/jmPHjgEAYmJi0KtXrzLLePr0Kc6fP4+QkBBJenR0NADg/v37OH/+PJo1ayY5HhAQAC8vLwwZMgSmpqYVtpOIiIiIiOidERRYbGysYG5uLoSFhZV5/Pz588Lly5ffup6ioiLh+fPnMmldu3YVgoKCxLRHjx4JVlZWwpYtW8S0Z8+eCQ4ODsKKFSsqVVdxcbHQrFkzmWuaOHGi0LFjR/H7mTNnBHNzc+HYsWNi2q1btwQLCwthz5495ZafnZ0tODg4CNOnTxfTnjx5Ipibmwu///67JK+vr6/g5+cnuRYnJydh06ZNFV5DfHy8YG1tLVy7dq3cPI8fPxasrKyEDRs2iGklJSWCt7e3MHTo0ArLFwRBWLx4sdCjR48yj23ZskUwNzcXBg0aJFhbWwupqall5tu+fbvQtm1bSVpxcbHg7u4u9O/fX7CzsxNmzZpV5rkDBgwQQkNDX9vO8ly4cEG4cOGC4OgoCAA//PDDDz/88MMPP/zw8yE+jo5v/Ff496Y0NqgMhZ5Cv3btWujr62PYsGFlHre3t4e1tbX4fcCAAQgKCkJMTAy6dOmCZs2aYejQoUhPT0dSUhIGDx4MR0dHeHl54eTJk+J5Kioq0NHRkZStoqICCwsLyZT148ePo6SkBF27dhXTdHV14ebmhqNHj1bqmgRBQFFRkcxofe3atSEIgvj96NGj0NbWhpubm5jWuHFjWFlZVVjXn3/+iZycHPTo0UNMK52a/2qdWlpakjr37duHkpIS9O3bt8Jr2LZtG1xcXGBhYVFunmvXrqG4uFjSfiUlJbRp0wbHjx+XmTXwqp07d8LT07PMYzExMfjf//6HSZMmoaioCHv37i0zX2xsLNq3by9JO336NB4/fgwfHx+0b98ee/fuRXFxscy5Xbt2RXR0tMyyBiIiIiIiovdFYQP4oqIinDlzBq1atRKni1fGlStXsGHDBkycOBEzZsxAYmIipk6dilGjRqF9+/YIDw+Hnp4egoODkZ2dXWH958+fR+PGjcW027dvo06dOjLBvpmZWaXXwauoqOCzzz7DL7/8ggsXLiA7Oxvx8fHYtWsXvvrqK0ldjRo1gpKSkuT8xo0bV1hXTEwMjI2N4eTkJKYZGRmhTZs2iIiIwM2bN5GVlYW9e/fixIkT8PX1FfOdP38ejRo1ws6dO+Hh4QFra2v07NkTR44ckdRRel9mz56NFi1awN7eHoMHD8adO3fEPKUBupqamuRcNTU1FBQU4OHDh+Vew71795CUlCS5hlKPHz/G6dOn4e3tDQsLC5ibmyMmJkYmX2FhIY4fPw4PDw9JenR0NNTV1dGpUyd4e3sjLS0N8fHxMuc7OTnh2bNnuHr1arntJCIiIiKi6ik3Nxc5OTnV4vPyoOnrKOwa+OfPn6OgoABGRkaSdEEQJCOmysrKUFb+v+cUWVlZiIiIgJ6eHgDg+vXrWLNmDaZPn47+/fsDAAwMDNCjRw8kJCSgU6dOZda/atUqJCcnY9CgQWJaRkZGmevctbW1kZ6eXulrmzZtGqZNm4bPP/9cTAsKCoK/v/9r69LR0cGlS5fKLPfZs2c4ceIEvv76a5lj4eHhGDt2LLy8vAC8eJAwZcoUySh3SkoK7ty5g8WLF2PChAnQ19fHpk2bMHz4cOzcuRNNmzYV823fvh1NmjTB/PnzUVhYiEWLFmHw4MHYt28fatasif/9738AgAsXLqBBgwZiHaUb61V0vy5evAgAZY7wx8TEQBAEeHt7AwB69OiBBQsW4P79+zAxMRHznT59GsXFxZL9CwoKCnDw4EF06NABGhoaaN++PWrXro3o6GiZfRSaNGkCFRUVXLhwAXZ2duW2lYiIiIiIqp87d+4gNzdX3s0QvTqwWR6FDeBLvToCfeDAAYwePVr87uvrK9mkzNLSUgzeAYibkLVu3Vom7dWd1UudOHEC4eHhGD58uGQjuHdl/vz5iIuLQ2hoKExNTXHu3DksXboU2traGDJkyBuXu2/fPhQWForBbSlBEPDdd9/h7t27WLBgAfT19REfH48ffvgBOjo6YlAvCAJycnIwf/58dOzYEQDg4uICT09PrFy5EvPmzRPzFRcXY/ny5ahbty6AF7MQvLy8EB0djb59+8Lc3BwtWrTA/PnzYWRkBFNTU2zfvh2nT58GINuvL0tJSYGysjI++eQTmWMxMTGwsbERZ0Z4eXlh4cKFiI6OxogRI8R8sbGxcHV1Ra1atcS0o0ePIj09Xbw/ampq6Ny5M/bv34+8vDxJ3ho1aqB27doyu/4TEREREVH116hRoyqNfL9PN2/erHRehQ3gdXV1oaamJhNku7q6IioqCgDKXBuvra0t+a6qqgpAuv679OnHqzurA8Dly5cRHBwMb29vjBw5UqbsrKwsmXMyMjJkptWX559//sGaNWuwfPlydOjQAQDg7OyMoqIiLF68GD4+PtDS0oK2tnaZDxjS09PLrSsmJkacVv6yuLg47N+/H7t37xZHtVu2bIm0tDTMmTNHDOBL712rVq3Ec1VVVeHs7IwbN25I7kO9evXE4B14MbW/Xr16kh/nnDlzMGbMGPj4+AAAjI2NMXz4cISHh0NfX7/ce5Sfn48aNWrIBPm3bt3C1atXERwcjIyMDAAv+tXW1hYxMTGSAD4uLg6DBw+WnB8dHY3atWvDwcFBPN/DwwPbt2/H4cOH0b17d0l+NTW1Mn8jRERERERUvamrq8u7CaKKBi9fpbABfI0aNeDk5ISEhAQUFxdDRUUFwIsp5KVTmis7DaGy7t27h4CAADg6OiI0NFTmeOPGjZGamioTRN++fVuyVr4ipQGulZWVJN3a2hoFBQVITk6GlpYWGjdujISEBAiCIOnwO3fuyAToAPDvv//izJkzGDduXJl1qqioyJxnZWWF3377Dbm5uVBXV0eTJk3KbffLgWyTJk3KfJDxar6GDRvi999/x8OHD5GXl4dGjRqJGxMaGxuXW5eOjg4KCgqQn5+PmjVrium7d+8G8GI5QHh4uMx5ly9fho2NDW7duoX79+9L1r9nZWUhLi4OeXl5kmn1L5f9agCfmZkJXV3dcttJRERERET0LinsJnYA4O/vjydPniAiIuK91/XkyRN8/fXXMDIyQlhYmDhy/7I2bdpAWVkZBw8eFNPS09Nx/PhxuLu7V6qe0sD18uXLkvRLly5BSUkJ9evXBwC4u7sjPT0dCQkJYp47d+7gypUrZdZVupHbq9PnS+ssLi7G9evXJemXL19GnTp1xKdTpQHvy3UWFBTg9OnTkve2e3h44ObNm0hJSRHTbt26hcePH5f5fvcGDRqgSZMmKCwsRFRUlGTtf1kaNWoEADIb3e3ZswcODg7YsGGD5LN69WqoqqqK73ePjY2FtbU1DA0NxXMPHTqEvLw8zJgxQ+b83r174/jx43j+/LmY/+nTp8jNzRXbQkRERERE9L4p7Ag8ALRv3x6BgYEICwvDtWvX0K1bNxgYGCAzMxOJiYlISUmBpqbmW9eTl5eHgIAAPHv2DN9//71kuriampr4qrp69eqhb9++mDdvHpSVlWFoaIjIyEjUrl1bnCb+Ora2trC1tcW0adOQlpYGExMTXLhwAStWrECfPn3EYNrR0RFt2rTB5MmT8e2336JmzZpYtGgRLCws0KVLF5lyY2Ji4OTkJD4AeJm7uzvq16+PUaNGYcSIETAwMMDx48exY8cOBAcHi/lsbGzg6emJqVOn4vnz59DX18evv/6K1NRUyXT0zz//HBs3bkRQUBCGDx+OwsJCLF68GCYmJuJ0fAD45ZdfoKWlBSMjIyQlJWHt2rWoWbMmAgICKrxH9vb2qFGjBi5dugQzMzMAwNmzZ/HgwQMMGzYMLVu2lDmnffv22LNnDyZOnFjm6+Oio6NhbGyMfv36yUxh0dHRwY4dO7B//36xH0s30mvevHmFbSUiIiIiInpXFDqAB4Dx48ejefPm2LRpE2bMmIGsrCzo6OjAxsYGP/zwgyRgfFOpqam4du0aANl19cbGxjh8+LD4fcqUKdDU1MSCBQuQnZ0NJycnrF27tswd48uioqKCiIgILF68GJGRkUhLS0O9evUwZMgQmcD2559/xo8//oiQkBAUFRWhTZs2mDJlisxr9W7evInr169j2rRpZdappaWFdevWYdGiRZg/fz4yMzPRoEEDTJo0SfLqOuDFuvWFCxdiwYIFyMrKgo2NDdauXSvZEV5LSwvr16/H7NmzMWHCBPH97pMnT5asNSkoKMCSJUvw+PFj6OrqokuXLhg9ejQ0NDQqvEcaGhpo27Ytjh49ip49ewJ48YBCXV293HfD9+rVC3/88Qf++OMPnD17Ft9++614LC0tDQkJCQgMDCxz/YmlpSWsrKwQHR0tBvDHjh1DixYtJOv8iYiIiIiI3iclobpsvUdUBYcPH8b48eMRHx9fpQ0ooqOjMWfOHBw/frxKm0W8rKioCO3bt8c333yDXr16vVEZpSP4Awfa4ezZNyqCiIiIiIiqyNEROHNG3q2QKo0NKvN6aoVeA08fLw8PDzRq1Ai//fZblc7r0aMHTpw48cbBO/BitF9TU7PM/QSIiIiIiIjeFwbwH1hRUVG5n+LiYnk3T2EoKSlh+vTpcnn9g5KSEmbPni2zVIGIiIiIiOh9YgTyAT18+BAdO3Ys97iLiws2btz4AVuk2Ozt7WFvb//B6y1dd09ERERERPQhMYD/gAwMDBAVFVXu8XexYz4RERERERH9NzGA/4DU1NQqtTEBERERERER0asYwBPJkZWVvFtARERERPTxUPS/fzOAJ5KjTZvk3QIiIiIioo9LcTGgoiLvVrwZ7kJPJCcFBQXIzc2VdzM+arm5ubhy5Qr7QY7YB9UD+0H+2Afyxz6oHtgP8vcx9IGiBu8AA3giuRIEQd5N+KgJgoDc3Fz2gxyxD6oH9oP8sQ/kj31QPbAf5I99UL0xgCciIiIiIiJSAAzgiYiIiIiIiBQAA3giIiIiIiIiBcAAnkiOlJSU5N2Ej5qSkhLU1dXZD3LEPqge2A9ERESKga+RI5ITNTU1qKury7sZHzV1dXVYW1vLuxkfNfZB9fCx9oMiv0aIiIg+TgzgieTI1xe4elXerSAi+vhYWQGbNsm7FURERFXDAJ5Ijq5eBc6elXcriIiIiIhIEXANPBEREREREZECYABPREREREREpAAYwBMREREREREpAAbwRERERERERAqAATwRERERERGRAmAAT0RERERERKQA/hMB/JEjRxAQEIBWrVrBxsYGrVu3RmBgIGJiYlBSUvLW5RcXF2PlypXw9fVFy5Yt4eLiggEDBiAxMVEmb0FBAebOnQs3Nzc4ODjA398ft2/frlJ9ubm5WLBgATp27IhmzZrB09MTERERKCoqEvM8ePAAQUFBcHd3h52dHdq0aYNRo0bhzp075ZabnZ0Nd3d3WFhY4OLFizLHMzIyEBoaijZt2sDOzg6dOnXCmjVrxOOZmZkIDg5Ghw4dYG9vj1atWmHIkCG4cOGCTFmJiYkYMGAAnJ2d0bJlSwwZMgRXy3jheX5+PhYvXowOHTrA1tYW7du3x9y5cyt1nzZt2oQ+ffqUeSw0NBQWFhZYunRphWWMHz8ekydPlqQ9ffoUNjY2cHR0RF5ensw5y5cvh7+/f6XaSERERERE9K4ofAC/cOFCBAYGombNmggJCcG6deswdepU1K5dGxMmTMCJEyfeuo68vDysWLECNjY2mDt3LubPnw8dHR34+fkhISFBkjc0NBS//fYbxo4di/DwcBQUFGDQoEHIzMysdH0zZ87Er7/+iq+//hqRkZHo3bs3wsLCEB4eLubJzs5G3bp1MW7cOKxatQqTJk3CnTt34Ofnh6dPn5ZZ7rJly1BcXFzmsZycHAwYMABnzpzB5MmTsWrVKgQEBEAQBDFPQUEB1NTUMGzYMERGRmLWrFnIy8vDwIEDJQ8Obt++jcGDB0NDQwMLFizA7NmzkZ6ejkGDBiElJUXMV1JSguHDh2PPnj0YOXIk1qxZgzFjxkBNTe219yg3NxfLly9HYGCgzLHi4mLs27cPABATE1NuGUVFRTh27Bg8PDwk6Xv37kVRURFycnJw+PBhmfN8fX1x4cIF/PXXX69tJxERERER0TsjKLDY2FjB3NxcCAsLK/P4+fPnhcuXL791PUVFRcLz589l0rp27SoEBQWJaY8ePRKsrKyELVu2iGnPnj0THBwchBUrVlSqruLiYqFZs2Yy1zRx4kShY8eOFZ57584dwdzcXNi9e7fMsZs3bwoODg7C5s2bBXNzc+HChQuS44sWLRI6duwoZGdnV6qdpbKysgQbGxth+fLlYlpkZKRgZ2cn5Obmimn3798XzM3NhR07dohp27ZtE5o3by4kJydXqU5BEISoqCjBxcVFKCwslDl27NgxwdzcXBg0aJBgbm4uXLp0qcwyTp48Kdja2gpZWVmS9H79+gndunUT2rZtK+nfl02aNEkYNmxYldtd6sKFC8KFCxcER0dBAPjhhx9++PnQH0fHN/5P+HuRnZ0tJCYmVvn/w/TusA+qB/aD/LEPPrzS2KAyFHoEfu3atdDX18ewYcPKPG5vbw9ra2vx+4ABAxAUFISYmBh06dIFzZo1w9ChQ5Geno6kpCQMHjwYjo6O8PLywsmTJ8XzVFRUoKOjIylbRUUFFhYWePLkiZh2/PhxlJSUoGvXrmKarq4u3NzccPTo0UpdkyAIKCoqQu3atSXptWvXhiAIFZ6rq6sLACgsLJQ5FhoaCh8fHzRq1KjMc6OiotCnTx9oaGhUqp2lNDQ0ULNmTUmdhYWFUFNTQ82aNSXtf9Vvv/2Grl27wsDAoEp1AsDOnTvRsWNH1KhRQ+ZYTEwMNDU1MWfOHKiqqiI6OrrMMmJjY+Hi4gJNTU0x7cGDBzh79ix69OgBLy8vHD9+HM+fP5c5t2vXrjhy5Ei5sx2IiIiIiIjeNdnoR0EUFRXhzJkz8PT0LDOIK8+VK1fw7NkzTJw4EVlZWQgNDcXUqVORlJSEXr16wd/fH5GRkQgODkZsbKwkuHu1/vPnz6N58+Zi2u3bt1GnTh2ZYN/MzAxRUVGVap+Kigo+++wz/PLLL3BycoKZmRnOnz+PXbt2Yfjw4TL5S0pKUFxcjOTkZCxatAhGRkbo3LmzJM/+/fvxzz//IDw8HJcvX5Yp4+HDh0hJScEnn3yCoUOH4vjx49DQ0ECXLl3w3XffydyDkpISlJSU4OnTp1i9ejWUlZXRq1cv8biXlxdWrVqFn3/+GYMGDUJBQQEWLlwIIyMjdOzYEcCLIP/KlSto3749Jk6ciIMHD0JJSQnu7u6YMmUK9PX1y71HeXl5OHv2LHr27ClzLD8/HwcPHkTnzp1haGiINm3aYM+ePZg4cSKUlaXPq2JjY/HVV19J0kqn3Ht7eyMjIwNr1qzB/v374ePjI8nn6OiI4uJinDp1SvLAhoiIFEtubu5rH5B/qHa8/E/68NgH1QP7Qf7YBx+eIAhQUlKqVF6FDeCfP3+OgoICGBkZSdIFQZCs81ZWVpYEbllZWYiIiICenh4A4Pr161izZg2mT5+O/v37AwAMDAzQo0cPJCQkoFOnTmXWv2rVKiQnJ2PQoEFiWkZGRpkjzdra2khPT6/0tU2bNg3Tpk3D559/LqYFBQWVuXHaxIkTxRFmExMTrF27VtKG3NxczJkzB2PHjoWWllaZ9aWmpgIA5s6diy5dumDlypW4e/cuFixYgJycHCxcuFCSf/HixYiIiAAA1KlTBytWrEDDhg3F46ampli3bh2GDx8u5jM2Npa07fnz5ygsLMTKlSvh7OyMJUuW4OnTp/jpp58QHByMLVu2lHt/rl69isLCQlhYWMgcO3z4MLKzs+Ht7Q0A6NGjB2JjY3Hy5Em4urqK+e7fv487d+6gffv2kvP37NkDBwcH8XoaN26M6OhomQBeW1sb9evXx/nz5xnAExEpsDt37lSrv6TevXtX3k346LEPqgf2g/yxDz6syuwDBihwAF/q1ScVBw4cwOjRo8Xvvr6+CAkJEb9bWlqKwTvwItgEgNatW8ukPX78uMw6T5w4gfDwcAwfPhy2trZvewky5s+fj7i4OISGhsLU1BTnzp3D0qVLoa2tjSFDhkjyjh49Gn5+fnj06BHWr18Pf39//Prrr6hfvz6AFzum16lTp9zd2gGIO/U3atRI3AHe1dUVNWrUwJQpUzB27FhJgP7ll1+iU6dOSElJwW+//YbAwECsW7cONjY2AF78ZSg4OBhubm7o1asX8vPzsWbNGgQEBGDLli2oW7euWKempiaWLFki/mDr1q0Lf39/JCQkSALul5VuhPdyP5aKiYlBnTp1xP7s0KEDNDQ0EB0dLSnv8OHDMDc3R4MGDcS0a9eu4caNG5gyZYqY5uXlhSVLluDff/8V72kpXV1dyaZ8RESkeBo1alRtRuDv3r0LU1NTqKury7s5HyX2QfXAfpA/9sGHd/PmzUrnVdgAXldXF2pqajJBtqurqzhdvay18dra2pLvqqqqAKRrtEuDyfz8fJnzL1++jODgYHh7e2PkyJEyZWdlZcmck5GRITOtvjz//PMP1qxZg+XLl6NDhw4AAGdnZxQVFWHx4sXw8fGRjKQ3bNgQDRs2hL29Pdzd3dGlSxesWrUKISEhSEpKwpo1a7B06VJxF/ycnBzxn9nZ2dDU1BTb1rJlS0lbWrVqBQC4ceOGJIA3NDSEoaEhAKB9+/bo27cvwsLCEBkZCQBYtGgR6tati3nz5onnuLi4wMPDAxs2bMC4ceOgra0NJSUlODk5SZ42ubi4QEVFBTdv3iw3gC/tl1efUmVkZODIkSPo2bMnsrOzxfS2bdvijz/+wPTp08VzYmNjZUbfd+/eDWVlZbRp0wYZGRkAgHbt2iE8PBwxMTEyO96rqamV+RshIiLFUd3+cqqurl7l/Wjo3WIfVA/sB/ljH3w4lZ0+DyhwAF+jRg04OTkhISEBxcXFUFFRAQDo6OjAzs4OQOWnIVTWvXv3EBAQAEdHR4SGhsocb9y4MVJTU5Geni4J2G/fvo3GjRtXqo7Spy9WVlaSdGtraxQUFCA5ObncqfDq6uowMzPDvXv3ALxY215YWFjmq9b8/PzQrFkzbNu2DQ0bNqzwXlUUpCorK8PKygp///235BocHBwk+TQ1NWFiYoL79++LbTU2Nn6jOkvvbUZGhmSt/IEDB1BYWIioqKgy9xyIi4tDly5dkJWVhb///hujRo0SjwmCgL1798psQlgqOjpa5j5mZmaiadOm5baTiIiIiIjoXVLYAB4A/P39ERQUhIiICIwYMeK91vXkyRN8/fXXMDIyQlhYmDhy/7I2bdpAWVkZBw8eFNevp6en4/jx42VuQFeW0qD28uXLkvX9ly5dgpKSksw07pdlZWXh+vXr8PT0BPDiIcCGDRskea5evYoff/wRM2bMkDzocHNzk3mnfXx8PACIU+PLUlRUhAsXLkhG6OvXr4+rV69KNmPIysrCvXv3JKP8Hh4e2L9/P/Lz88Ud6//66y8UFxdXWGfpTvoPHz6EmZmZmB4dHQ1jY2P8+OOPMueMGzcO0dHR6NKlC44dOwYtLS04OjqKxxMTE/Ho0SMEBwfD2dlZcu6xY8ewcuVKXL9+XVx3X1JSgn///bfCpQlERERERETvkkIH8O3bt0dgYCDCwsJw7do1dOvWDQYGBsjMzERiYiJSUlLK3UW+KvLy8hAQEIBnz57h+++/x40bN8Rjampq4qvq6tWrh759+2LevHlQVlaGoaEhIiMjUbt2bZlN0Mpja2sLW1tbTJs2DWlpaTAxMcGFCxewYsUK9OnTR5zqFx4ejszMTDg5OUFPTw9JSUnYuHEjCgoKMHDgQAAvpvS/Oi2+lI2NjSRIHjlyJHx8fDB+/Hj07t0b9+7dw4IFC9CjRw+YmJgAALZu3YoLFy6gdevW0NfXR2pqKrZs2YI7d+5g2rRpYlk+Pj4YMWIEvvnmG/Ts2RMFBQVYs2YNCgoKJBvzDR48WNxd38/PD0+fPsWCBQvQvHlzcfp+WRo2bAh9fX1cvnwZ7dq1AwAkJyfj9OnTGDZsWJnX7O3tjV9//RWZmZmIjY2Fu7u7ZHPD6OhoaGhowN/fX+Y307RpU6xbtw4xMTFiAH/nzh3k5OSgRYsW5baTiIiIiIjoXVLoAB4Axo8fj+bNm2PTpk2YMWMGsrKyoKOjAxsbG/zwww/w8vJ66zpSU1Nx7do1ALLr6o2NjXH48GHx+5QpU6CpqYkFCxYgOzsbTk5OMjvDV0RFRQURERFYvHgxIiMjkZaWhnr16mHIkCEICAgQ81lbW2PdunXYtWsXcnJyYGhoCGdnZyxevFgyGl5Ztra2WLlyJebPn49hw4ZBR0cH/fr1w9ixY8U8TZo0wcGDBzF79mxx+rqdnR2ioqJgaWkp5uvUqRN+/vlnrF69GmPHjoWqqiqsra2xYcMGcYNAADAyMsKGDRvwww8/IDg4GOrq6ujYsSMmTZr02nUgXbt2xdGjR8WZDXv27EFJSYnkdXYv6927N9atW4cDBw7g6NGjko0NCwsLceDAAXTq1KnMBz56enpo164dYmJiMG7cOCgpKeHo0aMwNjYWZzEQERERERG9b0pCddh6laiKrl27ht69e+PQoUMVrqV/1ZkzZ+Dn54eEhIRKP1QpS58+feDh4SGzkWFlXbx4EQAwcKAdzp5942YQEdEbcnQEzpyRdyv+T05ODq5evQorKytuGiUn7IPqgf0gf+yDD680NqjM4KDya3MQVUOWlpbo0KGDzBr/13FycsKlS5feKng/ffo0Hjx4AD8/vzcug4iIiIiIqKoYwH9gRUVF5X6Ki4vl3TyFMmHCBBgYGHzwerOysjB37lyZVxISERERERG9Twq/Bl6RPHz4EB07diz3uIuLCzZu3PgBW6TYTE1NMXjw4A9er4eHxwevk4iIiIiIiAH8B2RgYFDm+8lLvYsd84mIiIiIiOi/iQH8B6SmpsZdy4mIiIiIiOiNMIAnkiMrK3m3gIjo48T//hIRkSJiAE8kR5s2ybsFREQfr+JiQEVF3q0gIiKqPO5CTyQnBQUFyM3NlXczPmq5ubm4cuUK+0GO2AfVw8faDwzeiYhI0TCAJ5IjQRDk3YSPmiAIyM3NZT/IEfugemA/EBERKQYG8EREREREREQKgAE8ERERERERkQJgAE9ERERERESkABjAE8mRkpKSvJtAREREREQKggE8kZyoqalBXV1d3s2otOJiebeAiIiIiOjjxvfAE8mRry9w9aq8W/F6VlZ8Zz0RERERkbwxgCeSo6tXgbNn5d0KIiIiIiJSBJxCT0RERERERKQAGMATERERERERKQAG8EREREREREQKgAE8ERERERERkQJgAE9ERERERESkAD7KXeiPHDmCX375BRcvXkRmZiZ0dHRga2uLTz/9FN27d4ey8ts91yguLsaaNWsQFxeHmzdvQhAEWFhYYPTo0WjRooUkb0FBARYtWoTdu3cjOzsbjo6OmDp1Kho3blylOjMyMhAWFob9+/cjPT0dhoaG+PLLL/H111+LeZYuXYrExETxuqOiomBnZycp5969e1i9ejXOnz+PGzduoHHjxoiJiZGpb8CAATh16pRM+t69e2FmZiZJO3fuHH7++WecP38eSkpKaNKkCWbMmAErKysxz61btxAaGoqzZ89CU1MTPXv2xJgxY6Cmpvbaa4+Li0NISAgOHTqEkJAQ7Nixo8L8Li4u2Lhxo/h94cKFOH/+PP7666/X1rVhwwYkJydj+fLliImJgYqKymvPISIiIiIiehc+ugB+4cKFiIyMROfOnRESEgJ9fX2kpqbi0KFDmDBhAnR0dNC2bdu3qiMvLw8rVqxA7969ERAQAGVlZWzbtg1+fn5YvXo1XF1dxbyhoaHYu3cvJk2aBENDQ0RERGDQoEHYs2cPateuXan6cnJyMGDAAKioqGDy5MmoU6cO7t69i6ysLEm+rVu3wsTEBK1bt8aBAwfKLOvGjRs4cuQImjVrhpKSEgiCUG69Tk5O+PbbbyVpDRo0kHxPSEhAYGAg+vTpg4CAABQVFeHChQvIzc0V86Snp2PgwIEwNTVFeHg4kpOTMWfOHOTl5SEkJKTCaxcEAYsWLcKgQYOgpqaG4cOHw8fHRzy+bNky3L59G/PnzxfTtLS0JGXExsaiR48eGDt2rJiWkpKCkSNHYty4cWjZsqWY3qRJE7Ro0QKLFy/Gzp070adPnwrbR0RERERE9K58VAF8XFwcIiMjMXLkSAQHB0uOdevWDQMHDkSNGm9/S2rVqoVDhw5BR0dHTHNzc4O3tzfWr18vBvCPHz9GVFQUpk2bhr59+wIA7Ozs4OHhgS1btiAgIKBS9a1YsQLZ2dnYvXs3NDQ0AEASdJaKi4uDsrIyTp48WW4A36FDB3Tq1AkAMGnSJFy6dKncerW1teHg4FDu8aKiInz//ffw8/PDhAkTxPR27dpJ8m3ZsgXZ2dlYsmQJdHV1AbyYxTBjxgwEBQXB0NCw3DpOnjyJGzduoFevXgAAExMTmJiYiMf19PTw77//ltvOpKQk/PPPP+jSpQtMTU3F9IcPHwIA/ve//5V5bu/evbFx40YG8ERERERE9MF8VGvg165dC319fQwbNqzM4/b29rC2tha/DxgwAEFBQYiJiUGXLl3QrFkzDB06FOnp6UhKSsLgwYPh6OgILy8vnDx5UjxPRUVFEryXpllYWODJkydi2vHjx1FSUoKuXbuKabq6unBzc8PRo0crfV1RUVHo06ePGLyXpzJLA952+cDL4uPjkZSUBD8/vwrzHT16FK6urmLwDrx4oFJSUoITJ05UeO7OnTvh7OwMPT29N2pjXFwcGjVqJAneK6Nbt264evUqrl279kb1EhERERERVdVHE8AXFRXhzJkzaNWqVZVG2a9cuYINGzZg4sSJmDFjBhITEzF16lSMGjUK7du3R3h4OPT09BAcHIzs7OwK6z9//rxkbfvt27dRp04dmWDfzMwMt2/frlT7Hj58iJSUFHzyyScYOnQobG1t4eLigilTplTYnnfh1KlTcHBwgJ2dHb766iucPn1acvz8+fPQ1dXFxYsX4enpCWtra3h6emLnzp2SfLdv35ZZ86+trQ19ff3X3of4+Hg4OTm98TUcPnwYHh4eVT7PzMwMOjo6r33AQERERERE9K58NFPonz9/joKCAhgZGUnSBUFAcXGx+F1ZWVkyCp2VlYWIiAhxhPf69etYs2YNpk+fjv79+wMADAwM0KNHDyQkJIjTz1+1atUqJCcnY9CgQWJaRkZGmevctbW1kZ6eXqnrSk1NBQDMnTsXXbp0wcqVK3H37l0sWLAAOTk5WLhwYaXKqSpnZ2f07NkTpqamePLkCVavXg1/f39s3LgRjo6OAF6sI8/NzcXkyZMxatQomJmZISYmBt9++y3q1Kkj7jWQkZEBbW1tmTp0dHQqvA9PnjxBcnIyLCws3ugacnJycOrUKQQGBr7R+RYWFjh//vwbnauocnNzK9wXQdGU7sXw8p4M9GGxD6oH9oP8sQ/kj31QPbAf5I998OEJggAlJaVK5f1oAvhSr96YAwcOYPTo0eJ3X19fycZplpaWkunZpVOtW7duLZP2+PHjMus8ceIEwsPDMXz4cNja2r7tJUiUlJQAABo1aoS5c+cCAFxdXVGjRg1MmTIFY8eORcOGDd9pnQAwatQoyff27dvD29sby5Ytw8qVKwG8+CHm5+fjm2++wVdffSW27fbt24iIiHjrzQJTUlIA4I2nz8fHx6NWrVpo3rz5G53/ySefiG34WNy5c+c/+R/zu3fvyrsJHz32QfXAfpA/9oH8sQ+qB/aD/LEPPqzKvH0L+IgCeF1dXaipqckE2a6uroiKigKAMtfGvzoyrKqqCgCSkfPSm52fny9z/uXLlxEcHAxvb2+MHDlSpuxXd4oHXoxIvzqtvjyl+V7dtK5Vq1YAXuwq/z4C+FdpaGigXbt2ks3xSu9daVtKubq6YtOmTZJ8mZmZMmWmp6dXeB9K73dlf+yvOnz4MNq2bfvGGxeqqqqW2ef/ZY0aNfrPjcDfvXsXpqamUFdXl3dzPkrsg+qB/SB/7AP5Yx9UD+wH+WMffHg3b96sdN6PJoCvUaMGnJyckJCQgOLiYvH93To6OuK70N80ECzPvXv3EBAQAEdHR4SGhsocb9y4MVJTU2UC1bLWhJenYcOGFbZbngFm06ZNyz32crsaN24ss9Y9MzMTKSkpFd6H0nuWkZFR5bYJgoCjR49i4sSJVT735Ta+vPHex+C/+h9xdXX1124CSe8X+6B6YD/IH/tA/tgH1QP7Qf7YBx9OZafPAx/RJnYA4O/vjydPniAiIuK91/XkyRN8/fXXMDIyQlhYmDhy/7I2bdpAWVkZBw8eFNPS09Nx/PhxuLu7V6oeNTU1uLm5ISEhQZIeHx8PALCxsXmLq6i8nJwcxMXFiQ9DgBfXp6qqKrbl5ba93C53d3fEx8dLAvH9+/dDWVkZbm5u5dbZoEEDqKqqiq98q4qLFy/i6dOnlb7PZUlKSkKjRo3e+HwiIiIiIqKq+GhG4IEX67QDAwMRFhaGa9euoVu3bjAwMEBmZiYSExORkpICTU3Nt64nLy8PAQEBePbsGb7//nvcuHFDPKampia+qq5evXro27cv5s2bB2VlZRgaGiIyMhK1a9eGj49PpesbOXIkfHx8MH78ePTu3Rv37t3DggUL0KNHD8k70U+dOoWnT5+KUzT++usvJCUlwdjYWAy8c3NzceTIEQAvAtSsrCzs378fAODi4gI9PT0kJiZi1apV6Ny5M4yNjfHkyROsXbsWKSkpWLx4sVhf3bp1MWDAACxevBhKSkowMzPDnj17cO7cOaxatUrM5+Pjg40bN2LEiBEICgpCcnIy5s2bBx8fnwrfAV+zZk3Y2tri8uXLlb5XpWJjY+Ho6PjGI+g5OTm4ffs2RowY8UbnExERERERVdVHFcADwPjx49G8eXNs2rQJM2bMQFZWFnR0dGBjY4MffvgBXl5eb11Hamqq+H7wV9fVGxsb4/Dhw+L3KVOmQFNTEwsWLEB2djacnJywdu3aMnenL4+trS1WrlyJ+fPnY9iwYdDR0UG/fv0wduxYSb7w8HCcOnVK/D5//nwAQO/evTFnzhwAQFpammRTPwDi9w0bNqBly5bQ19dHYWEhFi1ahOfPn0NdXR2Ojo6YMWMG7O3tJeeOHz8eGhoaWL16NZ4+fQozMzMsXboUbdq0EfPo6Ohg/fr1mDVrFkaMGAFNTU307dtXpv1l8fT0xLp166q0cyPw4v3v3bt3r3T+Vx0/fhy1atV6qxF8IiIiIiKiqlAS/ks7UtFH5+nTp2jXrh3WrFkDZ2fnSp2TnJwMd3d37NmzB02aNHmjekeNGgVNTU38+OOPb3T+xYsXAQADB9rh7Nk3KuKDcnQEzpyRdyvevZycHFy9ehVWVlZc4yUn7IPqgf0gf+wD+WMfVA/sB/ljH3x4pbHBy8uRy/PRjcDTf4uenh769++P9evXVzqANzQ0xPXr19+4zgcPHuDIkSOIjo5+4zKIiIiIiIiq6qPaxE4RFRUVlfspLi6Wd/OqhaFDh8LS0hIFBQUfpL7k5GTMnDlTsr8AERERERHR+8YR+Grs4cOH6NixY7nHXVxcsHHjxg/YoupJT08PI0eO/GD1tWjRAi1atPhg9REREREREQEM4Ks1AwMDREVFlXv8XeyYT0RERERERIqBAXw1pqamVqmNDIiIiIiIiOi/j2vgiYiIiIiIiBQAR+CJ5MjKSt4tqBxFaScRERER0X8ZA3giOdq0Sd4tqLziYkBFRd6tICIiIiL6eHEKPZGcFBQUIDc3V97NqDQG70RERERE8sUAnkiOBEGQdxOIiIiIiEhBMIAnIiIiIiIiUgAM4ImIiIiIiIgUAAN4IiIiIiIiIgXAAJ5IjpSUlOTdBCIiIiIiUhAM4InkRE1NDerq6nKpu7hYLtUSEREREdFb4HvgieTI1xe4evXD1mllpVjvnyciIiIiohcYwBPJ0dWrwNmz8m4FEREREREpAk6hJyIiIiIiIlIADOCJiIiIiIiIFAADeCIiIiIiIiIFwACeiIiIiIiISAEwgCciIiIiIiJSAB9VAH/kyBEEBASgVatWsLGxQevWrREYGIiYmBiUlJS8dfnFxcVYuXIlfH190bJlS7i4uGDAgAFITEyUyVtQUIC5c+fCzc0NDg4O8Pf3x+3bt6tcZ0ZGBkJDQ9GmTRvY2dmhU6dOWLNmjSTP0qVL4e/vjxYtWsDCwgIXL16UKefevXsICQlBz549YW1tDW9v7zLrGzBgACwsLGQ+t27dksl77tw5DBo0CI6OjnBycsIXX3yBq6+8M+3WrVvw9/eHg4MD3NzcMG/ePBQUFFTq2uPi4uDu7i6T/+HDh5g6dSo8PDxga2sLFxcXDB48GPv375cp4/jx43BwcEB+fr6YlpiYiGHDhsHV1RW2trZwd3fHN998I963kpISeHp6Yvfu3ZVqJxERERER0bvw0bxGbuHChYiMjETnzp0REhICfX19pKam4tChQ5gwYQJ0dHTQtm3bt6ojLy8PK1asQO/evREQEABlZWVs27YNfn5+WL16NVxdXcW8oaGh2Lt3LyZNmgRDQ0NERERg0KBB2LNnD2rXrl2p+nJycjBgwACoqKhg8uTJqFOnDu7evYusrCxJvq1bt8LExAStW7fGgQMHyizrxo0bOHLkCJo1a4aSkhIIglBuvU5OTvj2228laQ0aNJB8T0hIQGBgIPr06YOAgAAUFRXhwoULyM3NFfOkp6dj4MCBMDU1RXh4OJKTkzFnzhzk5eUhJCSkwmsXBAGLFi3CoEGDoKamJqafO3cOQ4YMgZ6eHgICAtCkSRNkZWXhyJEj+Oabb2BqagpLS0sxf2xsLFxdXVGzZk0AwKZNmzBr1iy0atUK33//PQwNDZGcnIzo6Gh8/fXXOH36NJSVlREYGIjw8HB0794dNWp8NH+MiIiIiIhIjj6KyCMuLg6RkZEYOXIkgoODJce6deuGgQMHvpMgrFatWjh06BB0dHTENDc3N3h7e2P9+vViAP/48WNERUVh2rRp6Nu3LwDAzs4OHh4e2LJlCwICAipV34oVK5CdnY3du3dDQ0MDANCyZUuZfHFxcVBWVsbJkyfLDeA7dOiATp06AQAmTZqES5culVuvtrY2HBwcyj1eVFSE77//Hn5+fpgwYYKY3q5dO0m+LVu2IDs7G0uWLIGuri6AF7MYZsyYgaCgIBgaGpZbx8mTJ3Hjxg306tVLTMvPz8eYMWNQr149bNmyBVpaWpLr69+/P7S1tSXlxMXFISgoCABw7do1/PDDD+jZsyfmzJkDJSUlMZ+3tzdiY2PF7927d0doaCji4uLE+0ZERERERPQ+fRRT6NeuXQt9fX0MGzaszOP29vawtrYWvw8YMABBQUGIiYlBly5d0KxZMwwdOhTp6elISkrC4MGD4ejoCC8vL5w8eVI8T0VFRRK8l6ZZWFjgyZMnYtrx48dRUlKCrl27imm6urpwc3PD0aNHK31dUVFR6NOnjxi8l0dZ+fXdXJk8lRUfH4+kpCT4+flVmO/o0aNwdXUVg3fgxQOVkpISnDhxosJzd+7cCWdnZ+jp6Ylp+/btw6NHjzBu3DhJ8F7K0tIS9evXF7/fuHEDSUlJaN++PQBgw4YNUFJSwrfffisJ3kt5eHiI/66uro527dphx44dFbaTiIiIiIjoXfnPj8AXFRXhzJkz8PT0rNIo+5UrV/Ds2TNMnDgRWVlZCA0NxdSpU5GUlIRevXrB398fkZGRCA4ORmxsLDQ1Ncut//z582jevLmYdvv2bdSpU0cm2DczM0NUVFSl2vfw4UOkpKTgk08+wdChQ3H8+HFoaGigS5cu+O6778ptz7tw6tQpODg4oLi4GM2aNcPo0aPh7OwsHj9//jx0dXVx8eJF+Pn54cGDB2jYsCGGDRsmGTG/ffs2+vTpIylbW1sb+vr6r90PID4+Xubc06dPQ0VFBa1bt67UdcTGxsLa2hoGBgbi+ba2tpKHAhVxdHREWFgYSkpK3ukDkA8lNze3wqUSH4PSJR0vL+2gD4t9UD2wH+SPfSB/7IPqgf0gf+yDD08QhDIHEMvynw/gnz9/joKCAhgZGUnSBUFAcXGx+F1ZWVkShGVlZSEiIkIM5q5fv441a9Zg+vTp6N+/PwDAwMAAPXr0QEJCQrnTqFetWoXk5GQMGjRITMvIyChznbu2tjbS09MrdV2pqakAgLlz56JLly5YuXIl7t69iwULFiAnJwcLFy6sVDlV5ezsjJ49e8LU1BRPnjzB6tWr4e/vj40bN8LR0REAkJKSgtzcXEyePBmjRo2CmZkZYmJi8O2336JOnTriXgMZGRkyU9oBQEdHp8L78OTJEyQnJ8PCwkKSnpycDD09PdSqVatS1xIbGysZVU9OToadnV2lzgVejOhnZWXh1q1baNq0aaXPqy7u3LnD/zD/f3fv3pV3Ez567IPqgf0gf+wD+WMfVA/sB/ljH3xYL+/rVZH/fABf6tUnGgcOHMDo0aPF776+vpKN0ywtLSUjsaampgAgGd0tTXv8+HGZdZ44cQLh4eEYPnw4bG1t3/YSJEp3zW/UqBHmzp0LAHB1dUWNGjUwZcoUjB07Fg0bNnyndQLAqFGjJN/bt28Pb29vLFu2DCtXrgTw4uFIfn4+vvnmG3z11Vdi227fvo2IiIi33iwwJSUFACo9Ul6WZ8+e4fz58/j+++8l6ZV98gUAn3zyidgeRQzgGzVqxBH43FzcvXsXpqamUFdXl3dzPkrsg+qB/SB/7AP5Yx9UD+wH+WMffHg3b96sdN7/fACvq6sLNTU1mSDb1dVVnK5e1tr4V0eGVVVVAUAycl76lOTlV5CVunz5MoKDg+Ht7Y2RI0fKlP3qTvHAixHpV6fVl6c036ub1rVq1QrAi/Xd7yOAf5WGhgbatWsn2Ryv9N6VtqWUq6srNm3aJMmXmZkpU2Z6enqF96H0fr/6lMrQ0BAJCQnIz88Xd5Uvz5EjR1CnTh3Y2NhIzv/3338rPO9lpfXn5eVV+pzqhP9B/j/q6uqv3UuC3i/2QfXAfpA/9oH8sQ+qB/aD/LEPPpyqDCIq3sLdKqpRowacnJyQkJAgmTKvo6MDOzs72NnZVXq6QmXdu3cPAQEBcHR0RGhoqMzxxo0bIzU1VWaa+O3bt9G4ceNK1dGwYcMK213WQ4UPpaLR6Jfb1bhxY5m17pmZmUhJSanwPpQG9xkZGZJ0FxcXFBUVISEh4bVtjIuLQ7t27SR/WFxcXHDp0iU8f/78tee/XP/Lm/ARERERERG9L//5AB4A/P398eTJE0RERLz3up48eYKvv/4aRkZGCAsLE0fuX9amTRsoKyvj4MGDYlp6ejqOHz8Od3f3StWjpqYGNzc3mWA1Pj4eACQjy+9TTk4O4uLiJGvH27RpA1VVVbEtL7ft5Xa5u7sjPj5eEojv378fysrKcHNzK7fOBg0aQFVVFQ8fPpSkd+3aFUZGRli4cGGZMxyuX7+OR48eobCwEMePH5esfwdevH2gpKREXJLwqri4OMn3pKQkAP+3lIKIiIiIiOh9+s9PoQderNMODAxEWFgYrl27hm7dusHAwACZmZlITExESkrKO9m1PS8vDwEBAXj27Bm+//573LhxQzympqYmvqquXr166Nu3L+bNmwdlZWUYGhoiMjIStWvXho+PT6XrGzlyJHx8fDB+/Hj07t0b9+7dw4IFC9CjRw+YmJiI+U6dOoWnT5+Kayv++usvJCUlwdjYWAy8c3NzceTIEQAvAtOsrCzs378fwIuRaT09PSQmJmLVqlXo3LkzjI2N8eTJE6xduxYpKSlYvHixWF/dunUxYMAALF68GEpKSjAzM8OePXtw7tw5rFq1Sszn4+ODjRs3YsSIEQgKCkJycjLmzZsHHx+fCt8BX7NmTdja2uLy5csy6T///DOGDBmCPn36YNCgQWjSpAmysrJw/PhxbNu2Db/99hvu3r2LgoICmd3qLS0tMXnyZMyaNQvJycno06cPDA0NkZycjD179iAxMRGnTp0S81+6dAlmZmZvtRafiIiIiIiosj6KAB4Axo8fj+bNm2PTpk2YMWMGsrKyoKOjAxsbG/zwww/w8vJ66zpSU1Nx7do1ALLr6o2NjXH48GHx+5QpU6CpqYkFCxYgOzsbTk5OWLt2bZm705fH1tYWK1euxPz58zFs2DDo6OigX79+GDt2rCRfeHi4JPCcP38+AKB3796YM2cOACAtLU2yqR8A8fuGDRvQsmVL6Ovro7CwEIsWLcLz58+hrq4OR0dHzJgxA/b29pJzx48fDw0NDaxevRpPnz6FmZkZli5dijZt2oh5dHR0sH79esyaNQsjRoyApqYm+vbtK9P+snh6emLdunUyr1xwcHDAjh07sGLFCkRGRiI1NRUaGhqws7PDwoULYWlpiR9//BEtW7Yscw24r68vLCwssHr1asycORNZWVnQ09NDq1atsHbtWkneo0ePwtPT87VtJSIiIiIieheUhI99G2pSSE+fPkW7du2wZs0ayTvoK8PT0xN+fn7w9fV94/pv3LiBnj174sCBA2+0WeDFixcBAAMH2uHs2TduxhtxdATOnPmwdVZXOTk5uHr1KqysrLhJi5ywD6oH9oP8sQ/kj31QPbAf5I998OGVxgaVeaX1RzMCT/8tenp66N+/P9avX1/lAP7lHfPf1Jo1a9CzZ88PstM/ERERERER8JFsYqeIioqKyv28vJv+x2zo0KGwtLREQUHBB623pKQE//vf/2SWHBAREREREb1PHIGvhh4+fIiOHTuWe9zFxQUbN278gC2qnvT09DBy5MgPXq+ysjKGDh36weslIiIiIqKPGwP4asjAwABRUVHlHn8XO+YTERERERGRYmEAXw2pqalVagMDIiIiIiIi+ngwgCeSIyurj6NOIiIiIiJ6ewzgieRo0yb51FtcDKioyKduIiIiIiJ6M9yFnkhOCgoKkJubK5e6GbwTERERESkeBvBEciQIgrybQERERERECoIBPBEREREREZECYABPREREREREpAAYwBMREREREREpAAbwRHKkpKQk7yZ81JSUlKCurs5+kCP2QfXAfpA/9oH8sQ+qB/YDUcWUBO6iRfTBXbx4EQBgZ2cn55YQERERVU/FJcVQUearcz60nJwcXL16FVZWVtDQ0JB3cz4KVYkN+B54Ijny3e6LqylX5d0MIiIiomrFSt8Kmz7bJO9mEFU7DOCJ5OhqylWcfXxW3s0gIiIiIiIFwDXwRERERERERAqAATwRERERERGRAmAAT0RERERERKQAGMATERERERERKQAG8EREREREREQKgAE8ERERERERkQJQiAD+yJEjCAgIQKtWrWBjY4PWrVsjMDAQMTExKCkpeWf1HD58GJ9++ins7Ozg6emJ33//XSbP0qVL4e/vjxYtWsDCwgIXL16scj0DBgyAhYWFzOfWrVvlnjN8+HBYWFhg9erV5eZ5/PgxHB0dYWFhgadPn4rpT548wbx589CzZ084OjrC3d0d48ePR1JSkuT8+Ph4jB07Fh06dECzZs3QvXt3rFq1CoWFheXWeenSJVhZWcHR0VHmWFXvVXZ2Ntzd3at0X+Pi4uDu7o6CggJJ+sOHDzF16lR4eHjA1tYWLi4uGDx4MPbv3y9TxvHjx+Hg4AA3N7cy++XlT3h4OB4+fAgHBwc8fPiwUm0kIiIiIiJ6F6r9e+AXLlyIyMhIdO7cGSEhIdDX10dqaioOHTqECRMmQEdHB23btn3rehITEzFy5Ej07dsXkydPxl9//YXvv/8empqa6Nq1q5hv69atMDExQevWrXHgwIE3rs/JyQnffvutJK1BgwZl5j1y5AjOnz//2jLnzJkDDQ0N5OTkSNIvX76MP/74A3369EGzZs3w7NkzLF++HJ9//jliYmKgp6cHANiyZQvy8vIwatQoGBkZ4fz58wgPD8etW7fw448/ytQnCAJmzZoFPT09mTqBqt+rZcuWobi4+LX5Xq5/0aJFGDRoENTU1MT0c+fOYciQIdDT00NAQACaNGmCrKwsHDlyBN988w1MTU1haWkp5o+NjYWrqyuCg4MlDwJGjhwJJycnfP3112JavXr1UK9ePXh6eiI8PBxz586tdHuJiIiIiIjeRrUO4OPi4hAZGYmRI0ciODhYcqxbt24YOHAgatR4N5ewfPly2NvbY+bMmQCAVq1a4cGDBwgLC5ME8HFxcVBWVsbJkyffKoDX1taGg4PDa/MVFBRg9uzZGDduHCZPnlxuvoSEBCQkJCAoKEgmqGzevDn27dsnuVdOTk5o3749du7cKQao06dPF4N5AGjZsiVKSkrw888/Y8KECZJjAPD777/j2bNn6NOnDzZu3CjTpqrcq1u3buHXX3/Ft99+i2nTplWYt9TJkydx48YN9OrVS0zLz8/HmDFjUK9ePWzZsgVaWlrisQ4dOqB///7Q1taWaWdQUBCsra0l6Wpqaqhbt26Z/dS3b1/4+/vj22+/lbkvRERERERE70O1nkK/du1a6OvrY9iwYWUet7e3lwRdAwYMQFBQEGJiYtClSxc0a9YMQ4cORXp6OpKSkjB48GA4OjrCy8sLJ0+eFM8rKCjAyZMnJYE6AHTv3h23bt2STJVWVv6wt2z16tXQ1tbGZ599Vm6ewsJCzJo1C8HBwdDV1ZU5rq2tLfOgo169etDT08OTJ0/EtLICUSsrKwiCgJSUFEl6RkYGFixYgO+++w6qqqpltqsq9yo0NBQ+Pj5o1KhRpc/ZuXMnnJ2dJe3et28fHj16hHHjxkmC91KWlpaoX7+++P3GjRtISkpC+/btK10v8OKhiK6uLqKjo6t0HhERERER0ZuqtiPwRUVFOHPmDDw9Pas0yn7lyhU8e/YMEydORFZWFkJDQzF16lQkJSWhV69e8Pf3R2RkJIKDgxEbGwtNTU3cv38fhYWFaNy4saQsMzMzAMDt27fLnd7+pk6dOgUHBwcUFxejWbNmGD16NJydnSV5/v33X6xYsQJr166FkpJSuWVt2LABKioq6N+/P3bt2lWp+u/cuYO0tDTxGstz5swZqKmpyVz/zz//DBsbG3h4eODSpUuVqrM8+/fvxz///IPw8HBcvny50ufFx8ejT58+krTTp09DRUUFrVu3rlQZsbGxsLa2hoGBQZXarKysjGbNmiE+Ph4DBw6s0rlEREREVDm5ubkQBEHezfio5ObmSv5J758gCBXGey+rtgH88+fPUVBQACMjI0m6IAiSddLKysqSkd6srCxERESIo7LXr1/HmjVrMH36dPTv3x8AYGBggB49eiAhIQGdOnVCeno6AMhMrS79Xnr8XXF2dkbPnj1hamqKJ0+eYPXq1fD398fGjRslm8H9+OOP6Ny5c4VT7ZOTk7F06VIsXboUKioqlapfEASEhobCwMAAXv+PvfsOi+Jq/wb+pa0CKmgEFRFBNIAoUgTBihWRGI0lalSUKGABjTWJLZLYY8cCYokYEgu2SFASH4FoRAyx10QRRaKCohQFlrLvH747P8elLIgs6PdzXV55ZubMnHvmbMxzzynj4VFiuaSkJISGhmL48OHQ1dUV9l+/fh3h4eE4ePCgUvWVJicnB8uWLcO0adOK7TEvSWpqKh49egQLCwvR/kePHqFBgwaoXbu2UteJjo5G9+7dyxWznKWlJcLCwip0LhERERGV7c6dO0wkVSQpKUnVIbxXXl3TqzTVNoGXe/1NRFRUFKZOnSpsjxw5EgsWLBC2LS0tRUOqTU1NAUDUIyvf9/Dhw7cQcdmmTJki2nZ1dcVHH32ETZs2ISQkBMDLldFPnTpV7Krpr1qxYgU6deoEFxcXpesPDAzEmTNnsHXrVujo6BRbJjs7G/7+/jA2Nsa0adOE/TKZDAEBAfjss8/K7L1XxubNm/HBBx8o9KSXRT6k/03mnz99+hQXL17E3LlzK3R+/fr18fTpU+Tn55c4jYCIiIiIKs7MzIw98FUsJycHSUlJMDU1hba2tqrDeS/cunVL6bLVNoHX19eHRCJRSLJdXFwQHh4OAMXOjX+9F12eWNWtW1fYJ3+7kZeXBwDQ09MDAGRlZYnOzczMFB1/W3R0dNCtWzfRQm+LFi2Cp6cntLW1hTjkMWdmZqJevXo4f/48oqKisHfvXqGM/A3l8+fPoa2trfAv3d69e7Fx40YsXry4xKRfKpVi8uTJyMjIwJ49e0RJfmRkJBITE7Fq1SqhTvlzzMzMRK1atVCrVi2l7jslJQXbt2/Hxo0bhWcvX83+xYsXeP78uajn/1XyOl9/U9WoUSPExcUhLy+vzDhiY2PxwQcfwNraWql4X/fq74gJPBEREVHlYwKpOtra2iV29lHlUnb4PFCNE3hNTU3Y29sjLi4OhYWFwvBwPT09tG3bFoDywwzKYmJiAi0tLSQmJoo+SZeYmAgACnPjq8KdO3cQFBSEoKAg0f5169Zh3bp1uHTpEu7cuYP8/Hx88sknCuf36tUL/fr1w5o1a4R9v//+OxYuXIgpU6ZgyJAhxdZbVFSEmTNn4urVqwgLC1OYwpCYmIiMjAz06NFD4VxHR0d4e3tj5syZSt3j/fv3kZ+fDx8fH4Vjnp6eaNeuHfbu3VvsufKXKq++3AAAJycnhIeHIy4ursyF6WJiYtCtW7dy/QvzqszMTGhpaZVr6D8REREREVFFVdsEHgC8vLzg6+uLoKAgTJ48+a3VI5FI0KFDB0RFRYkWJIuMjIS5uXmlL2D3uhcvXiAmJkZ4MQG8XJjudZ6enhg+fDj69esHLS0tdOnSRaHcyZMnERISgo0bNwpTBYCXn1ybPn06hg4dWuqzDAgIQHR0NLZt26YwvxwAPvnkEzg5OYn2HTx4EJGRkQgJCRGt8F4WKysrhfivX7+OpUuXIiAgQPQ8XmdsbAwtLS3RFwIAoG/fvlizZg1Wr16N9u3bKyTXN2/eRL169dCwYUOcOnXqjb7jnpKSUq5V84mIiIiIiN5EtU7gXV1d4ePjg/Xr1+PGjRtwd3eHoaEhsrKykJCQgLS0tBKHWJfXxIkT4enpiYULF8Ld3R3x8fGIiIgQ9WADL1ePT09PF+YpnDlzBikpKWjatGmpCadcQkICtm7dit69e6Np06ZITU3Fjh07kJaWhnXr1gnlOnToUOz5JiYmwjEDAwMYGBiIjqekpAB4+Z13+fzw27dvY/LkyTA1NcWAAQNw4cIFoXyDBg1gYmICAAgKCsLu3bsxbtw4SCQSUbmWLVuiTp06MDY2VnihcfbsWWhoaCjEXNazqlevXon3aW1tXerQ9lq1aqFNmzYKq9bXqlULa9euxfjx4zF48GCMHTsWLVu2RHZ2Nk6dOoW9e/di3759SEpKglQqVXq1+uJcuXIFDg4OFT6fiIiIiIioPKp1Ag8AM2bMgIODA8LCwhAQEIDs7Gzo6enB2toaS5YsKXUV9fJo3749AgMDsXbtWoSHh8PIyAiLFi2Cu7u7qFxgYCDOnj0rbK9cuRLAy57pZcuWlVmPgYEB8vPzsWbNGjx79gza2tqws7NDQEAAbGxsKuVeXnfx4kVkZWUhKytLWIlf7tW4//zzTwAvvz2/bds2UbnQ0NASk+2SvOmzKoubmxt++OEHhc8u2Nra4uDBg9iyZQuCg4Px+PFj6OjooG3btli9ejUsLS2xdOlSdOjQocLzqp48eYKrV69i+vTpb3wfREREREREylCTcVlHqqHS09PRrVs3bN++HY6OjuU6183NDZ6enhg5cmSF6g4LC8MPP/yA3377rUJz6C9fvgwAGHN6DM4/PF+hGIiIiIjeVXaN7XDO95yqw3gvvXjxAtevX4eVlRUXsasi8txAmRHd1b4HnqgkDRo0wIgRI7Bz585yJ/CvrvhfXkVFRQgNDcXkyZMrvAAeERERERFReamrOoB3TUFBQYl/CgsLVR3eO2fChAmwtLSEVCqtsjpTU1PxySef4OOPP66yOomIiIiIiNgDX4nu37+Pnj17lnjcyckJu3btqsKI3n0NGjSAn59fldbZuHFjTJgwoUrrJCIiIiIiYgJfiQwNDREeHl7i8cpaMZ+IiIiIiIjeP0zgK5FEIlFq4QEiIiIiIiKi8mICT6RCVgZWqg6BiIiIqNrh/0ciKh4TeCIVChsUpuoQiIiIiKqlwqJCaKhrqDoMomqFq9ATqYhUKkVOTo6qw3iv5eTk4Nq1a2wHFWIbVA9sB9VjG6ge26B6eLUdmLwTKWICT6RCMplM1SG812QyGXJyctgOKsQ2qB7YDqrHNlA9tkH1wHYgKh0TeCIiIiIiIqIagAk8ERERERERUQ3ABJ6IiIiIiIioBmACT6RCampqqg6BiIiIiIhqCCbwRCoikUigra2t6jDeCYVFhaoOgYiIiIjoreN34IlUaOSBkbiedl3VYdRoVgZWCBsUpuowiIiIiIjeOibwRCp0Pe06zj88r+owiIiIiIioBuAQeiIiIiIiIqIagAk8ERERERERUQ3ABJ6IiIiIiIioBmACT0RERERERFQDMIEnIiIiIiIiqgGqdQIfGxsLb29vODs7w9raGh07doSPjw8iIiJQVFRUafWcOHECH3/8Mdq2bQs3Nzfs379foczGjRvh5eWF9u3bw8LCApcvXy53PaNHj4aFhYXCn9u3b5d4zqRJk2BhYYFt27aVWObhw4ews7ODhYUF0tPThf2pqalYsWIFBgwYADs7O3Tt2hUzZsxASkqK6PzTp09j2rRp6NGjB9q1a4d+/fph69atyM/PV6grLy8P69atQ48ePdCmTRu4urpi+fLlojI9evQo9j7z8vKKjb+oqAiDBg2ChYUFjh07VuJ9vurmzZuws7MT3S8ApKenY9myZXBzc0Pbtm1hb2+PUaNGYd++fSgsFH8r/M6dO7CwsMB///0nuu6MGTPQuXNntGnTBh07doSfnx/i4uKEMl5eXti8ebNScRIREREREVWWavsZudWrVyM4OBi9e/fGggULYGBggMePH+P48eOYNWsW9PT00KVLlzeuJyEhAX5+fhgyZAjmzJmDM2fOYO7cudDV1UXfvn2Fcnv27IGJiQk6duyIqKioCtdnb2+PL7/8UrTP2Ni42LKxsbG4ePFimddctmwZdHR08OLFC9H+q1ev4vfff8fgwYPRrl07PH36FJs3b8bQoUMRERGBBg0aAAB2796N3NxcTJkyBU2aNMHFixcRGBiI27dvY+nSpcL1ioqKMGnSJCQnJ8PPzw/Gxsb477//cOfOHYWY3Nzc8Pnnn4v2SSSSYuPfvXs3Hj16VOZ9vmrt2rUYNGiQcA8AcPfuXXh6eqKwsBBeXl6wtraGVCrFmTNnsHTpUtSvXx+9evUSykdHR8PCwgJGRkYAgOPHj2PatGlo1aoVpk2bBhMTE6Snp+O3337D559/jrNnz6Ju3brw9fWFv78/PvvsM+jp6ZUrbiIiIiIiooqqlgl8TEwMgoOD4efnB39/f9Exd3d3jBkzBpqalRP65s2bYWNjg2+//RYA4OzsjOTkZKxfv16UwMfExEBdXR3x8fFvlMDXq1cPtra2ZZaTSqVYvHgxpk+fjjlz5pRYLi4uDnFxcfD19VXoCXdwcMDRo0dFz8re3h6urq44dOiQkGAvXLhQlAh36NABRUVFWLt2LWbNmiUc279/Py5evIjIyEgYGhqWGn/Dhg2Vus/09HSsW7cOs2fPLvU+X5WcnIzo6GgcOHBAtH/mzJkoLCzE/v370ahRI2F/165dMWrUKGRlZYnKR0dHo3v37gCAtLQ0fPnll3BwcMCWLVtELxvc3NwwdOhQ4Tk6OzujXr16OHjwIMaOHatUzERERERERG+qWg6h37FjBwwMDDBx4sRij9vY2KB169bC9ujRo+Hr64uIiAj06dMH7dq1w4QJE5CRkYGUlBSMGzcOdnZ28PDwQHx8vHCeVCpFfHy8KFEHgH79+uH27du4f/++sE9dvWof1bZt21CvXj0MGjSoxDL5+fn47rvv4O/vD319fYXj9erVU3jR0bhxYzRo0ACpqanCvleTdzkrKyvIZDKkpaUJ+/bt24e+ffuWmbyXx+rVq9GhQwd06NBB6XMOHTqEZs2aiX4DCQkJuHTpEnx9fUXJu5yRkREsLCyE7czMTJw7d05I4Pfu3Yvs7Gx8/fXXxY4UcHZ2hra2trDdt29fHDp0SOmYiYiIiIiI3lS1S+ALCgpw7tw5ODs7l6uX/dq1awgNDcXs2bMREBCAhIQEzJ8/H1OmTIGrqysCAwPRoEED+Pv74/nz5wCAe/fuIT8/Hy1atBBdy9zcHACQmJhYeTf2/509exa2trZo27YtRo0ahb/++kuhzH///YctW7Zg3rx5UFNTK/FaoaGh0NDQwIgRI5Su/86dO3jy5IlwjyU5d+4cJBKJMLw/Pz8f165dg5GREWbPng1bW1vY2dlh6tSpoiRf7siRI2jTpg3s7Ozg7e2NmzdvKpS5dOkSIiIiMHv2bKXjB17O2bezsxPtO3v2LAAoPa3i5MmT0NPTg42NDQDgr7/+gqGhoSjJL42dnR2uX7+uMAefiIiIiIjobal2Q+ifPXsGqVSKJk2aiPbLZDLRImTq6uqiXvHs7GwEBQUJvck3b97E9u3bsXDhQiHBNTQ0RP/+/REXF4devXohIyMDwMue6lfJt+XHK4ujoyMGDBgAU1NTpKamYtu2bfDy8sKuXbtECenSpUvRu3fvUoegP3r0CBs3bsTGjRuhoaGhVP0ymQyLFi2CoaEhPDw8SiyXlJSE0NBQDB8+HLq6ugBetkt+fj5CQkLg6OiIDRs2ID09Hd9//z38/f2xe/du4fwePXrAxsYGRkZGSE5ORlBQED777DOh5xx4OZ8+ICAAXl5eMDY2Fo12KOserly5IprLLn8eAIT57GWJjo5G165dhd/Qo0ePlD4XACwtLQG8fAnh6uqq9Hn09uTk5EAmk5X7nFf/SVWPbVA9sB1Uj22gemyD6oHtoHpsg6onk8lK7bh9VbVL4OVev4GoqChMnTpV2B45ciQWLFggbFtaWoqGgpuamgIAOnbsqLDv4cOHbyHisk2ZMkW07erqio8++gibNm1CSEgIAODUqVM4depUmauxr1ixAp06dYKLi4vS9QcGBuLMmTPYunUrdHR0ii2TnZ0Nf39/GBsbY9q0acJ++ar/urq62LBhgzDMvGHDhvDy8kJcXJwQy7x584Tz2rdvj06dOsHd3R3btm3DwoULAbwcjv/48WP4+PgoHT/w8qWKVCotdti/sgoLC3Hy5Elh3QM5Zf+lAYD69esDQLGjD0g17ty5U+H/0CQlJVVuMFRubIPqge2gemwD1WMbVA9sB9VjG1Stkhb8fl21S+D19fUhkUgUkmwXFxeEh4cDQLFz41/vRdfS0gIA1K1bV9gnfyjyz5nJVxB/fXGzzMxM0fG3RUdHB926dRMtirdo0SJ4enpCW1tbiEMec2ZmJurVq4fz588jKioKe/fuFcrIE5fnz59DW1tbNF8beDnHe+PGjVi8eHGJSb9UKsXkyZORkZGBPXv2iJL8evXqQU1NDfb29qIfl5OTEzQ0NHDr1q0Sr2toaAgHBwdcvXpViHH16tWYNm0a8vPzkZ+fj+zsbABAbm4usrOzUadOnWKvJW+713/g8nnvDx48QPPmzYs9V+78+fN4/vw5OnXqJDq/PFMm5PXn5uYqfQ69XWZmZhXqgU9KSoKpqanCvzNUNdgG1QPbQfXYBqrHNqge2A6qxzaoerdu3VK6bLVL4DU1NWFvb4+4uDgUFhYKw8P19PTQtm1bAMq/nSiLiYkJtLS0kJiYKJo7LU/kXp8bXxXu3LmDoKAgBAUFifavW7cO69atw6VLl3Dnzh3k5+fjk08+UTi/V69e6NevH9asWSPs+/3337Fw4UJMmTIFQ4YMKbbeoqIizJw5E1evXkVYWJjCFAZtbW00bdq0xLhL+sZ7cZ4+fYpnz57hm2++wTfffCM69uWXX6Jhw4b4888/iz1Xvljfqy83gJcvEoCXIxjKSuCjo6Ph6Ogoekng5OSEuLg4/Pvvv2jVqlWZ9yB/6VPc4oGkGm/yHxhtbe0SR6VQ1WAbVA9sB9VjG6ge26B6YDuoHtug6pRnJHC1S+ABwMvLC76+vggKCsLkyZPfWj0SiQQdOnRAVFQUxowZI+yPjIyEubl5id9nrywvXrxATEyM8GICeLkw3es8PT0xfPhw9OvXD1paWujSpYtCuZMnTyIkJAQbN24UpgoAQHx8PKZPn46hQ4eW+iwDAgIQHR2Nbdu2lbiQW/fu3XHs2DHk5eWhVq1aAIAzZ86gsLAQ1tbWJV770aNH+PvvvzFgwAAAgIGBgUL8jx8/xvTp0+Hv7y+a9vC6WrVqwcjISGHOfPv27WFjY4OgoCD07t1bYaX8Bw8eIDMzExYWFoiJicGwYcNEx4cOHYpt27Zh6dKlCA4OFkZwyMXHx8PGxkZIEuX1m5mZlRgrERERERFRZaqWCbyrqyt8fHywfv163LhxA+7u7jA0NERWVhYSEhKQlpYmLK72piZOnAhPT08sXLgQ7u7uiI+PR0REhKgHG3i5ynl6erowvOHMmTNISUlB06ZNRQl4SRISErB161b07t0bTZs2RWpqKnbs2IG0tDSsW7dOKFfS59RMTEyEYwYGBjAwMBAdT0lJAfDyO+/y+eG3b9/G5MmTYWpqigEDBuDChQtC+QYNGsDExAQAEBQUhN27d2PcuHGQSCSici1bthR6qseNG4fDhw9j0qRJ8PT0RHp6OlatWgUHBwc4OzsDACIiIhAdHY1u3brB0NAQycnJ2LJlCzQ0NODl5QXgZRL++n3KE+KWLVvC3t6+1Gdpb28vDMd/1cqVKzF69GgMHjwYXl5esLa2hlQqxV9//YWwsDAsX74cOjo6uHXrlvD5ODkDAwMsX74cX3zxBUaMGIGRI0eiWbNmePr0KY4fP44jR46IPkF45coV6OjowMrKqtRYiYiIiIiIKku1TOABYMaMGXBwcEBYWBgCAgKQnZ0NPT09WFtbY8mSJaWuol4e7du3R2BgINauXYvw8HAYGRlh0aJFcHd3F5ULDAwUPlUGvEwWAeCTTz7BsmXLyqzHwMAA+fn5WLNmDZ49ewZtbW3Y2dkhICBA+JRZZbt48SKysrKQlZWl8Km5V+OWD1fftm0btm3bJioXGhoqJNtNmjRBaGgolixZAn9/f2hra6Nnz5746quvhGEfxsbGSE1NxZIlS5CVlYW6devC2dkZU6ZMEVagf1Nubm6YOXOmwlz55s2b4+DBgwgJCcHPP/+MBw8eQCKRoHXr1pgzZw66d++OsLAwmJubFxtLr169EB4ejpCQEKxatQpPnz5FvXr14ODggO3bt4vWU/jjjz/Qu3dvpb8AQERERERE9KbUZOVd9YlIxfLz89G9e3fMnDkTAwcOLNe5n3/+OSwtLcv97flXZWRkoFOnTtixYwccHR0rdI3Lly8DAMacHoPzD89XOBYC7Brb4ZzvuQqd++LFC1y/fh1WVlac46UibIPqge2gemwD1WMbVA9sB9VjG1Q9eW6gzMjuatsDT1QSLS0teHt7IzQ0tNwJ/Pbt29+4/l27dsHe3r7CyTsREREREVFFMIGvJAUFBSUeU1NT41DrSjZixAhkZ2cjPT39jb4JXxH6+vqib90TERERERFVBSbwleD+/fvo2bNnicednJywa9euKozo3SeRSN7qFwpKM2rUKJXUS0RERERE7zcm8JXA0NAQ4eHhJR6vrBXziYiIiIiI6P3FBL4SSCQSpRYcICIiIiIiIqoodVUHQERERERERERlYw88kQpZGVipOoQaj8+QiIiIiN4XTOCJVChsUJiqQ3gnFBYVQkOdX3ogIiIioncbh9ATqYhUKkVOTo6qw3gnMHknIiIiovcBE3giFZLJZKoOgYiIiIiIaggm8EREREREREQ1ABN4IiIiIiIiohqACTwRERERERFRDcAEnkiF1NTUVB0CERERERHVEEzgiVREIpFAW1tb1WG8H2SFqo6AiIiIiOiN8TvwRKp0eiSQcV3VUbzb9KyAjmGqjoKIiIiI6I0xgSdSpYzrwNPzqo6CiIiIiIhqAA6hJyIiIiIiIqoBmMATERERERER1QBM4ImIiIiIiIhqACbwRERERERERDUAE3giIiIiIiKiGqDaJ/CxsbHw9vaGs7MzrK2t0bFjR/j4+CAiIgJFRUWVVs+JEyfw8ccfo23btnBzc8P+/fsVyqSkpGD69Ono3Lkz7OzsMHjwYERFRVW4zuPHj8PCwgIfffSRwrF//vkHvr6+cHZ2Rvv27TFy5EicOXNGVCYwMBAWFhYKf37++WdRuZkzZ6JPnz6wtbWFo6MjRo4ciVOnThUb04ULFzB27FjY2dnB3t4en376Ka5fL/4zZ8+fP0fXrl1hYWGBy5cvC/uzs7MRGBiIIUOGoH379ujYsSMmTJiAmzdvis6XSqVYsWIFRo4cCVtbW1hYWCA9PV2pZwcAT548gZ2dHf755x+FY9euXYOFhQV69+5d6jVOnToFW1tb5OXlifZPnDgRFhYWOHTokMI59+/fh62tLe7fv690rERERERERG+qWifwq1evho+PD2rVqoUFCxbghx9+wPz581G3bl3MmjULf/75Z6XUk5CQAD8/P9ja2iIkJATu7u6YO3cujh07JpSRSqUYP348rl+/jjlz5mDDhg0wNzfH1KlTcfLkyXLXmZubiyVLlqBhw4YKx9LT0zF27Fg8e/YMixcvxurVq6GjowNvb2+FJLh27drYs2eP6E+fPn1EZfLz8zF27Fhs2rQJK1asgL6+Pnx8fJCQkCAqFxcXh9GjR8PU1BQbNmzAmjVr0KVLF+Tk5BR7D5s2bUJhYaHC/v/++w979uxBp06dsHbtWnz33XfIysrCsGHDcPv2bdEz2LdvH2rVqgUHBweln53c5s2b0aFDB3z44YcKx44cOQIAuHfvHi5evFjiNaKjo+Hi4oJatWoJ+549eya0aUREhMI5xsbGcHNzQ2BgYLljJiIiIiIiqqhq+x34mJgYBAcHw8/PD/7+/qJj7u7uGDNmDDQ1Kyf8zZs3w8bGBt9++y0AwNnZGcnJyVi/fj369u0L4GWPbmJiIkJDQ9GhQwcAgIuLCxISEnD06FF06dKlXHUGBwfDyMgIxsbGuHLliuhYXFwcnjx5gr1798LY2BgA4OTkBCcnJ6HXXk5dXR22tral1rVu3TrRdteuXdGzZ08cPnwY7du3BwAUFBRg7ty58PT0xKxZs4Sy3bp1K/aat2/fxk8//YQvv/wS33zzjeiYsbExfv/9d2hrawv7nJ2d0aNHD/z000+YP38+AKBevXo4e/Ys1NTUcODAgRJHBRTn+fPn2L9/P1asWKFwrKioCJGRkXBwcMCVK1dw5MgRtGvXrtjrxMTEwNfXV7QvKioK+fn56Nixo9AWH3zwgajMkCFD4OXlhS+//BINGjRQOm4iIiIiIqKKqrY98Dt27ICBgQEmTpxY7HEbGxu0bt1a2B49ejR8fX0RERGBPn36oF27dpgwYQIyMjKQkpKCcePGwc7ODh4eHoiPjxfOk0qliI+PFxJ1uX79+uH27dvCMOmCggIAQN26dYUy6urq0NXVhUwmK9e93bt3Dzt27MC8efOKPZ6fn69QV61ataClpVXuuoqjoaGBunXrCvUAwOnTp5GSkgJPT0+lrrFo0SIMHz4cZmZmCsd0dHREyTsA6OrqwsTEBKmpqaL9ampqFbgDCFMXunbtqnDsr7/+wsOHDzF8+HC4uroiMjKy2JEC//77L1JSUuDq6iraHxERgebNm+Orr75CQUEBIiMjFc51cHCAvr6+0NNPRERERET0tlXLHviCggKcO3cObm5u5eplv3btGp4+fYrZs2cjOzsbixYtwvz585GSkoKBAwfCy8sLwcHB8Pf3R3R0NHR1dXHv3j3k5+ejRYsWomuZm5sDABITE2FsbAxbW1u0atUKa9aswYIFC6Cnp4fDhw8jKSlJ6LlX1uLFizFgwABYWloWe7x79+5o2LAhli1bhmnTpkFTUxPbt2+HmpoaBgwYICqbm5sLZ2dnZGZmwtTUFGPHjsWnn36qcE2ZTIbCwkJkZWXhwIEDuHv3rijuixcvQl9fH5cvX4anpyeSk5PRrFkzTJw4EQMHDhRd69ixY/jnn38QGBiIq1evKnXPmZmZ+Pfff9GxY0elypfl9OnTaN26tWjou9yRI0egra2NXr16oXbt2oiKisLp06cVRklER0ejdevWMDQ0FPY9fPgQf/31FyZNmgQLCwt8+OGHiIiIwOjRo0Xnqquro127djh9+jTGjBlTKfdEb1dOTo7CCzD59JCSponQ28c2qB7YDqrHNlA9tkH1wHZQPbZB1ZPJZEp3bFbLBP7Zs2eQSqVo0qSJaL88CZVTV1eHuvr/DSLIzs5GUFCQMKT55s2b2L59OxYuXIgRI0YAAAwNDdG/f3/ExcWhV69eyMjIAPByOPer5Nvy45qamti5cycmTpyIXr16AXg5/3zNmjWws7NT+t5OnDiB8+fPi+bXv05PTw9hYWHw9fUVkk59fX2EhISgWbNmQjkTExPMnDkTrVu3Rl5eHo4cOYL58+cjKysL48aNE10zPDxc6PHX0dFRiDstLQ05OTmYM2cOpkyZAnNzc0RERODLL7/EBx98IMSRk5MjvFioU6eO0vf9/fffQ01NTWiHN3X58mV06tRJYb9UKsVvv/2GHj16QEdHB66urqhbty6OHDlSbALfvXt30b6IiAjIZDJhYcH+/ftj1apVuHfvHkxMTERlLS0tERYWVin3Q2/fnTt3SvwPUVJSUtUGQwrYBtUD20H12AaqxzaoHtgOqsc2qFoSiUSpctUygZd7/S1EVFQUpk6dKmyPHDkSCxYsELYtLS1F85FNTU0BQNTrK9/38OHDcsWSm5uLKVOmQCaTYePGjdDV1cWxY8cwY8YMhISEwMnJqcxr5OXlYcmSJfD39y913vSTJ0/g5+cHExMTzJkzBxoaGti7dy8mTpyIsLAwYXTA673xrq6uyM/Px+bNm+Hp6QktLS3hWM+ePWFpaYmnT5/i2LFj+OKLL7BhwwZhjrtMJkNeXh5mzpyJUaNGAXg5xz8xMRFBQUFC8rt582Z88MEHGDx4sNLPbv/+/di7dy+WLVuGxo0bK31eadLS0op9hn/88QcyMjKEBFwikaB37944duwYcnNzUbt2bQDA06dPcfHiRcydO1d0fkREBKytrYURGR4eHli9ejWOHDmCyZMni8rWr18fT58+RX5+vuhZU/VkZmZWbA98UlISTE1NFaZ9UNVgG1QPbAfVYxuoHtugemA7qB7boOrdunVL6bLVMoHX19eHRCJRSLJdXFwQHh4OAMXOjX+9F12eVL06l1z+ZkP+2TA9PT0AQFZWlujczMxM0fHw8HBcunQJsbGxQuLo4uKCe/fuYfXq1di9e3eZ97Vz506oq6vDw8NDuH5+fj6KioqQmZmJ2rVrQyKRYOvWrcjIyMCBAweEeF1cXODh4YFNmzZh1apVJdbh7u6OqKgo3Lt3T0j0AaBBgwZC3F27dkVGRga+//57IYGXPztnZ2fR9VxcXIRe5pSUFGzfvh0bN24UnteLFy+Efz5//hy6urqi82NjY7FgwQJMmjQJn3zySZnPSFlSqbTYt1RHjhxB3bp1YWtrKzzj7t2748CBAzhx4gT69esnxPXBBx/A2tpaOPf27du4fv06/P39hXPr1q2LNm3aICIiQiGBf/W3xAS++ivtP0Da2trQ0dGpwmjodWyD6oHtoHpsA9VjG1QPbAfVYxtUnfKsC1YtE3hNTU3Y29sjLi4OhYWF0NDQAPAymW7bti0A5YcYlMXExARaWlpITEwUDbFOTEwEAKEn9tatW2jUqJFCr6+VlVWx3wovTmJiIu7evQsXFxeFY46OjsJQ/1u3bqFFixaie9TQ0ICFhQXu3btX3lsslrW1Nf744w9hu1WrViWWlb/suH//PvLz8+Hj46NQxtPTE+3atcPevXuFfRcuXMDUqVMxcOBA0ciJyqCnpyck2XLZ2dmIiYlBbm5usc/4l19+ERL4mJgYdOvWTfQvyy+//AIACAwMLPYTcVevXhUl/JmZmdDS0irXVAIiIiIiIqKKqpYJPAB4eXnB19cXQUFBCj2flUkikaBDhw6IiooSLUYWGRkJc3Nz4TNuRkZGePjwIdLT00VJ/NWrV9G0aVOl6vL29lbohd6yZQvu3LmDpUuXCsP7jYyM8L///Q95eXnCIm2FhYW4ceMGrKysSq0jMjIS9erVU5iv/bq///5bNJ++c+fO0NLSwunTp0XfVT99+rSQtFpZWSE0NFR0nevXr2Pp0qUICAgQXq4AL194+Pr6wtnZGQEBAaXGUhFmZmbCFwLkjh8/jtzcXAQEBCisjn/w4EFERETg2bNn0NXVxalTp7B8+XJRmV9//RW2traYPn26aH9+fj4mTJiAI0eOiBL4lJSUYlfhJyIiIiIiehuqbQLv6uoKHx8frF+/Hjdu3IC7uzsMDQ2RlZWFhIQEpKWlKQzXrqiJEyfC09MTCxcuhLu7O+Lj4xEREYE1a9YIZfr374/g4GB4e3vDx8dHmAN/5syZYr9FXhxzc3PRsHbgZWL56NEj4dvyADB06FCEh4dj0qRJGDlyJDQ0NLBnzx7cvXsXixYtEsoNGjQIAwcORIsWLZCbm4sjR47gt99+w5w5c4Qh3TExMTh06BBcXV3RpEkTZGRkICIiAqdOncLq1auFazVs2BCjR4/GunXroKamBnNzc/z666+4cOECtm7dCuDlMPtX43yVtbW1kNw+efIE48aNQ61atTBmzBjRd+7r1KmDli1bCtuxsbHIyckRysi/DtCyZUtRudfZ29vj6NGjon1HjhxB06ZNMWzYMIVhKHp6ejh48CCOHTuG5s2bQyqVitZGOH/+PJKTkzFx4sRi79HV1RW//vorZs+eLSyceOXKFTg4OJQYIxERERERUWWqtgk8AMyYMQMODg4ICwtDQEAAsrOzoaenB2trayxZsgQeHh6VUk/79u0RGBiItWvXIjw8HEZGRli0aBHc3d2FMk2aNEFoaCjWrl2LgIAA5ObmwtTUFCtWrFBYTO5NtWnTBlu3bsWmTZvw9ddfo6ioCC1btsSWLVvg6OgolDMxMcEPP/yAx48fQ01NDR9++CG+//57fPzxx0KZZs2aQSqVYtWqVXj69Cnq168PCwsL7Nq1S2HhvRkzZkBHRwfbtm1Deno6zM3NsXHjRnTu3Llc8d+6dUtYv2Ds2LGiY05OTti1a5ewHRAQgJSUFGF7zpw5AAA/Pz/4+/uXWIebmxuCg4OFBTaePHmCuLg4+Pj4FDuHxNLSElZWVjhy5AjatGmDDh06iOZER0REQFtbG25ubsXWN3DgQPz++++Ij4+Hi4sLnjx5gqtXryr01hMREREREb0tarLXl2UmqiEGDRqEHj16wM/Pr1znubm5wdPTEyNHjqxw3WFhYfjhhx/w22+/lWvRCbnLly8DANreHwM8PV/hOEgJ9e0A93PFHnrx4gWuX78OKysrLtKiImyD6oHtoHpsA9VjG1QPbAfVYxtUPSE3eGVKcknUyyxBVE1NmjQJu3fvhlQqLdd5UVFRb5S8FxUVITQ0FJMnT65Q8k5ERERERFQR1XoIfU1TUFBQ4jE1NTVhNX2qHL169cLdu3fx4MEDNG/evMrqTU1NxSeffCKaqkBERERERPS2MYGvJPfv30fPnj1LPP763G+qHOPGjavyOhs3bowJEyZUeb1ERERERPR+YwJfSQwNDREeHl7i8cpaMZ+IiIiIiIjeT0zgK4lEIlFq0QEiIiIiIiKiilAqgf/666/LfWE1NTUsWbKk3OcRvVf0rFQdwbuPz5iIiIiI3hFKJfDx8fHlvjBX5yZSQscwVUfwfpAVAmpcRJKIiIiIajalEvgTJ0687TiI3jtSqRQ5OTnQ1tZWdSjvPibvRERERPQO4HfgiVRIJpOpOgQiIiIiIqoh3mgRuwsXLiA+Ph5PnjzBZ599BlNTU+Tk5CAxMRGmpqZceZ2IiIiIiIioklQogZdKpZg+fTr+97//QSaTQU1NDd27d4epqSnU1dXx+eefY+zYsZg4cWJlx0tERERERET0XqrQEPp169YhJiYGCxcuxLFjx0TDgGvVqoW+ffvif//7X6UFSURERERERPS+q1AC/+uvv2L48OEYNmwY9PT0FI6bm5sjOTn5jYMjetfxaw1ERERERKSsCiXwT548gYWFRYnHNTQ0kJubW+GgiN4HEomEK9C/Slao6giIiIiIiKq1Cs2Bb9KkCRITE0s8fu7cOZiYmFQ4KKL3xumRQMZ1VUehenpWQMcwVUdBRERERFStVSiB/+ijj7Bjxw706dMHpqamAP5vKPDevXtx9OhRzJgxo9KCJHpnZVwHnp5XdRRERERERFQDVCiBnzBhAi5evIhRo0ahRYsWUFNTw9KlS5GRkYGHDx+iW7duGDt2bCWHSkRERERERPT+qlACL5FIsHXrVvzyyy+IiopCUVERpFIpLCws8MUXX2DAgAFcnIuIiIiIiIioElUogQdeDpkfMGAABgwYUJnxEBEREREREVExKpzAA0BhYSGuXr2K+/fvAwCMjY1hbW0NDQ2NSgmOiIiIiIiIiF6qcAJ/4MABrF69Gk+ePIFMJgPwsle+QYMGmDZtGoYMGVJpQRIRERERERG97yqUwO/evRsLFy6ElZUV/Pz8hJXo79y5gz179mD+/PnIz8/HiBEjKjNWIiIiIiIiovdWhRL4kJAQtG/fHjt27ICWlpaw39nZGUOGDMGYMWOwdevWKk/gY2Nj8eOPP+Ly5cvIysqCnp4e2rRpg48//hj9+vWDurp6pdRz4sQJrF27Fnfu3IGRkRF8fHwwePBgUZmUlBSsWrUKZ8+exfPnz9GiRQv4+PjAzc2tQnUeP34ckydPRqtWrRARESHsDwwMxIYNG4o9Z9iwYfj222+F7f379yMkJAT3799HkyZN4OnpidGjR4vOycnJwaZNmxAZGYnHjx+jcePG+OSTTzB+/Hhoar78uSQnJ2PRokW4fv06nj59Cj09Pdjb22PatGkwMzMTrnX06FH88ssvuHr1KjIzM9G8eXOMHj0agwcPFi1yOHr0aJw9e1Yh/sjISJibm5f5bKZMmYKmTZviyy+/hIWFRZnlly5dikGDBgEAZDIZunbtilq1aiE5ObnU85o2bYoTJ07Ay8sLTk5OmDhxYpl1ERERERERVZYKJfCPHz/G559/Lkre5bS0tODh4YHvv//+jYMrj9WrVyM4OBi9e/fGggULYGBggMePH+P48eOYNWsW9PT00KVLlzeuJyEhAX5+fhgyZAjmzJmDM2fOYO7cudDV1UXfvn0BAFKpFOPHjwcAzJkzB3p6ejh8+DCmTp2KkJCQcseRm5uLJUuWoGHDhgrHhg4dqnC9v/76CytXrkTXrl2FfZGRkZgzZw48PT3h6uqKhIQELF26FGpqahg1apRQ7ttvv8Vvv/2G6dOnw9zcHBcuXMD69euRk5ODadOmAQCeP3+Ohg0bYvr06WjSpAnS0tIQHBwMT09PHD58GA0aNAAA/PDDD2jatCm++uor1K9fH6dPn8b8+fPx8OFD+Pn5iWK2t7fHl19+KdpnbGxc5rO5evUqoqOjcfz4cQDAnj17RMeHDRuG0aNH46OPPhL2mZiYiM6X/07S0tKE/fv27UNERAR27twp7JNIJAAAX19f+Pv747PPPoOenl6ZMRIREREREVWGCiXwVlZWuHPnTonH79y5A0tLywoHVV4xMTEIDg6Gn58f/P39Rcfc3d0xZswYoff4TW3evBk2NjZCz7azszOSk5Oxfv16IYG/du0aEhMTERoaig4dOgAAXFxckJCQgKNHj5Y7gQ8ODoaRkRGMjY1x5coV0bHGjRujcePGon27d++Gnp6eKIFfv349+vTpg7lz5wIAOnXqhMzMTAQGBmLYsGHQ0tJCUVERjh49inHjxmHkyJHC/d25cwe//vqrkMBbWlpi8eLFojrbtGkDNzc3/Pnnn+jfv7/wrOTJvPwZPHv2DDt27MCkSZNEIyLq1asHW1vbcj0XAAgNDUXnzp3RqFEjACj2Gk2aNCnx2jExMWjXrh2aNm2Kpk2bCvtPnjwJdXX1Ys9zdnZGvXr1cPDgQYwdO7bcMRMREREREVVEhcaUz58/H8eOHcPOnTuRm5sr7M/NzcUPP/yAY8eOYcGCBZUWZFl27NgBAwODEoc029jYoHXr1sL26NGj4evri4iICPTp0wft2rXDhAkTkJGRgZSUFIwbNw52dnbw8PBAfHy8cJ5UKkV8fLyQqMv169cPt2/fFlbjLygoAADUrVtXKKOurg5dXV1hwT9l3bt3Dzt27MC8efOUKp+Xl4fff/8dbm5uQo9xTk4OkpKS0KlTJ1HZzp0749mzZ7hw4QKAl8PJCwoKRHHL76OsuPX19QEA+fn5wr5Xk3c5KysrZGdn48WLF0rdT2levHiB3377rcLTEgAgOjoaPXr0KPd5ffv2xaFDhypcLxERERERUXkp1S0t71F9lbq6OpYtW4bvv/8ehoaGAIDU1FQUFhbCwMAAX331FX755ZfKjbYYBQUFOHfuHNzc3MrVy37t2jU8ffoUs2fPRnZ2NhYtWoT58+cjJSUFAwcOhJeXF4KDg+Hv74/o6Gjo6uri3r17yM/PR4sWLUTXks/TTkxMhLGxMWxtbdGqVSusWbMGCxYsEIbQJyUlieakK2Px4sUYMGCA0iMaoqOjkZ2dLRoyLpVKIZPJhIReTr59+/ZtODo6QkNDA4MGDcKPP/4Ie3t7mJub4+LFizh8+DAmTZqkUFdRUREKCwvx6NEjrFmzBk2aNEHv3r1Lje/vv/9Go0aNUKdOHdH+s2fPwtbWFoWFhWjXrh2mTp0KR0fHUq914cIFvHjxAg4ODqWWK0lqaiquXr2KpUuXlvtcOzs7bN26Fenp6cW+qKCKycnJKfdLrjet79V/UtVjG1QPbAfVYxuoHtugemA7qB7boOrJZDLRGmGlUSrjlfeuvr6vefPmon2vDkGuKs+ePYNUKkWTJk1E+2UyGQoLC4VtdXV10ZDt7OxsBAUFCcnXzZs3sX37dixcuFBYfM/Q0BD9+/dHXFwcevXqhYyMDAAvh3u/Sr4tP66pqYmdO3di4sSJ6NWrFwCgdu3aWLNmDezs7JS+txMnTuD8+fM4duyY0udERESgUaNGouRXT08P+vr6uHTpkrB4GwCh510eNwB88803+OabbzB06FBhn6+vL7y8vBTqmj17No4cOQLg5bzyHTt2KPTevyohIQGRkZEKc90dHR0xYMAAmJqaIjU1Fdu2bYOXlxd27dpV6vO6fPkydHR00KxZsxLLlCY2NhZGRkb48MMPy32u/IXKpUuX4OrqWqH6SdGdO3dU8h+LpKSkKq+TxNgG1QPbQfXYBqrHNqge2A6qxzaoWq93tpZEqQR+165dbxRMVXj9jUVUVBSmTp0qbI8cOVI0rN/S0lLUcyr/FF7Hjh0V9j18+LBcseTm5mLKlCmQyWTYuHEjdHV1cezYMcyYMQMhISFwcnIq8xp5eXlYsmQJ/P39le7hzczMRGxsLEaNGqWw4v5nn32Gbdu2wcHBAV27dsW5c+cQGhoKQPzsVq5ciZiYGCxatAimpqa4cOECNm7ciHr16gkL88lNnToVnp6eePDgAXbu3AkvLy/89NNPMDIyUojt4cOHmDZtGjp06ABPT0/RsSlTpoi2XV1d8dFHH2HTpk0ICQkp8X7T0tJQv359pZ5NcaKjo9G9e/cKnSuv99WF7+jNmZmZVXkPfFJSEkxNTaGtrV1l9dL/YRtUD2wH1WMbqB7boHpgO6ge26Dq3bp1S+mylbOymwrp6+tDIpEoJNkuLi4IDw8HgGLnxr/eiy5fUf/VHmT5W5C8vDwAEFYcz8rKEp2bmZkpOh4eHo5Lly4hNjZWSL5dXFxw7949rF69Grt37y7zvnbu3Al1dXV4eHgI18/Pz0dRUREyMzNRu3Zthbc0UVFRkEqlxU558PX1xb179zBr1izIZDLo6Ohg5syZ+Pbbb2FgYAAA+Oeff7B9+3Zs3rxZmBfu6OiIgoICrFu3DsOHDxcNfW/WrBmaNWsGGxsbdO3aFX369MHWrVsV1j/IzMyEt7c39PX1ERgYWObn/HR0dNCtWzdERUWVWi4vL0/pN1Wvk0qliIuLw/r16yt0vrzeV9eAoDenqv9IaGtrQ0dHRyV100tsg+qB7aB6bAPVYxtUD2wH1WMbVB1lh88Db5jA5+fnIzExEVlZWcX2mpU1h7kyaGpqwt7eHnFxcSgsLISGhgaAl8l027ZtASg/HKEsJiYm0NLSQmJiomgl+cTERAAQ5sbfunULjRo1Uug5t7KyUnrhs8TERNy9excuLi4KxxwdHUVD/eUiIiLQokUL0YJ9crVr18aqVaswd+5cpKWloVmzZsKbnnbt2glxy+N8VevWrSGVSvHo0SOFuety2traMDc3x927d0X7c3Nz4evri6ysLOzZs6fUIfblpaenp/AyRVlxcXEAIHwloLzk9RY3vYSIiIiIiOhtqFACX1RUhFWrVuGnn34qtQfy+vXrFQ6sPLy8vODr64ugoCBMnjz5rdUjkUjQoUMHREVFYcyYMcL+yMhImJubC98tNzIywsOHDxUWOLt69arS6wR4e3vjk08+Ee3bsmUL7ty5g6VLlwrD++VSU1Nx9uxZhe+rv65BgwZCTGFhYWjfvr3w4kEe29WrV0VrCly5cgVqamrFDo2Xy87Oxs2bN0UrwhcUFOCLL75AYmIiwsLChE+9leXFixeIiYkRXsCUxMzMDOnp6Xjx4kW53w7GxMSgU6dOFX65I//igJmZWYXOJyIiIiIiKq8KJfBBQUHYtm0bhg0bBgcHB8yePRszZ85EvXr18NNPP0FNTQ2zZs2q7FhL5OrqCh8fH6xfvx43btyAu7s7DA0NkZWVhYSEBKSlpUFXV7dS6po4cSI8PT2xcOFCuLu7Iz4+HhEREVizZo1Qpn///ggODoa3tzd8fHyEOfBnzpzBihUrlKrH3NxcWN1e7uDBg3j06FGxvcaRkZEoKioqdvg88HLBtnv37qFly5bIyMjAkSNHEB8fj59//lko06ZNG7Rp0wbffPMNnjx5AhMTE1y6dAlbtmzB4MGDheHNgYGByMrKgr29PRo0aICUlBTs2rULUqlU9GIjICAA0dHR+Oqrr5CdnS0smge87NWXSCRISEjA1q1b0bt3bzRt2hSpqanYsWMH0tLSsG7dulKfkb29PYqKinDt2jW0b9++zGf6qpiYmDd62XPlyhXo6OgojFYgIiIiIiJ6WyqUwB88eBDu7u4ICAjA06dPAQDW1tZwcXHBwIEDMXz4cJw5c0a0INzbNmPGDDg4OCAsLAwBAQHIzs6Gnp4erK2tsWTJEnh4eFRKPe3bt0dgYCDWrl2L8PBwGBkZYdGiRXB3dxfKNGnSBKGhoVi7di0CAgKQm5sLU1NTrFixAgMGDKiUOF535MgR2NjYwMTEpNjjmpqaCA8Px927d6GpqQknJyfs2bNH9JJAQ0MDQUFBWLduHYKDg/HkyRM0btwY48ePh7e3t1CudevW+OGHH3D48GG8ePFCWPV+3bp1ohXh//zzTwDAsmXLFOL53//+B2NjYxgYGCA/Px9r1qzBs2fPoK2tDTs7OwQEBMDGxqbUezYzM8OHH36IkydPliuBv3HjBh48ePBGq8f/8ccf6N27tzBlg4iIiIiI6G1Tk1Vgyee2bdti3rx5GDZsGJ4/fw4HBwcEBwejW7duAIAffvgBO3bsQGxsbKUHTPSqXbt2ITQ0FL/99pvSiz8EBQXh+PHjwiKH5ZWRkYFOnTphx44dFV7n4fLlywCAtvfHAE/PV+ga75T6doD7uSqv9sWLF7h+/TqsrKy4SIuKsA2qB7aD6rENVI9tUD2wHVSPbVD1hNygjCnEAFD6cuAl0NfXx4sXLwAAurq6qFOnDpKTk0Vl5CunE71NQ4cORW5uLk6cOKH0ORMmTKhw8g68fGlgb29fJYs0EhERERERyVVoCH3r1q2FtwTAy5W8d+7cCSsrK8hkMoSGhsLCwqLSgnwXFRQUlHhMTU2NQ7OVVLt2bSxbtqzCq9FXhL6+PubNm1dl9REREREREQEVTOA//fRTHDx4EFKpFBKJBNOmTcPIkSMxatQoyGQy6Onp4auvvqrsWN8Z9+/fR8+ePUs87uTkhF27dlVhRDVbp06dqrS+UaNGVWl9REREREREQAUT+J49e4oS0JYtW+L48eOIj4+HhoYG7Ozs+H3sUhgaGpY6hLuyVswnIiIiIiKid0eFEvji1K1bF7169QIAnDt3DmfPnsWECRMq6/LvFIlEotQCBURERERERERylZbAvyo+Ph7r169nAk9UFj1+Rx4AnwMRERERkRLeSgJPRErqGKbqCKoPWSGgxsUbiYiIiIhKUqHPyBHRm5NKpcjJyVF1GNUHk3ciIiIiolIxgSdSIZlMpuoQiIiIiIiohmACT0RERERERFQDKD0HftGiRUpf9MqVKxUKhoiIiIiIiIiKp3QC/+OPP5brwmpqauUOhoiIiIiIiIiKp3QCf+PGjbcZB9F7iS+6iIiIiIhIWZwDT6QiEokE2traqg6jYmSFqo6AiIiIiOi9w+/AE6nS6ZFAxnVVR1E+elb8fj0RERERkQowgSdSpYzrwNPzqo6CiIiIiIhqAA6hJyIiIiIiIqoBmMATERERERER1QBM4ImIiIiIiIhqgHLNgc/Ly8P//vc/3L9/H/r6+nB1dYWhoeHbio2IiIiIiIiI/j+lE/gnT55g+PDhuH//PmQyGQBAW1sbGzduRMeOHd9agERERERERERUjgR+06ZNSElJwdixY+Hs7Iy7d+9i06ZNWLBgAY4fP/42YyxWbGwsfvzxR1y+fBlZWVnQ09NDmzZt8PHHH6Nfv35QV6+c2QEnTpzA2rVrcefOHRgZGcHHxweDBw8WlUlJScGqVatw9uxZPH/+HC1atICPjw/c3NyUrkcqlWLdunU4fPgwMjMz8eGHH2LGjBlwcXERldu4cSMSEhKE+w4PD0fbtm2LvebBgwexc+dO3L59Gzo6Omjbti02bNiA2rVrC2Xy8vIQFBSEw4cPIzU1FQ0bNoS7uzu+/PJLAEBqaip++OEH/Pnnn7h37x7q1q0LR0dHTJ8+HU2bNhWuc/r0aezbtw8XL17EkydP0LRpUwwaNAhjxoyBlpaWUK6wsBDbt2/H/v378eDBAzRs2BB9+vSBn58fdHV1y3xOy5cvR0pKCtavXy/af/78eezYsQPnzp3Ds2fPoKurCwsLC/Tr1w+DBg2CRCIRlT916hT8/PwQHx+PWrVqlesa8+bNAwAsWrSozHiJiIiIiIgqi9IJ/KlTpzBgwAAhsQOAhg0bYsaMGUhMTESLFi3eSoDFWb16NYKDg9G7d28sWLAABgYGePz4MY4fP45Zs2ZBT08PXbp0eeN6EhIS4OfnhyFDhmDOnDk4c+YM5s6dC11dXfTt2xfAy8R7/PjxAIA5c+ZAT08Phw8fxtSpUxESEqJ0HEuWLMHhw4fxxRdfwMzMDAcOHIC3tzf27NkDa2trodyePXtgYmKCjh07IioqqsTrbd68GSEhIZgwYQJsbW3x9OlTxMXFobCwUChTVFSESZMmITk5GX5+fjA2NsZ///2HO3fuCGWuXr2K33//HYMHD0a7du3w9OlTbN68GUOHDkVERAQaNGgAANi9ezdyc3MxZcoUNGnSBBcvXkRgYCBu376NpUuXiuLavHkzpk6dChsbG/z7779YvXo1UlNTsWrVqlKf0aNHj/DTTz8hLEz8DfKffvoJ3333Hdq3b4+ZM2eiadOmyMjIwB9//IHFixcDAIYPHy46Jzo6Gi4uLkLyXp5reHt7w8PDA+PHj4epqWmpMRMREREREVUWpRP4Bw8ewMHBQbTPwcEBMpkMT548qbIEPiYmBsHBwfDz84O/v7/omLu7O8aMGQNNzcr5vP3mzZthY2ODb7/9FgDg7OyM5ORkrF+/Xkjgr127hsTERISGhqJDhw4AABcXFyQkJODo0aNKJfCPHj3C3r178fXXX2P06NEAgC5duuDjjz/Ghg0bsHnzZtH9q6urIz4+vsQEPjExERs2bMCmTZvQrVs3Yf/rIwL279+PixcvIjIyssS1DBwcHHD06FHRM7W3t4erqysOHTqEzz//HACwcOFCIZkHgA4dOqCoqAhr167FrFmzhGMRERHo378/fHx8hGf69OlThISEoKCgoNS227NnD5o3b442bdoI+27cuIHFixdj4MCBWLJkCdTU1IRjvXr1wueff44HDx4oXCsmJga+vr4Vukbz5s1hb2+PsLAwzJ07t8R4iYiIiIiIKpPS48ylUqnQWyknH1JcUFBQuVGVYseOHTAwMMDEiROLPW5jY4PWrVsL26NHj4avry8iIiLQp08ftGvXDhMmTEBGRgZSUlIwbtw42NnZwcPDA/Hx8cJ5UqkU8fHxQqIu169fP9y+fRv3798H8H/3XrduXaGMuro6dHV1hbUCynLjxg0UFhaiU6dOwj41NTV07twZp06dglQqFV27LAcOHICxsbEoeS/Ovn370Ldv31IXIqxXr55CUt24cWM0aNAAqampwr5Xk3c5KysryGQypKWlCfsKCgpQp04dUbm6desq9awOHTqk8BIiNDQU6urq+PLLL0WJt5ypqanCNIR///0XKSkpcHV1rfA1+vbtiyNHjlTpb5+IiIiIiN5v5ZoonpKSgqtXrwp/bt68CQC4e/euaL/8T2UrKCjAuXPn4OzsXK5e9mvXriE0NBSzZ89GQEAAEhISMH/+fEyZMgWurq4IDAxEgwYN4O/vj+fPnwMA7t27h/z8fIWRBebm5gBe9nIDgK2tLVq1aoU1a9YgOTkZmZmZ2LVrF5KSkvDpp58qFZ88QX99nrZEIoFUKhVeFijr4sWL+PDDD7Fp0ya4uLigTZs2GD58OC5evCiUyc/Px7Vr12BkZITZs2fD1tYWdnZ2mDp1qijhLs6dO3fw5MkT4VmU5Ny5c5BIJDA2Nhb2DR06FL/88gvi4uLw/PlzXLp0Cbt27cLw4cNLbdO7d+8iJSUF9vb2ov1nz55FmzZtoK+vX2osr4qOjkbr1q2FFxcVuYa9vT2ePn2K69evK30OERERERHRmyjXWPN169Zh3bp1CvsDAgJE2zKZDGpqapWe3Dx79gxSqRRNmjRRqO/Vud3q6uqinurs7GwEBQUJvcQ3b97E9u3bsXDhQowYMQIAYGhoiP79+yMuLg69evVCRkYGgJc90K+Sb8uPa2pqYufOnZg4cSJ69eoFAKhduzbWrFkDOzs7pe6refPmAIBLly6Jkt0LFy6I6lJWWloarly5gn/++QfffPMNtLW1ERQUhM8//xy//fYbPvjgAzx79gz5+fkICQmBo6MjNmzYgPT0dHz//ffw9/fH7t27i722TCbDokWLYGhoCA8PjxJjSEpKQmhoKIYPHy5anM7X1xdSqRReXl5Cr/vHH3+MOXPmlHpPly9fBgBYWFiI9qempsLGxkah/Ks946//HqKjo9G9e/c3ukbLli2hoaGBS5culbiI4LsuJydH6VEm1VVOTo7on1T12AbVA9tB9dgGqsc2qB7YDqrHNqh68vxZGUon8K8uRKZqr99cVFQUpk6dKmyPHDkSCxYsELYtLS1FQ7zlC4+9+vk7+b6HDx+WKxb5wm0ymQwbN26Erq4ujh07hhkzZiAkJAROTk5lXuPDDz9E+/btsXLlSjRp0gSmpqY4cOAA/vrrr2LvtywymQwvXrzAunXrYGlpCQBo164devTogR9//BFTp05FUVERAEBXVxcbNmwQev8bNmwILy8vxMXFKQwbB4DAwECcOXMGW7duhY6OTrH1Z2dnw9/fH8bGxpg2bZro2I8//ojQ0FB8/fXXaN26Nf7991+sW7cO3333Hb755psS7yktLQ3q6uqoX7++wrHXn8/ly5cxZMgQYdvV1RXBwcEAgKdPn+LixYsKc9fLcw3g5YubunXriqYRvG/u3LnzzvzFnpSUpOoQ3ntsg+qB7aB6bAPVYxtUD2wH1WMbVK3XR2OXROkE/pNPPqlwMJVFX18fEolEIcl2cXFBeHg4ABQ7N/71XnT5Z81enbcuf2B5eXkAAD09PQBAVlaW6NzMzEzR8fDwcFy6dAmxsbHCSwIXFxfcu3cPq1evLrEn+3XLli3DF198Iax03rRpU0yaNAmBgYEwMDBQ6hpy9erVg76+vpC8Ay+fXevWrXHr1i2hjJqaGuzt7UU/FicnJ2hoaODWrVsKCfzevXuxceNGLF68uNjkHng5HWDy5MnIyMjAnj17REn+06dPsXz5csyePVtYrM/R0RF16tTBrFmz4OnpCTMzs2Kvm5eXB01NTYVE29DQUOH30LJlS+H38PpLgdjYWHzwwQeilf3Lew05iUQi/F7eR2ZmZu9ED3xSUhJMTU2hra2t6nDeS2yD6oHtoHpsA9VjG1QPbAfVYxtUPXmOpozKWa69imhqasLe3l74HJqGhgaAl8m0fBizsm8uymJiYgItLS0kJiaKVpKXz32Xz42/desWGjVqpLCIm5WVFQ4dOqR0fc2aNcP+/ftx//595ObmwszMTFiw79XvrSujZcuWuHfvXrHH5AmntrZ2qdd9PTH9/fffsXDhQkyZMkXUM/2qoqIizJw5E1evXkVYWJjCVIfk5GRIpVJYWVmJ9ssXHbx3716JCbyenh6kUiny8vJEiyk6OTkhIiICGRkZwksVbW1t4ffw+rflY2Ji0K1bN9GLgPJeQy4rK6tc8+bfNe/SX+ja2toljiihqsE2qB7YDqrHNlA9tkH1wHZQPbZB1SnPiGulF7H77bffyv3nbfDy8kJqaiqCgoLeyvXlJBIJOnTooPCptsjISJibmwtz1Y2MjPDw4UOkp6eLyl29erXciTcAGBsbo2XLlsjPz0d4eDiGDh1a7mt0794dz549E61B8PTpU1y9elXU89y9e3ecO3dOlKyfOXMGhYWFonLx8fGYPn06hg4dismTJ5dYb0BAAKKjo7Fp0yaFuerAy2cFQGGBwytXrgCAaP7/6+SJ/esL+nl6eqKwsBArVqwo8Vy5/Px8nDp1SjT/vbzXkEtPT0dOTk6JLxyIiIiIiIgqm9I98FOmTIGamlqZQ2blbw/U1NRw7dq1N4uuGK6urvDx8cH69etx48YNuLu7w9DQEFlZWUhISEBaWlqJPablNXHiRHh6emLhwoVwd3dHfHw8IiIisGbNGqFM//79ERwcDG9vb/j4+Ahz4M+cOVOuhPDHH39EnTp10KRJE6SkpGDHjh2oVasWvL29ReXOnj2L9PR0YZjFmTNnkJKSgqZNmwo9xr169ULbtm0xZcoUTJs2DbVq1cKWLVsgkUjw2WefCdcaN24cDh8+jEmTJsHT0xPp6elYtWoVHBwc4OzsDAC4ffs2Jk+eDFNTUwwYMEBYWA94+ek4ExMTAEBQUBB2796NcePGQSKRiMq1bNkSderUQcOGDdGrVy+sW7cOhYWFwpD+wMBAdOzYsdRV7W1sbKCpqYkrV66IyllaWmLu3Ln47rvvkJycjEGDBsHY2BjPnz/HlStXcPPmTXTu3BkAkJCQAKlUKlr7oLzXkJMvqufg4FByoxIREREREVUipRP40NDQMsukpaVh69atuH79ujC8/W2YMWMGHBwcEBYWhoCAAGRnZ0NPTw/W1tZYsmRJqaujl0f79u0RGBiItWvXIjw8HEZGRli0aBHc3d2FMk2aNEFoaCjWrl2LgIAA5ObmwtTUFCtWrMCAAQOUrksqlWLDhg14+PAh9PX10adPH0ydOlVh2EpgYCDOnj0rbK9cuRLAyzUKli1bBuDliulbtmzB0qVLsWDBAuTn56N9+/YICwsTzaeXx75kyRL4+/tDW1sbPXv2xFdffSW8iLl48SKysrKQlZUlrNgv92qdf/75JwBg27Zt2LZtm6hcaGgoOnToAABYvnw5Nm7ciJ9//hmPHj2CgYEB+vfvD39//1Kfj46ODrp06YI//vhD4bl+9tlnsLS0xI4dO/D999/j2bNn0NXVhaWlJaZNm4bBgwcDeDl8vkOHDsUO/Vb2GnInT55E+/bt0bBhw1LjJiIiIiIiqixqskpYherx48cICQnBnj17kJ+fj48//hgTJ04UemeJKsOJEycwY8YMnD59ukLzr93c3ODp6YmRI0e+URwFBQVwdXXFzJkzMXDgwApdQ96D3/b+GODp+TeKp8rVtwPcz6k6ikrx4sULXL9+HVZWVpzjpSJsg+qB7aB6bAPVYxtUD2wH1WMbVD0hN1Di89RvtIjd48ePsWXLFuzduxcFBQXo378/Jk2ahGbNmr3JZYmK1b17d5iZmWHfvn3w9PQs9/mvr2dQUREREdDV1cVHH31UKdcjIiIiIiJSRoUS+LS0NGzZsgX79u1DQUGB0OPOxL14BQUFJR5TU1N7q9MN3iVqampYuHAhbt68qfI4Fi9eDE3NGvURByIiIiIiquHKlYEwcS+/+/fvo2fPniUed3Jywq5du6owoprNxsYGNjY2Ko2hPGsbEBERERERVRalE/hFixZh3759KCwsxMCBAzFhwoRSP/tFLxkaGiI8PLzE45W1Yj4RERERERG925RO4H/88UeoqamhZcuWePz4MRYtWlRqeTU1NWzevPmNA6zpJBKJUosREBEREREREZVG6QTeyMgIAPD8+XP8888/ZZaXf4aMiIiIiIiIiN6c0gn8iRMn3mYcRO8nPStVR1B+NTFmIiIiIqJ3AJfRJlKljmGqjqBiZIWAGr+eQERERERUlSolgb99+zaOHTuGtLQ0mJmZYfDgwahTp05lXJronSWVSpGTkwNtbW1Vh1J+TN6JiIiIiKpcuRax27VrF37++Wc0aNBA2H/ixAlMnToV+fn5orJ79uwRlSMiRTKZTNUhEBERERFRDaGubMETJ06gWbNmoqS8oKAA8+bNg4aGBpYuXYojR45gxowZ+O+//xAUFPRWAiYiIiIiIiJ6HymdwN+6dQu2traiffHx8UhPT8eYMWPwySefoFWrVvD29kbfvn0RGxtb2bESERERERERvbeUTuCfPXuGxo0bi/bFxcVBTU0NvXv3Fu23t7fHgwcPKidCIiIiIiIiIlI+gW/YsCEeP34s2peQkIDatWvD0tJStF8ikUBLS6tyIiR6h6mpqak6BCIiIiIiqiGUTuDbtGmDgwcPIjs7GwDw77//4vLly+jSpQs0NcVr4SUmJir01hORmEQiqVkr0MsKVR0BEREREdF7TelV6CdPnowhQ4bAzc0NLVu2xNWrV6GmpgYfHx+Fsr///jucnZ0rNVCid9LpkUDGdVVHUTY9q5r7zXoiIiIioneE0gm8hYUFdu7ciaCgICQnJ6Ndu3YYN24c2rRpIyoXHx8PbW1t9O3bt9KDJXrnZFwHnp5XdRRERERERFQDKJ3AAy8Xp9uyZUupZTp06IAjR468UVBEREREREREJKb0HHgiIiIiIiIiUh0m8EREREREREQ1ABN4IiIiIiIiohqACTwRERERERFRDVAtE/jY2Fh4e3vD2dkZ1tbW6NixI3x8fBAREYGioqJKq+fEiRP4+OOP0bZtW7i5uWH//v0KZVJSUjB9+nR07twZdnZ2GDx4MKKioipc5/Hjx2FhYYGPPvpI4dg///wDX19fODs7o3379hg5ciTOnDkjKhMYGAgLCwuFPz///LPC9R49eoQvv/wSzs7OsLGxgbu7O3755Zcyr2VhYYEFCxaIrlVUVIQffvgBffv2RZs2bdCpUyfMmDFDoc7MzEwsWrQInTt3Rtu2bdGrVy9s375dIa4vvvgCDg4OsLOzw4QJE5CcnKz0MxwyZAjCwsSfNJPJZPjll1/g6ekJJycntGnTBl26dMGUKVMQGxtb7HVWr16NMWPGlPsaRUVFcHNzEz1LIiIiIiKit61cq9BXhdWrVyM4OBi9e/fGggULYGBggMePH+P48eOYNWsW9PT00KVLlzeuJyEhAX5+fhgyZAjmzJmDM2fOYO7cudDV1RU+gSeVSjF+/HgAwJw5c6Cnp4fDhw9j6tSpCAkJKXccubm5WLJkCRo2bKhwLD09HWPHjkWzZs2wePFiaGlpYdeuXfD29kZ4eDgsLCyEsrVr18bOnTtF5zdr1ky0nZqaimHDhsHMzAzfffcd6tSpg3///RdSqVQoM3ToUIV7+Ouvv7By5Up07dpVtH/BggWIjo7GpEmT0KpVK6SlpeHvv/8WlXnx4gVGjx4NDQ0NzJkzBx988AGSkpKQnZ0tlCksLMT48eORk5OD7777DhKJBBs2bMCYMWNw5MgR6OrqlvoMf//9d6SkpGDw4MHCPplMhpkzZyIyMhIDBw7E6NGjoa+vj//++w9Hjx6Fj48Pjh49ihYtWoiuFR0dLVynPNdQV1eHj48PAgMD0a9fP2hqVrt/jYiIiIiI6B1UrTKPmJgYBAcHw8/PD/7+/qJj7u7uGDNmTKUlS5s3b4aNjQ2+/fZbAICzszOSk5Oxfv16IYG/du0aEhMTERoaig4dOgAAXFxckJCQgKNHj5Y7gQ8ODoaRkRGMjY1x5coV0bG4uDg8efIEe/fuhbGxMQDAyckJTk5OQq+9nLq6OmxtbUut6/vvv0fjxo2xdetWaGhoCLG/qnHjxmjcuLFo3+7du6GnpydK4OPi4nDw4EEcOHBAFIeHh4fo3C1btuD58+f45ZdfoKOjAwDCc5M7duwY/vnnHxw+fBiWlpYAIPTU79u3D2PHji31vnbu3AkPDw/Url1b2PfTTz8hIiICS5cuxaBBg0TlBwwYgNjYWGhra4v2p6Sk4J9//oGrq2uFrtGvXz8sWrQIMTEx6NWrV6kxExERERERVYZqNYR+x44dMDAwwMSJE4s9bmNjg9atWwvbo0ePhq+vLyIiItCnTx+0a9cOEyZMQEZGBlJSUjBu3DjY2dnBw8MD8fHxwnlSqRTx8fFCoi7Xr18/3L59G/fv3wcAFBQUAADq1q0rlFFXV4euri5kMlm57u3evXvYsWMH5s2bV+zx/Px8hbpq1aoFLS2tcteVnZ2No0eP4rPPPhOSd2Xk5eXh999/h5ubGyQSibB/7969cHJyEiXvxQkPD8fgwYOF5L04165dg4GBgZC8A0CjRo3QqlUrnDhxotTrJycnIyEhQaHdduzYgbZt2yok3nLdunVDkyZNRPtiYmJgZmYGU1PTCl1DW1sb3bp1w8GDB0uNmYiIiIiIqLJUmx74goICnDt3Dm5ubuXqZb927RqePn2K2bNnIzs7G4sWLcL8+fORkpKCgQMHwsvLC8HBwfD390d0dDR0dXVx79495OfnKwypNjc3BwAkJibC2NgYtra2aNWqFdasWYMFCxYIQ+iTkpKEnntlLV68GAMGDBAlrq/q3r07GjZsiGXLlmHatGnQ1NTE9u3boaamhgEDBojK5ubmwtnZGZmZmTA1NcXYsWPx6aefCsevXr2K/Px8aGpqYtSoUTh//jz09fUxcOBAfPHFF9DS0io2hujoaGRnZyvMz7948SK6d++OxYsX4+DBg5BKpXB0dMS8efNgZmYGALh//z7S0tJQv359TJgwAadOnYKOjg769OmDr7/+Whgan5eXJ3o5ICeRSJCYmFjqMzxz5gw0NTVhY2Mj7Hvw4AGSk5MVRgOU5cSJE+jevfsbXcPOzg7r169HUVER1NWr1buwtyYnJ6fcL5Sqs5ycHNE/qeqxDaoHtoPqsQ1Uj21QPbAdVI9tUPVkMhnU1NSUKlttEvhnz55BKpUq9JTKZDIUFhYK2+rq6qJkKTs7G0FBQWjQoAEA4ObNm9i+fTsWLlyIESNGAAAMDQ3Rv39/xMXFoVevXsjIyAAA1KtXT1SXfFt+XFNTEzt37sTEiROFYdK1a9fGmjVrYGdnp/S9nThxAufPn8exY8dKLKOnp4ewsDD4+voKQ/P19fUREhIimt9uYmKCmTNnonXr1sjLy8ORI0cwf/58ZGVlYdy4cQCAx48fAwDmzZuHTz/9FH5+frh06RLWr18PdXX1YhefA4CIiAg0atQIjo6Oov1paWk4cOAAWrZsiZUrVyI/Px9r1qzBuHHjcPToUdSqVUuoc/ny5ejTpw9CQkKQlJSEVatW4cWLF1i9ejUAwNTUFA8fPsSjR4/QqFEjAMDz589x69Yt5ObmlvocL1++DFNTU9ELgNTUVAAo83ejoaEh/Evx4sULnD17Fj4+PhW+BgBYWloiOzsbt2/fRqtWrUqN/V1x586dd/Iv86SkJFWH8N5jG1QPbAfVYxuoHtugemA7qB7boGoV18lZnGqTwMu9/uYhKioKU6dOFbZHjhwpWiHd0tJSSN4BCEOiO3bsqLDv4cOH5YolNzcXU6ZMgUwmw8aNG6Grq4tjx45hxowZCAkJgZOTU5nXyMvLw5IlS+Dv7y+K83VPnjyBn58fTExMMGfOHGhoaGDv3r2YOHEiwsLChNEBr/fGu7q6Ij8/H5s3b4anpye0tLSElfo7duyIr776CsDLOf7Pnz/H9u3bMXnyZNEccuDl6vGxsbEYNWqUQm+yPJHdvHmzsACfubk5PDw8cOTIEQwZMkSo08zMDMuXLwfwcs69pqYm5s2bh2nTpqFZs2b46KOPsG7dOsyZMwcLFy6ElpYWli9fjhcvXpQ53D81NRX169cv9tjrv5vt27djxYoVwvbs2bOFFxynT59G7dq14eDgUOFrABBiSUtLe28SeDMzs3euBz4pKQmmpqYK6yRQ1WAbVA9sB9VjG6ge26B6YDuoHtug6t26dUvpstUmgdfX14dEIlFIsl1cXBAeHg4Axc6Nf70XXT48/NW55PK3GXl5eQBe9nYDQFZWlujczMxM0fHw8HBcunQJsbGxQvLt4uKCe/fuYfXq1di9e3eZ97Vz506oq6vDw8NDuH5+fj6KioqQmZmJ2rVrQyKRYOvWrcjIyMCBAweEeF1cXODh4YFNmzZh1apVJdbh7u6OqKgo3Lt3D+bm5sIzcXZ2FpVzcXFBUFAQ7t69qzCfPSoqClKpFP3791e4fr169dC4cWPR6vktWrRA48aNhR+b/Jm9vmidPIZ///0XzZo1g76+PlavXo05c+YIoxocHR0xcOBAhU/mvU4qlSq8mTI0NASg+HJmwIABwguWIUOGiI6dOHECXbp0EaZqVOQawP/9rsoaOfAueVf/EtfW1i517QZ6+9gG1QPbQfXYBqrHNqge2A6qxzaoOsoOnweq0SJ2mpqasLe3R1xcnGjYsp6eHtq2bYu2bdsqPaygLCYmJtDS0lKYcy3fls+Nv3XrFho1aqTQc25lZYV79+4pVVdiYiLu3r0LFxcXODo6wtHREREREbh9+zYcHR2Fb8/funULLVq0EN2jhoYGLCwslK5LrmXLlqUel7/IeFVERARatGghWiRQmevJr9WsWbNS2+fVOrt06YKYmBhERkbi+PHj+PHHH/H48eMyV9bX09NTeOnSpEkTNGvWDH/++adof8OGDYXfzatkMhn++OMPYfX5ilxDTv5CRl9fv9S4iYiIiIiIKkO1SeABwMvLC6mpqQgKCnqr9UgkEnTo0AFRUVGi/ZGRkTA3Nxc+42ZkZISHDx8iPT1dVO7q1ato2rSpUnV5e3sjNDRU9Kdz585o2rQpQkND0aNHD6Gu27dvixLdwsJC3Lhxo8y6IiMjUa9ePZiYmAAAmjZtig8//BCnT58WlZMPHX89IU9NTcXZs2cVFq+T6969O27duoW0tDRh3+3bt/Hw4UNYW1sDePlMO3XqhLi4OIU6AQjl5DQ0NGBubo5mzZrh9u3bOH36NIYOHVrqfZqZmQlfCHiVl5cXLl68iEOHDpV6PvByHn16errCd+7Lcw25lJQUAP83RYOIiIiIiOhtqjZD6IGX87l9fHywfv163LhxA+7u7jA0NERWVhYSEhKQlpYmrGb+piZOnAhPT08sXLgQ7u7uiI+PR0REBNasWSOU6d+/P4KDg+Ht7Q0fHx9hDvyZM2dEc6NLY25uLsxflzt48CAePXokGm4+dOhQhIeHY9KkSRg5ciQ0NDSwZ88e3L17F4sWLRLKDRo0CAMHDkSLFi2Qm5uLI0eO4LfffsOcOXNEq8tPmzYNkyZNwuLFi+Hq6orLly9j+/btGDdunMJQmMjISBQVFRU7fF4e265du+Dr64tJkyYhPz8f69atg4mJiWjldj8/PwwfPhwzZszAJ598grt372LVqlXo37+/8HIBePmNeltbW9SpUwc3b97E5s2bMXDgQIXv1L/O3t4eGzduxMOHD0Xfr//ss89w7tw5fP3114iPj0ePHj1Qv359PHv2DKdOnQIA4XcTHR0NOzs7hV7z8lxD7sqVKzA3Ny91bQMiIiIiIqLKUq0SeACYMWMGHBwcEBYWhoCAAGRnZ0NPTw/W1tZYsmRJuT/1VZL27dsjMDAQa9euRXh4OIyMjLBo0SK4u7sLZZo0aYLQ0FCsXbsWAQEByM3NhampKVasWKGwmNybatOmDbZu3YpNmzbh66+/RlFREVq2bIktW7aIVoU3MTHBDz/8gMePH0NNTQ0ffvghvv/+e3z88cei6/Xo0QOrV6/Gpk2b8PPPP8PQ0BD+/v7CyuuvOnLkCGxsbERJ9qvq1KmDnTt3YvHixZg1axbU1NTQuXNnzJkzRzQnuk2bNggJCcHKlSsxceJE6OnpYdiwYZg2bZroeg8fPsTChQuRkZEBY2NjTJgwAZ6enmU+IycnJ+jr6+OPP/4QfTZPTU0NK1euRNeuXREeHo45c+YgJycH9evXh62tLYKDg4Uh8zExMejXr5/CtctzDbk//vgDbm5uZcZNRERERERUGdRk79KS0vTOW7ZsGa5du4bQ0NByn/vo0SN07doVv/76a5nrBJTl33//xYABAxAVFSX6zJ+yLl++DABoe38M8PT8G8VSJerbAe7nVB1FpXvx4gWuX78OKysrLtKiImyD6oHtoHpsA9VjG1QPbAfVYxtUPSE3KGHtrVdVqznwRGX5/PPPcenSJdy4caPc5zZq1Ag3b9584+QdePmJuQEDBlQoeSciIiIiIqqIajeEvqYpKCgo8ZiamlqZ3zan8jE0NMTSpUsVFhasSkVFRWjevDkGDhyoshiIiIiIiOj9wwT+Ddy/fx89e/Ys8biTkxN27dpVhRG9H15dp0AV1NXVMWHCBJXGQERERERE7x8m8G/A0NAQ4eHhJR6vrBXziYiIiIiIiJjAvwGJRKLUQgNEREREREREb4oJPJEq6VmpOgLl1JQ4iYiIiIjeYUzgiVSpY5iqI1CerBBQ46KMRERERESqws/IEamIVCpFTk6OqsNQHpN3IiIiIiKVYgJPpEIymUzVIRARERERUQ3BBJ6IiIiIiIioBmACT0RERERERFQDMIEnIiIiIiIiqgGYwBOpkJqamqpDICIiIiKiGoIJPJGKSCQSaGtrqzYIWaFq6yciIiIiIqXxO/BEqnR6JJBxXTV161nVrO/QExERERG955jAE6lSxnXg6XlVR0FERERERDUAh9ATERERERER1QBM4ImIiIiIiIhqACbwRERERERERDUAE3giIiIiIiKiGoAJPBEREREREVENwASeiIiIiIiIqAaoMQl8bGwsvL294ezsDGtra3Ts2BE+Pj6IiIhAUVFRpdVz4sQJfPzxx2jbti3c3Nywf/9+hTIpKSmYPn06OnfuDDs7OwwePBhRUVEVrvP48eOwsLDARx99JNp///59WFhYKPz59NNPFa6xf/9+9O3bF23atEHv3r2xa9euYuvat28f3Nzc0LZtW3z88ceIjo4uNbZJkybBwsIC27ZtE+3fvXs3Pv/8c3Tq1An29vb49NNPcfz4cYXzw8LC4OvrC2dnZ1hYWODYsWPF1vPo0SP4+/vDzs4OTk5OmDt3LrKzs0uNTe7mzZuws7NDeno6AgMDi31mr/7p0aOH6Pw9e/agV69eZZ5nYWGBAwcO4O+//0aHDh2Ujo+IiIiIiKgy1IjvwK9evRrBwcHo3bs3FixYAAMDAzx+/BjHjx/HrFmzoKenhy5durxxPQkJCfDz88OQIUMwZ84cnDlzBnPnzoWuri769u0LAJBKpRg/fjwAYM6cOdDT08Phw4cxdepUhISElDuO3NxcLFmyBA0bNiyxzPTp09GhQwdhW1dXV3Q8MjISc+bMgaenJ1xdXZGQkIClS5dCTU0No0aNEsr9+uuvmD9/PiZMmABnZ2dERkbCz88PYWFhsLW1Vag3NjYWFy9eLDamoKAgdO7cGSNGjICOjg6OHTuGyZMnY9myZfjkk0+EcocPHwYAdOvWDYcOHSr2Wvn5+cIzXbVqFXJzc7F8+XLMmDEDwcHBJT4XubVr12LQoEFo0KABhg4dKmqDffv2ISIiAjt37hT2SSQS0fnR0dFwdXVVeIEybNgwjB49WrTfxMQEDRo0QKtWrbB9+3ZMmTKlzPiIiIiIiIgqQ7VP4GNiYhAcHAw/Pz/4+/uLjrm7u2PMmDHQ1Kyc29i8eTNsbGzw7bffAgCcnZ2RnJyM9evXCwn8tWvXkJiYiNDQUCGpdnFxQUJCAo4ePVruBD44OBhGRkYwNjbGlStXii3TvHnzYhNsufXr16NPnz6YO3cuAKBTp07IzMxEYGAghg0bBi0tLaGch4cHvvjiC+H+/vnnH2zcuBEhISGia0qlUixevBjTp0/HnDlzFOo8cOAAGjRoIGx36tQJKSkp2L59uyiB3717N9TV1XH//v0SE/ioqCj8+++/iIyMRIsWLQAA9erVw7hx43Dp0iXY2NiUeO/JycmIjo7GgQMHAACNGzdG48aNheMnT56Eurp6ic8vLy8PZ86cwcaNG4st06RJk2L3DxkyBMuXL8fEiROF50tERERERPQ2Vfsh9Dt27ICBgQEmTpxY7HEbGxu0bt1a2B49ejR8fX0RERGBPn36oF27dpgwYQIyMjKQkpKCcePGwc7ODh4eHoiPjxfOk0qliI+PFxJ1uX79+uH27du4f/8+AKCgoAAAULduXaGMuro6dHV1IZPJynVv9+7dw44dOzBv3rxynfeqnJwcJCUloVOnTqL9nTt3xrNnz3DhwgUALxPdpKQkuLu7i8r169cPcXFxkEqlov3btm1DvXr1MGjQoGLrfTV5l7OyskJqaqpon7p62T+xP/74AxYWFkLyDrx8IaCvr4/Y2NhSzz106BCaNWsm+g2UR1xcHNTV1eHo6Fiu83r16oWsrKwy4yMiIiIiIqos1boHvqCgAOfOnYObm1u5etmvXbuGp0+fYvbs2cjOzsaiRYswf/58pKSkYODAgfDy8kJwcDD8/f0RHR0NXV1d3Lt3D/n5+aIkEgDMzc0BAImJiTA2NoatrS1atWqFNWvWYMGCBcIQ+qSkJKHnXlmLFy/GgAEDYGlpWWq5hQsXYtq0adDX10fPnj0xc+ZM6OvrA3j54kEmkykMC5dv3759G46OjkhMTAQAmJmZKdxffn4+kpOThXv977//sGXLFuzYsQNqampK38/ff/+t8PyUkZiYqHCempoazMzMhLhLcvr0adjZ2ZW7TrkTJ06gc+fOCs+vLHXq1EHLli1x+vRp9OrVq8L1Vwc5OTnlfvn0rsjJyRH9k6oe26B6YDuoHttA9dgG1QPbQfXYBlVPJpMpnXdV6wT+2bNnkEqlaNKkiWi/TCZDYWGhsK2uri7q6c3OzkZQUJDQS3zz5k1s374dCxcuxIgRIwAAhoaG6N+/P+Li4tCrVy9kZGQAeDl0+1XybflxTU1N7Ny5ExMnThQSt9q1a2PNmjXlSiRPnDiB8+fPl7ioG/AyCR8xYgQ6d+6MevXq4eLFiwgKCsKVK1ewb98+aGlpQU9PD/r6+rh06ZKot1ze8y6PW9n7A4ClS5eid+/epQ7bf92RI0dw/vx5bNy4Uelz5DIzM0UjGuT09PREcb1OJpPhypUrb5RAx8bGYurUqRU619LSssQ1AmqSO3fuvPd/QSclJak6hPce26B6YDuoHttA9dgG1QPbQfXYBlVL2Q7Fap3Ay73+NiIqKkqUdI0cORILFiwQti0tLUVDvE1NTQEAHTt2VNj38OHDcsWSm5uLKVOmQCaTYePGjdDV1cWxY8cwY8YMhISEwMnJqcxr5OXlYcmSJfD39y92KLqcoaEhFi5cKGw7OTmhVatW8PX1xe+//45+/foBAD777DNs27YNDg4O6Nq1K86dO4fQ0FAAis+uLKdOncKpU6dKfbHwuhs3buCbb77BoEGDqrQ3OiMjA1KptNRnWJrr168jNTUVrq6uFTq/fv36SEtLq9C51YmZmdl73QOflJQEU1NTaGtrqzqc9xLboHpgO6ge20D12AbVA9tB9dgGVe/WrVtKl63WCby+vj4kEolCku3i4oLw8HAAKHZu/Ou9zPJFxl7t5ZW/4cjLywPwsrcXALKyskTnZmZmio6Hh4fj0qVLiI2NFRJHFxcX3Lt3D6tXr8bu3bvLvK+dO3dCXV0dHh4ewvXz8/NRVFSEzMxM1K5du8Q3MN26dYOOjg6uXr0qJPC+vr64d+8eZs2aBZlMBh0dHcycORPffvstDAwMFO5Pvq+4+1u0aBE8PT2hra0tHJM/p8zMTIVnm5KSAm9vb9Hif+VVr169Yj/JlpGRoTD64lXytivv8He5EydOwMbGpsIvACQSiRBDTca/mF8+Ax0dHVWH8V5jG1QPbAfVYxuoHtugemA7qB7boOqUp9O1Wi9ip6mpCXt7e8TFxYmGzOvp6aFt27Zo27ZthZO315mYmEBLS0thzrV8Wz5H+9atW2jUqJFC0mdlZYV79+4pVVdiYiLu3r0LFxcXODo6wtHREREREcJ89eK+PV+a2rVrY9WqVTh9+jR++eUX/Pnnn2jbti0AoF27dqL4i7s/LS0tNGvWDMDL4dRBQUFCXPLF3datWwdHR0dRwpqeno5x48bhgw8+wIYNGyq8GnuLFi0U4pLJZLhz506pc+rl6wC8+qKhPGJiYirc+y6vVx4DERERERHR21ate+ABwMvLC76+vggKCsLkyZPfWj0SiQQdOnRAVFQUxowZI+yPjIyEubk5jI2NAQBGRkZ4+PAh0tPTRUn81atX0bRpU6Xq8vb2Fn1qDQC2bNmCO3fuYOnSpcLw/uJER0fjxYsXQoL+qgYNGggxhYWFoX379kIC3KxZM5iamuLYsWOiYe6RkZFwcXERXoTIh96/ytPTE8OHD0e/fv2EJP358+fw9vZGfn4+QkNDUadOHaXuvThdu3bFL7/8IgzVAV6uDv/s2TN069atxPNq1aoFIyMj4QsB5fH48WNcvnwZ3333XUXDRkpKisKigERERERERG9LtU/gXV1d4ePjg/Xr1+PGjRtwd3eHoaEhsrKykJCQgLS0NOjq6lZKXRMnToSnpycWLlwId3d3xMfHIyIiAmvWrBHK9O/fH8HBwfD29oaPj48wB/7MmTNYsWKFUvWYm5sLK77LHTx4EI8ePRK+LQ8Ay5Ytg5qaGmxtbVGvXj1cunQJwcHBaNOmjSgJj42Nxb1799CyZUtkZGTgyJEjiI+Px88//yyqw9/fHzNnzoSJiQk6dOiAyMhIXLp0CT/++KNQ5tX6XyU/59Vr3bhxA4sXL8Z///2H//77Tzj26uJ3ly9fRkpKCtLT0wFAWPStQYMGwnoBbm5uwlcBpk+fjpycHKxYsQKurq6lfgMeAOzt7XH16tVSyxQnJiYGTZo0KfMLAKW5cuUKvLy8Knw+ERERERFReVT7BB4AZsyYAQcHB4SFhSEgIADZ2dnQ09ODtbU1lixZAg8Pj0qpp3379ggMDMTatWsRHh4OIyMjLFq0SPTt9CZNmiA0NBRr165FQEAAcnNzYWpqihUrVmDAgAGVEoecubk5fv75Z+zduxe5ublo1KgRhgwZgilTpog+q6epqYnw8HDcvXsXmpqacHJywp49exReEnz00UfIyclBSEgItmzZAjMzM2zYsKFCn2H7888/AQBffvmlwrGbN28K/zssLAwHDx4Utrdv3w7g5YJ8u3btAvByjYKtW7di0aJFmD59OjQ1NdG7d2/MmTOnzDjc3Nwwc+ZMZGdnl2sUQExMTKm9+2W5evUq0tPT4ebmVuFrEBERERERlYea7H1dfpreCfn5+ejevTtmzpyJgQMHKnWOVCpFhw4dsHbt2gon8cuXL8fVq1eLnXKgjMuXLwMA2t4fAzw9X6FrvLH6doD7OdXUXU28ePEC169fh5WVFRdpURG2QfXAdlA9toHqsQ2qB7aD6rENqp6QGxQzTfp11XoRO6KyaGlpwdvbu1yJtEQiwfnz5yucvGdnZyM8PBz+/v4VOp+IiIiIiKgiasQQ+pqmoKCgxGNqamrQ0NCowmjefSNGjEB2drbCwoJvy3///YepU6cKK/QTERERERFVBSbwlez+/fvo2bNnicdfnftNlUMikbzVLxS87sMPP8SHH35YZfUREREREREBTOArnaGhIcLDw0s8Xlkr5hMREREREdH7hQl8JZNIJEotPkBERERERERUHkzgiVRJz+r9rJuIiIiIiMqNCTyRKnUMU239skJAjYsqEhERERHVBPyMHJGKSKVS5OTkqDYIJu9ERERERDUGE3giFZLJZKoOgYiIiIiIaggm8EREREREREQ1ABN4IiIiIiIiohqACTwRERERERFRDcAEnkiF1NTUVB0CERERERHVEEzgiVREIpFAW1tb1WG8oUJVB0BERERE9N7gd+CJVGokgOuqDqKCrACo+Dv2RERERETvESbwRCp1HcB5VQdBREREREQ1AIfQExEREREREdUATOCJiIiIiIiIagAm8EREREREREQ1ABN4IiIiIiIiohqACTwRERERERFRDVCtE/jY2Fh4e3vD2dkZ1tbW6NixI3x8fBAREYGioqJKqePPP//EjBkz0KtXL1hYWODbb78tttzGjRvh5eWF9u3bw8LCApcvXy53XZGRkfD390fXrl1hYWGBbdu2lVj2woULGDt2LOzs7GBvb49PP/0U16//3+fGDhw4AAsLC4U/K1euFF1n+fLl8PDwEK4zePBg/Prrr6IyiYmJ+Pbbb9GvXz+0a9cOPXr0wDfffIP09HRROWXrBIB9+/bBzc0Nbdu2xccff4zo6GjR8cDAwGKvZWFhgQULFpT5LJ88eQI7Ozv8888/ov3Pnz/Hhg0b8NFHH6Fdu3awtbXFkCFDsGPHDuTl5YnKZmdno02bNkhISBD23b9/H/Pnz0f37t3Rpk0bODk5Ydy4cTh27JhQZt68eZg3b16ZMRIREREREVWmavsZudWrVyM4OBi9e/fGggULYGBggMePH+P48eOYNWsW9PT00KVLlzeu5+TJk7hx4wYcHR2RkZFRYrk9e/bAxMQEHTt2RFRUVIXqOnbsGJKTk+Hq6oo9e/aUWC4uLg4+Pj4YPHgwvL29UVBQgEuXLiEnJ0eh7NatW1G3bl1hu1GjRqLjz58/x9ChQ9GiRQuoqakhKioK06dPR1FREfr37w8AOH36NBISEjBs2DBYWlriv//+w/r163H27FkcPnwYEomkXHX++uuvmD9/PiZMmABnZ2dERkbCz88PYWFhsLW1BQAMHTpUof3++usvrFy5El27di3lKb60efNmdOjQAR9++KGwLz09HWPGjMGDBw8wZswYODg4AADOnz+PLVu2QF1dHWPGjBHK//nnn9DV1YWdnR2Aly9Nxo8fjwYNGsDb2xstW7ZEdnY2YmNjMXPmTJiamsLS0hLe3t7w8PDA+PHjYWpqWmasRERERERElaFaJvAxMTEIDg6Gn58f/P39Rcfc3d0xZswYaGpWTuizZ8/GV199BQCIj48vNSZ1dXXEx8dXOIFfu3Yt1NVfDnooKYEvKCjA3Llz4enpiVmzZgn7u3XrVmx5a2trNGjQoMQ6Xx9R0KVLF9y6dQsHDx4UEngPDw+MHDkSampqQrnmzZtjxIgRiI6OhpubW7nqXL9+PTw8PPDFF18AAJydnfHPP/9g48aNCAkJAQA0btwYjRs3Fp23e/du6OnplZnAP3/+HPv378eKFStE+wMCApCcnIy9e/eKEvuOHTti5MiRSExMFJWPiYlB586doaGhgby8PHzxxRdo3Lgxdu/ejTp16gjlevTogREjRqBevXrCs7G3t0dYWBjmzp1baqxERERERESVpVoOod+xYwcMDAwwceLEYo/b2NigdevWwvbo0aPh6+uLiIgI9OnTB+3atcOECROQkZGBlJQUjBs3DnZ2dvDw8FBI0uUJdVmULfem1zh9+jRSUlLg6en5xvWVRF9fH/n5+cJ2/fr1Rck7AOH5pqamluvaycnJSEpKgru7u2h/v379EBcXB6lUWux5eXl5+P333+Hm5qbQ4/86+QuUVxP9lJQUREVFYfjw4aLkXU5fXx/29vbCdlFREWJjY9GjRw8AwNGjR/HgwQNMnz5dlLzLWVpawsjISNju27cvjhw5goKCglJjJSIiIiIiqizVLoEvKCjAuXPn4OzsXK5e9mvXriE0NBSzZ89GQEAAEhISMH/+fEyZMgWurq4IDAxEgwYN4O/vj+fPn7/FO3gzFy9ehL6+Pi5fvgw3Nze0bt0abm5uOHToULHlP/roI1hZWaFnz54IDg5GYWGhQhmZTIaCggJkZmbi0KFD+PPPPzFy5MhS4/j7778BAObm5uWqU97LbWZmJjrH3Nwc+fn5SE5OLra+6OhoZGdn46OPPio1LuDlS47WrVujVq1awr6EhATIZDKlp1VcunQJGRkZQvm//voLGhoa6Nixo1Ln29vb4+nTp6J1CYiIiIiIiN6majeE/tmzZ5BKpWjSpIlov0wmEyWK6urqoh7t7OxsBAUFCUO7b968ie3bt2PhwoUYMWIEAMDQ0BD9+/dHXFwcevXqVQV3U35paWnIycnBnDlzMGXKFJibmyMiIgJffvklPvjgAyHhNDAwgL+/P9q1awc1NTWcOHECa9euxaNHjxQWgYuLi4OXlxcAQFNTE/Pnz0ffvn1LjCEvLw/Lly9H69at4eLiIuxXpk75OgLy4eZy8u2S1hmIiIhAo0aN4OjoWOYzunz5Mjp16iTa9+jRIwBQ+N2UJDo6Gvb29kJcjx49QoMGDVC7dm2lzm/ZsiU0NDRw6dIltG3bVqlz3lU5OTmQyWSqDqNC5OtKFLe+BFUNtkH1wHZQPbaB6rENqge2g+qxDaqeTCZTGBFdkmqXwMu9fgNRUVGYOnWqsD1y5EhRomppaSmaly1fXOzVHlX5vocPH76FiCuHTCZDXl4eZs6ciVGjRgEAXFxckJiYiKCgICGB79Kli6i3uXPnzqhVqxZ27tyJCRMmwNDQUDhmY2OD8PBwZGdn448//sCiRYugoaGBoUOHFhvDN998g/v372P37t2idihPneWRmZmJ2NhYjBo1SqlpBmlpaSXOwVf2hx8TE4MBAwaUK85XaWpqom7duuWeYvAuunPnTo3/Cz4pKUnVIbz32AbVA9tB9dgGqsc2qB7YDqrHNqhaZU0jlqt2Cby+vj4kEolCku3i4oLw8HAAKHZu/Os9vlpaWgAgWi1d/lBe/5xYdSK/D2dnZ9F+FxcXhIWFlXquu7s7tm/fjuvXr4uS6Tp16gi9xC4uLigsLMSyZcswaNAgaGhohAQsbQAAhV5JREFUiK6xZs0aHDlyBEFBQcXOJS+rTj09PQBAVlYWDAwMhHKZmZkAIBx/VVRUFKRSqbCoXlmkUqnCD1y+Ev6DBw8Uhu+/7sGDB7hx4wbWrFkjOj8uLg55eXmiofmlkUgk1fq3VFXMzMxqdA98UlISTE1Noa2trepw3ktsg+qB7aB6bAPVYxtUD2wH1WMbVL1bt24pXbbaJfCampqwt7dHXFwcCgsLhQRTT09PSEKVfTtRE7Vq1arEY5WVLFpbW2Pnzp1IT08XJdm7du1CcHAwli1bVuFP9LVo0QLAy7nw8v8t39bS0kKzZs0UzomIiECLFi1ECxOWRk9PT3ghIOfo6Ag1NTWcPHmyzHns0dHRaN68uSg+JycnhIeHIy4uDq6urkrFkZWVBX19faXKvsvehb/YtbW1oaOjo+ow3mtsg+qB7aB6bAPVYxtUD2wH1WMbVB1lRxED1XAROwDw8vJCamoqgoKCVB1KlevcuTO0tLRw+vRp0f7Tp0/D2tq61HMjIyOhoaFRZiL8999/o06dOqhfv76wLyIiAosXL8b06dMxcOBApeN9vc5mzZrB1NQUx44dUyjn4uKi8PIlNTUVZ8+eVWrxOjkzMzPcv39ftM/IyAhubm7YvXt3sW+wMjMzcf78eQAvh893795ddLxv375o0qQJVq9ejezsbIXzb968iQcPHgjb6enpyMnJKbO3n4iIiIiIqLJUux54AHB1dYWPjw/Wr1+PGzduwN3dHYaGhsjKykJCQgLS0tKgq6tbKXWlpKTg8uXLAF4OF7l3756QfL660NvZs2eRnp4uJIdnzpxBSkoKmjZtqvQiZrdu3RIll//88w+OHTsGbW1t4TvvDRs2xOjRo7Fu3TqoqanB3Nwcv/76Ky5cuICtW7cK544bNw4dOnSAhYUFAOB///sf9u7dC09PT6FX/caNG1i5ciX69u2Lpk2b4sWLF4iJicG+ffswffp0YZX/s2fP4quvvoKzszOcnJxw4cIFoZ5Xv9euTJ0A4O/vj5kzZ8LExAQdOnRAZGQkLl26hB9//FHhmURGRqKoqEjp4fPAyxXgjx49qrD/m2++gaenJ0aMGIExY8bAwcEBwMuV/X/88Ud4e3vD0tISZ86cERb1k6tVqxbWrl2L8ePHY/DgwRg7dixatmyJ7OxsnDp1Cnv37sW+ffuERfLkvxl5HURERERERG9btUzgAWDGjBlwcHBAWFgYAgICkJ2dDT09PVhbW2PJkiXw8PColHri4+Px9ddfC9snT57EyZMnAbzsdZULDAzE2bNnhe2VK1cCAD755BMsW7ZMqbqOHj2KDRs2CNuHDh3CoUOH0LRpU5w4cULYP2PGDOjo6GDbtm1IT0+Hubk5Nm7ciM6dOwtlzMzMsH//fjx8+BBFRUUwNTXFnDlzMHr0aKFMw4YNUa9ePWzatAlpaWmoW7cuWrRogQ0bNohW4Y+Pj0d+fj7i4uIQFxcnitnPzw/+/v5K1wm8/MxcTk4OQkJCsGXLFpiZmWHDhg2ws7NTeCZHjhyBjY0NTExMlHqGAODm5obg4GBhbo5cgwYNsHv3bvzwww84evQotmzZAnV1dbRs2RLjx4/H8OHDcerUKUgkErRv317hura2tjh48CC2bNmC4OBgPH78GDo6Omjbti1Wr14NS0tLoezJkyfRvn17NGzYUOm4iYiIiIiI3oSarKauPkXvtUGDBqFHjx7w8/Mr13nz589HZmYm1q1bV+G6CwoK4OrqipkzZ5ZrusGr5D34bduOAXC+wrGolh2Ac6oO4o28ePEC169fh5WVFed4qQjboHpgO6ge20D12AbVA9tB9dgGVe//coOyR3ZXyznwRGWZNGkSdu/eDalUWq7zvvvuuzdK3oGX6wXo6uqWa94+ERERERHRm6q2Q+hrmoKCghKPqampKXyujd5Mr169cPfuXTx48ADNmzev0rrV1NSwePFiYQ0BIiIiIiKiqsAMpBLcv38fPXv2LPG4k5MTdu3aVYURvR/GjRunknoHDBigknqJiIiIiOj9xgS+EhgaGiI8PLzE45W1Yj4RERERERG9v5jAVwKJRKL0p+SIiIiIiIiIKoKL2BERERERERHVAOyBJ1IpK1UH8AZqcuxERERERDUPE3gilQpTdQBvqBAAv7BARERERFQVOISeSEWkUilycnJUHcYbYvJORERERFRVmMATqZBMJlN1CEREREREVEMwgSciIiIiIiKqAZjAExEREREREdUATOCJiIiIiIiIagAm8EQqpKampuoQiIiIiIiohmACT6QiEokE2traqg4DLz8FR0RERERE1R2/A0+kUiMBXFdh/Vao+d+iJyIiIiJ6PzCBJ1Kp6wDOqzoIIiIiIiKqATiEnoiIiIiIiKgGYAJPREREREREVAMwgSciIiIiIiKqAZjAExEREREREdUATOCJiIiIiIiIaoBqncDHxsbC29sbzs7OsLa2RseOHeHj44OIiAgUFRVVSh1//vknZsyYgV69esHCwgLffvttseU2btwILy8vtG/fHhYWFrh8+XK564qMjIS/vz+6du0KCwsLbNu2rcxzJk2aVGLZc+fOYdiwYbCxsUH37t2xZcsWyGQyUZmZM2eiT58+sLW1haOjI0aOHIlTp06JygQGBsLCwqLYPwsWLBCV3bdvH/r37w9bW1t069YN8+bNw5MnT0RlwsLC4OvrC2dnZ1hYWODYsWPF3tujR4/g7+8POzs7ODk5Ye7cucjOzi7zmQDAzZs3YWdnh/T0dNH+9PR0LFu2DG5ubmjbti3s7e0xatQo7Nu3D4WF4u+d37lzBxYWFvjvv/9E150xYwY6d+6MNm3aoGPHjvDz80NcXJxQxsvLC5s3b1YqTiIiIiIiospSbT8jt3r1agQHB6N3795YsGABDAwM8PjxYxw/fhyzZs2Cnp4eunTp8sb1nDx5Ejdu3ICjoyMyMjJKLLdnzx6YmJigY8eOiIqKqlBdx44dQ3JyMlxdXbFnz54yy8fGxuLixYvFHrt79y7GjRuHTp064YsvvsDNmzexcuVKaGhoYNy4cUK5/Px8jB07FqampsjLy0N4eDh8fHwQGhqK9u3bAwCGDh2q8Cz/+usvrFy5El27dhX2HTp0CPPmzcO4cePQpUsX/Pfff1izZg1u3bqF3bt3C+UOHz4MAOjWrRsOHTpUbPz5+fkYP348AGDVqlXIzc3F8uXLMWPGDAQHB5f5bNauXYtBgwahQYMGomfi6emJwsJCeHl5wdraGlKpFGfOnMHSpUtRv3599OrVSygfHR0NCwsLGBkZAQCOHz+OadOmoVWrVpg2bRpMTEyQnp6O3377DZ9//jnOnj2LunXrwtfXF/7+/vjss8+gp6dXZqxERERERESVoVom8DExMQgODoafn9//a+++w6K43reB30tTEAGNgIoiaAJSBRQBRewi9horlthjjRo1xBpN1MRevmJXDNEoxgKipogdCzYsaBSwgIoFpQiylHn/8N35OS5lUWTZeH+uiyuZM2fmnJnHVZ6dc85g7Nixkn1+fn4YOHAgdHRKputTpkzBtGnTAABnz54ttE9aWlo4e/bseyfwy5Ytg5bWm0EPRSXwcrkcP/74IyZOnIiAgACl/Rs3bkSlSpWwZMkS6OnpwcvLC8nJyQgMDIS/vz/09PQAAMuXL5cc5+Pjg5YtW2Lfvn1iAl+1alVUrVpVUm/Hjh0wNjaWJPChoaFo2LAhpkyZIqkbEBCAR48eoVq1auKxWlpaSEhIKDCBP3z4MG7fvo3w8HDUrl0bAGBkZIQhQ4YgOjoazs7OBd6bBw8eICIiAn/88YekfPLkycjNzcXu3bthbm4uueb+/fsjLS1NUj8iIgLNmzcHADx9+hRTp05F/fr1sW7dOvH+AYCvry969uwp/pnz9PSEkZER9uzZg0GDBhXYTyIiIiIiopJUJofQb968Gaamphg1alS++52dnWFvby9u+/v7Y8SIEQgLC0ObNm1Qr149jBw5EikpKUhMTMSQIUPg6uqK9u3bKyXpioS6KKrWK6lzbNy4EUZGRujWrVu++48fP46WLVtKEs127dohNTUVly5dKvC82traqFixIrKzswusk5WVhb/++gu+vr6S8+fk5MDQ0FBSt2LFigAgGbqvynUeP34ctra2YvIOAI0bN4aJiQmOHTtW6LF79+5FzZo1JX8GoqKiEB0djREjRkiSd4Xq1avD1tZW3E5NTcXFixfFBH7nzp1IT0/Hd999J7lmBU9PT+jr64vbbdu2LfDLCSIiIiIioo+hzD2Bz8nJwcWLF+Hr61usp+w3btzAixcvMGXKFKSnp2PevHmYMWMGEhMT0aVLFwwePBhr167F2LFjERERgQoVKnzEq/gwDx8+xLp167B582bIZDKl/RkZGXj06JEk+QWA2rVrQyaTIS4uDh4eHmK5IAjIzc1FWloa/vjjD9y7d6/Auf7AmyfT6enp6NChg6S8R48e+O6773Do0CF4e3vj0aNHCAwMRPPmzcVh6KqKi4tT6r9MJoO1tTXi4uIKPfb06dNwdXWVlJ07dw4AVJ5WceLECRgbG4tP+s+fPw8zMzNJkl8YV1dXbNiwAcnJyZJh/JoqMzNTaf2ET0FmZqbkv1T6GIOygXFQP8ZA/RiDsoFxUD/GoPQJgpBv3pefMpfAv3z5EnK5XByOraBIQhW0tLQkT3rT09MRGBgoJlO3bt3Cpk2bMHv2bPTp0wcAYGZmho4dOyIyMlIyF7qsmT9/Plq3bg0XF5d89yuGghsZGUnK9fT0oK+vrzSXPyQkBNOnTwcAGBgYYOnSpUoJ8NvCwsJgbm4Od3d3SXnHjh2RmZmJyZMni0/wGzVqhKVLlxbr+oA3T8AVT+/fZmxsXOhaBIIg4Nq1a0rxS0pKAgCVv0iIiIiAj4+P+GcoKSmpWF9C1K1bFwAQHR2NZs2aqXxcWRUfH/9J/yV99+5ddXfhk8cYlA2Mg/oxBurHGJQNjIP6MQalK79RwPkpcwm8wrvfQBw+fBjjx48Xt/v16ydZIb1u3bqSJ6FWVlYA3iSY75Y9fvz4I/S4ZJw8eRInT54scOX299GyZUvUrVsXL168wKFDhzBhwgSsWrUKTZs2VaqbmpqKY8eOoX///kpD4f/8808sWLAAo0aNgru7Ox4+fIgVK1ZgwoQJCAwMVPlbow+RkpICuVz+QU+9c3NzceLECaVRCMXpf6VKlQC8mTv/X2Btbf3JPoG/e/curKysJFMkqPQwBmUD46B+jIH6MQZlA+OgfoxB6btz547KdctcAm9iYgI9PT2lJNvLywshISEAkO/c+HefRuvq6gKA5Cmv4luNrKysEu1zSZo3bx4GDBgAfX19pKamiuVZWVlITU2FkZGReE3vLsoml8uRmZmptDJ65cqVxYTXx8cHKSkp+OWXX/JN4A8fPgy5XI6OHTtKygVBwKxZs/Dll19i9OjRYnnNmjXRt29fnDp1Ct7e3ipfp5GRUb6vjEtJSVEaffE2Reze/YZKMe/90aNHqFWrVqFtX7p0Ca9evULjxo0lxxc1dP9tivZfv36t8jFl2af+l7O+vj4MDAzU3Y1PGmNQNjAO6scYqB9jUDYwDurHGJSe4jxILHOL2Ono6MDNzQ2RkZGSIfPGxsZwcnKCk5OTysMLNFF8fDwCAwPh7u4u/gBvVpN3d3dHVlYWDAwMUK1aNaWEMz4+HoIgKM0tf5eDgwPu3buX776wsDDUrl1bskAc8Ob96snJyeLQcQVFvfv37xfrOmvXrq3Uf0EQEB8fX2j/TUxMAEDy5QYANGzYEACU3nGfn4iICLi7u0sW5GvYsCGSkpJw+/Ztlfqv+PJE0R8iIiIiIqKPrcwl8AAwePBgPHnyBIGBgeruSqkLCgpS+gGA3r17IygoSBxZ4OPjg3/++Ueymnx4eDiMjIwKnd8OABcuXEDNmjWVyp88eYJz584pLV4HvHmKr6+vjxs3bkjKr1+/DgCwsLAo1nX6+Pjg5s2bkrk1kZGRePnyZb4jAxTKlSuH6tWrIyEhQVLeoEEDODs7IzAwEE+ePFE67tGjR7h16xaAN68EVKw+r9CzZ08YGhpi/vz5+a7Qf/bsWckccUX71tbWRV8sERERERFRCShzQ+gBoFmzZhg+fDhWrFiBmzdvws/PD2ZmZkhLS0NUVBSePn1aYqvIJyYm4urVqwDezPe4f/++OP+8bdu2Yr1z584hOTlZnJ9w5swZJCYmwsLCAk5OTiq1defOHcn8hn///ReHDh2Cvr6+mLS+vXr82ywtLSX7hgwZgtDQUEyaNAl9+vTBv//+i40bN+Kbb74RRygcPXoUe/fuRbNmzVCtWjWkpKQgLCwMJ0+exJIlS5TaCA8PR15entLweeDNsI4vv/wSv/32GwwNDcU58KtWrcIXX3wBLy8vse7Vq1eRmJiI5ORkAMCVK1cAvPkSQPGk3NfXV3wrwMSJE5GZmYmff/4ZzZo1K/Qd8ADg5uYmfnHwtkWLFsHf3x/du3fH4MGD4eDgALlcjvPnzyM4OBgLFy6EgYEB7ty5o5TAm5qaYuHChZgwYQL69OmDfv36oWbNmnjx4gX+/vtvhIaGSl5BeO3aNRgYGMDOzq7QvhIREREREZWUMpnAA8CkSZNQv359BAcHY86cOUhPT4exsTEcHBzw008/oX379iXSztmzZ/Hdd9+J2ydOnMCJEycAQHxiCwArV64UX1UGvEkWAaBr165YsGCBSm0dPHgQq1atErf37t2LvXv3wsLCAkeOHClWv2vVqoWNGzdiwYIFGD58OCpXroxx48bhq6++EuvUrFkTcrkcixcvxosXL1CpUiXY2tpi27ZtYiL9ttDQUDg7O8PS0jLfNidPnozKlStj37592LhxIypVqgQPDw/JlwYAEBwcjD179ojbmzZtAvBmmPq2bdsAvFmjYMOGDZg3bx4mTpwIHR0dtG7dGgEBAUVeu6+vLyZPnoz09HTJMPhatWphz549WL9+PbZv345Hjx5BT08P9vb2CAgIQPPmzREcHIw6derkOwKhVatWCAkJwfr168V7ZmRkhPr162PTpk2S9RSOHz+O1q1bQ1tbu8j+EhERERERlQSZ8CkuPU0aLTs7G82bN8fkyZPRpUuXYh371VdfoW7dupgyZcp7t5+SkoLGjRtj8+bNSq/aU5Vi1IeT00AAl967Lx/OFcBFNbavXhkZGYiJiYGdnR0XaVETxqBsYBzUjzFQP8agbGAc1I8xKH3/lxsUPbK7zD6BJyqIrq4uhg0bhqCgoGIn8IrRAB9i27ZtcHNze+/knYiIiIiI6H0wgS8hOTk5Be6TyWQcal3C+vTpg/T0dCQnJ3/QO+Hfh4mJCaZPn16qbRIRERERETGBLwEJCQlo2bJlgfvfnvtNJUNPT0/yPvrS1L9/f7W0S0REREREnzYm8CXAzMwMISEhBe4vqRXziYiIiIiI6NPFBL4E6OnpqfwqOSIiIiIiIqL3wQSeSK3U/R55dbdPRERERESqYgJPpFbB6u4AgFwAXGSRiIiIiKis01J3B4g+VXK5HJmZmeruBpi8ExERERFpBibwRGokCIK6u0BERERERBqCCTwRERERERGRBmACT0RERERERKQBmMATERERERERaQAm8ERqJJPJ1N2FT5pMJoO+vj7jQEREREQaga+RI1ITPT096Ovrq7sbnzR9fX3Y29uruxv/ebm5udDW5tsOiIiIiD4UE3giNerXrx9iYmLU3Q2ij8bOzg7BwcHq7gYRERHRfwITeCI1iomJwaVLl9TdDSIiIiIi0gCcA09ERERERESkAZjAExEREREREWkAJvBEREREREREGoAJPBEREREREZEGYAJPREREREREpAGYwBMRERERERFpgP9EAn/s2DEMGzYMnp6ecHBwQKNGjTB8+HCEhYUhLy+vxNo5cuQIOnXqBCcnJ/j6+mL37t351ouNjcXo0aPh7u4OFxcXdOnSBadOnVK5HUEQsH79erRo0QKOjo7o0KEDwsPDleqlpaVhxowZ8PDwQL169eDv76/0TvEHDx5gxIgR8PHxgZOTE7y9vTFu3DjEx8dL6t27dw8zZ85E586dYW9vjw4dOuTbt4ULF6J9+/ZwdXWFm5sbunfvjgMHDijVW716NQYPHowGDRrA1tYWV69eVaqjapsFOXr0KHx8fCCXy5X2bdmyBba2tggICCj0HEuWLMHAgQMlZdnZ2fDw8EDdunXx8OFDpWP2798PPz8/5ObmFqu/REREREREH0LjE/glS5Zg+PDhKFeuHGbOnIktW7ZgxowZqFixIr799ttiJc6FiYqKwpgxY+Di4oL169fDz88P33//PQ4dOiSpd/v2bfTq1Qu6urr45ZdfsHr1arRr1w6ZmZkqt7VhwwYsW7YM3bp1Q2BgIBo2bIiJEyfiyJEjknoTJ07E33//jW+//RbLly+HtrY2Bg4ciEePHol1Xr16hSpVqmDixInYsGEDpk2bhvj4eAwYMADJycmSfh87dgy1atVCnTp1Cuzbq1ev0LNnTyxfvhzLly+HnZ0dJk6ciNDQUEm933//HdnZ2WjUqFGB51K1zfwIgoClS5di0KBB0NPTU9q/f/9+AMBff/2Vb4KvEBERgebNm0vKTp48iZcvX0IQBISFhSkd0759e8jlcuzdu7dYfSYiIiIiIvogggaLiIgQbGxshBUrVuS7/8qVK8L169dLpK2vvvpK6NWrl6Rs4sSJgp+fn6SsT58+wvjx49+7naysLMHV1VWYP3++pHzEiBFCx44dxe1Lly4JNjY2wj///COWZWRkCF5eXsLcuXMLbSM+Pl6wsbER9u/fL5bl5uaK/z916lShffv2Kve5V69ewuDBgyVlivOdOXNGsLGxEaKjo5WO+5A2IyMjBTs7O+H58+dK++Li4gQbGxth0KBBgo2NjXD48OF8z5GQkCDY2NgI8fHxkvKJEycKDRo0ELp27Sp06NAh32NXrlwpdO7cWeX+vis6OlqIjo4WXF1dBQD84c9/9sfV1bXQz8KrV6+EqKgo4dWrV+/9eaIPxzioH2OgfoxB2cA4qB9jUPoUuYEqNPoJ/ObNm2FqaopRo0blu9/Z2Rn29vbitr+/P0aMGIGwsDC0adMG9erVw8iRI5GSkoLExEQMGTIErq6uaN++Pc6ePSseJ5fLcfbsWbRt21Zy/nbt2iE2NhYJCQkA3gydv3DhAvz9/d/7mh48eIBXr16hcePGknJvb2/cunVLHNJ948YNyGQyST19fX00aNAAERERhbZhYmIC4M1QcQUtrff/o2BiYiI5l6rn+5A29+7dC3d3d1SuXFlpX1hYGGQyGX744QdUqVJFaXSAwtGjR2FtbQ0rKyuxLCMjA0eOHIGvry+6deuGf//9F7du3VI61s/PDzExMbh58+Z7XwMREREREVFx6Ki7A+8rJycHFy9ehK+vL3R0VL+MGzdu4MWLF5gyZQrS09Mxb948zJgxA4mJiejSpQsGDx6MtWvXYuzYsYiIiECFChVw//59ZGdno3bt2pJzKYZ9x8XFoUaNGrhy5QqAN0lg165dcevWLZiZmcHf3x9DhgxRqX9ZWVkAoDQsXLEdGxuL6tWrQy6XQ0tLC9ra2pJ6urq6SExMxOvXr1G+fHmxPC8vD7m5uUhKSsLSpUtRrVo1tG7dWuX79jZBEJCbmysmu6dOncIvv/zyXud6X6dPn0b37t3z3RcWFoYGDRqgZs2a8PPzw++//460tDRUrFhRUu/IkSNKw+f//vtvZGRkoEOHDvjiiy8wf/58hIWFwdbWVlKvTp06MDY2xqlTp1C3bt2SvTii/6DMzEwIgpBv+dv/JfVgHNSPMVA/xqBsYBzUjzEofYIgQCaTqVRXYxP4ly9fQi6Xo1q1apJyRXKpoKWlJXnSm56ejsDAQPHJ7a1bt7Bp0ybMnj0bffr0AQCYmZmhY8eOiIyMRKtWrZCSkgIAMDIykrSl2Fbsf/bsGQBg8uTJGDRoEKZOnYqTJ0/il19+QYUKFdC7d+8ir8vS0hIymQzR0dHw8PAQyy9fvixpq1atWsjNzcWNGzfg7OwM4E2Sfu3aNQiCgNTUVEkCP2XKFPFJtKWlJTZv3qyU0KoqMjISgwcPBgDo6OhgxowZSqMTPqYnT54gKSlJKakGgOjoaNy9e1fsX4cOHbBt2zYcPnwYPXr0EOtlZGTg3LlzGD58uOT4sLAwmJubo2HDhtDS0oKnpyfCwsIwceJEpQ+Vra2t+KUNERUuPj6+0F8E7t69W3qdoQIxDurHGKgfY1A2MA7qxxiUrvzW9cqPxibwCu8mVYcPH8b48ePF7X79+mHmzJnidt26dSXDrhXDp99ebE1R9vjx42L1RbHifZcuXcRh/Z6ennj8+DECAwNVSuANDQ3RqVMnbNiwATY2NnBxcUFERIS40rviehs3bgxLS0vMmjULCxcuxGeffYZ169bhwYMHknoK48ePx4ABA/Do0SNs3boVgwcPxm+//Ybq1asX6xqBN1MTQkJCkJ6ejuPHj2PevHnQ1tZGz549i32u9/H06VMAKHD4vK6urviFgouLC2rWrInQ0FBJAn/69GmUL18e9evXF8uSk5Nx6tQp+Pv7i1/6dOzYEVOnTsWFCxfQoEEDSVuVKlUS+0JEhbO2ti7wCfzdu3dhZWUFfX19NfSMAMahLGAM1I8xKBsYB/VjDErfnTt3VK6rsQm8iYkJ9PT0lJJsLy8vhISEAEC+c+PffYquq6sLAJKn0YpvPxTD2Y2NjQG8eW3b21JTUyX7Fef29PRU6lNoaCjS09NhaGhY5LV99913ePbsmfh0uFKlShg/fjwWLlwIU1NTsY9Lly7FpEmT0LFjRwCAjY0NBg4ciG3btonz3BVq1qyJmjVrwtnZGT4+PmjTpg02bNgg+XJDVYaGhnBychKvLTc3FwsWLEC3bt2UhvR/DAVNM8jLy0N4eLj49FwRn5YtWyIoKAhJSUkwNzcH8Gb4fJMmTSTTLw4ePIicnBw0bdpUPNbDwwN6enoIDQ1VSuB1dXXFvhBR4Yr6BUBfXx8GBgal1BsqCOOgfoyB+jEGZQPjoH6MQelRdfg8oMEJvI6ODtzc3BAZGYnc3FwxcTQ2NhaTS1WHIRTF0tISurq6iIuLQ5MmTcTyuLg4ABDnxn/xxReFnqew15m9rVKlSti0aROSkpKQkpICKysr/PPPP9DV1ZUsyufo6IhDhw7h3r17EAQBVlZW+OGHH+Dg4CB+MZEffX191KlTB/fu3VOpP0VxcHDA1q1bkZycLH7B8DEpvjBRJNkKZ86cwdOnT/H06VO4u7srHRceHo7BgwdDEAQcP34cU6ZMkexXvDJu0KBBSsceOnQI06dPl9zXtLQ0pS9KiIiIiIiIPhaNXoV+8ODBePLkCQIDAz9qO3p6evDw8MDhw4cl5eHh4ahTpw5q1KgB4M1wbRMTE5w+fVpS7/Tp06hevXq+Q74LY25uDhsbG2hra2P79u1o166d0hN8mUwGKysrWFtb48WLFwgPDy9yKHt6ejpu3bqFmjVrFqs/Bblw4QIMDQ1RqVKlEjlfUWrUqAFdXV1x9X+F0NBQGBgYYMuWLQgKCpL81K1bV1wD4OrVq0hOToaPj494bGJiIi5duoTevXsrHfvdd9/h5cuXOHHihKS9xMREWFtbf/wLJiIiIiIiggY/gQeAZs2aYfjw4VixYgVu3rwJPz8/mJmZIS0tDVFRUXj69CkqVKhQIm2NGjUKAwYMwOzZs+Hn54ezZ88iLCwMS5cuFevo6upi7NixmD9/PoyNjeHm5oYTJ07gwIEDmDt3rspt7d+/H1lZWbC0tMSTJ0/w+++/IyEhAYsWLZLUW7NmDWrVqoXPPvsM8fHxWLt2LRwdHdGtWzexzsqVK5GWlgY3NzdUrlwZiYmJ2LZtG+RyOQYOHCjWy8zMxLFjxwC8SUzT09Nx6NAhAEDDhg1RuXJl3Lx5E4sWLULbtm1hYWGBjIwMHD16FLt27cLEiRMlw9HPnTuH5ORkcT7HmTNnkJiYCAsLC3GEhCpt5qdcuXJwdHTE9evXxbKsrCz89ddfaNOmDby8vJSO6d69O3788UfExcUhIiICrq6ukqfniqfvQ4cOVfpio379+li3bh3CwsLQokULAG8WwYuLi8Po0aPz7SMREREREVFJ0+gEHgAmTZqE+vXrIzg4GHPmzEF6ejqMjY3h4OCAn376Ce3bty+Rdho0aICVK1di2bJlCAkJQfXq1TFv3jz4+flJ6vXv3x+CIGDr1q0IDAyEhYUF5s6dW6wF3gRBwKZNm5CQkAADAwM0bdoUixYtgpmZmaReamoqFi5ciOfPn8PMzAydOnXC119/LVl1397eHlu2bMG+ffuQkZEBc3NzuLu7Y/ny5ZJE9fnz55LF/wCI20FBQfDw8ECVKlVgZGSE//3vf3j69CkqVqyI2rVrY9WqVWjVqpXk2JUrV+LcuXPituLLh65du2LBggUqt1kQX19fbNmyRXzlwtGjR5GWloYuXbrkW79Dhw74+eefERoaiqNHj6Jdu3aS/WFhYXBzc8t3VIKOjg46dOiAnTt34tWrV6hQoQJOnjyJ8uXLS57iExERERERfUwyIb9lgYnKuOTkZDRt2hSbNm3Kd757QZKSkuDj44MDBw7g888/f+/2x40bhwoVKmD+/PnvdfzVq1cBAAMHDsSlS5feux9EZZ2rqysuXrxY4P6MjAzExMTAzs6OC+WoEeOgfoyB+jEGZQPjoH6MQelT5AaKkcqF0fgn8PRpqly5Mvr06YOtW7cWK4E3NzfHrVu3PqjtBw8e4NixY+KceiIiIiIiotKg0YvYaaKcnJwCf3Jzc9XdPY0ycuRI1K1bV+XV/UtKUlISfvjhB1haWpZqu0RERERE9GnjE/hS5uDgUOA+CwsLHDlypBR7o9kqV66MMWPGlHq7DRo0UHonPBERERER0cfGBL6UhYSEFLivpN5bT0RERERERP89TOBLmSoLExARERERERG9iwk8kRrZ2dmpuwtEHxX/jBMRERGVHCbwRGoUHBys7i4QfXS5ubnQ1tZWdzeIiIiINB5XoSdSE7lcjszMTHV345OWmZmJGzduMA4fGZN3IiIiopLBBJ5IjQRBUHcXPmmCICAzM5NxICIiIiKNwASeiIiIiIiISAMwgSciIiIiIiLSAEzgiYiIiIiIiDQAE3giNZLJZOruwidNJpNBX1+fcSAiIiIijcDXyBGpiZ6eHvT19dXdjU+avr4+7O3t1d0NlfF1bERERESfNibwRGrUr18/xMTEqLsbpAHs7OwQHBys7m4QERERkRoxgSdSo5iYGFy6dEnd3SAiIiIiIg3AOfBEREREREREGoAJPBEREREREZEGYAJPREREREREpAGYwBMRERERERFpACbwRERERERERBqgTCfwx44dw7Bhw+Dp6QkHBwc0atQIw4cPR1hYGPLy8kqsnSNHjqBTp05wcnKCr68vdu/eLdmflpaGsWPHokWLFnB2doanpyeGDh2K6OjoYrWzYcMGdOnSBQ0aNICLiws6duyIX3/9FYIgKLU3Y8YMeHh4oF69evD391d61ZhcLsfPP/+Mfv36wcXFBba2tkhOTs633V27dqFjx45wcXFB06ZNMX36dDx//rzAfm7ZsgW2trYYMWKEpHzatGmwtbXN92fdunWSukXd03f9+OOPsLW1xQ8//FBoPYWsrCw0bdoUR48elZTL5XJs2bIF3bp1g6urK5ydndGxY0esXLkSqampkrqCIKBJkybYu3evWJacnIwFCxbA19cXTk5OcHNzQ//+/bFr1y7k5uYCANasWYPBgwer1E8iIiIiIqKSUmZfI7dkyRKsXbsWrVu3xsyZM2Fqaopnz57h77//xrfffgtjY2M0adLkg9uJiorCmDFj0KNHDwQEBODMmTP4/vvvUaFCBbRt2xbAm6RQT08Po0aNQo0aNZCeno6tW7di4MCB+OOPP2Btba1SW2lpaWjXrh2++OILlCtXDpGRkZg3bx7S09MxcuRIsd7EiRNx7do1fPvtt6hSpQq2bNmCgQMHYt++fahWrRoA4PXr19i1axecnJxQv359nDx5Mt829+7di+nTp2PIkCFo0qQJHj58iKVLl+LOnTvYsWOHUv2nT59i9erV+Oyzz5T2ff311+jdu7ekLDw8HFu3boWPj0+x7unbbt26hd27d8PQ0FCl+wgA27dvh5GREZo1ayaWZWVlYejQobh8+TL69euHCRMmQE9PDzExMdi2bRvS0tIQEBAg1r9+/TqePXuGpk2bAgDu3buHAQMGIDc3F4MHD4aDgwPkcjnOnDmD+fPno1KlSmjVqhX69euHDRs24MyZM/D09FS5z0RERERERB+iTCbwR48exdq1azFmzBiMHTtWss/Pzw8DBw6Ejk7JdH3NmjVwdnYWn/x6enriwYMHWLFihZhsfvbZZ1i8eLHkuEaNGsHDwwOHDx+WJN+F+eabb5TO8fDhQ+zZs0c8x+XLl3H8+HGsWbMGLVq0AAB4eHigZcuW2LhxI6ZPnw4AMDIywrlz5yCTyfDHH38UmMCHhoaiYcOGmDJliqQ8ICAAjx49Er8QUPjll1/QokULPHz4UOlclpaWsLS0lJQtXrwYn3/+OerWrSuWqXJP3zZ37lwMGjRI8iS8MIIgICgoCAMGDJCUL1++HFFRUdi4cSMaNWoklnt6eqJv3764ePGipP7Ro0dRr149VKpUCQAwefJk5ObmYvfu3TA3Nxfr+fj4oH///khLSwPw5t63adMGQUFBTOCJiIiIiKjUlMkh9Js3b4apqSlGjRqV735nZ2fY29uL2/7+/hgxYgTCwsLQpk0b1KtXDyNHjkRKSgoSExMxZMgQuLq6on379jh79qx4nFwux9mzZ5WSynbt2iE2NhYJCQkF9tHAwADlypVDdnb2B11rpUqVJOe4ceMGZDIZGjduLJbp6+ujQYMGiIiIkBwrk8mKPH9OTo7Sk+2KFSsCgNLQ/aioKPz999+YNGmSSn1PSkpCVFQUOnbsKJYV957u378fCQkJGDZsmEptAsC5c+eQmJgIX19fsez169fYvn07WrVqJUneFcqVKwcvLy9JWUREhPglSVRUFKKjozFixAhJ8q5QvXp12Nraittt27bFsWPHCpy2QEREREREVNLKXAKfk5ODixcvwtPTs1hP2W/cuIGgoCBMmTIFc+bMQVRUFGbMmIFx48ahWbNmWLlyJSpXroyxY8fi1atXAID79+8jOzsbtWvXlpyrTp06AIC4uDhJeV5eHnJycvDkyRMsWLAAWlpa6NKly3tdY3p6Oo4ePYq9e/dKniTL5XJoaWlBW1tbcoyuri4SExPx+vXrYrXVo0cPnDhxAocOHUJ6ejpu376NwMBANG/eHNWrVxfr5ebmYu7cuRg5ciTMzMxUOrdiLYL27duLZcW5p+np6fj5558xZcoU6Ovrq3xNp0+fRrVq1SSjB65du4aMjAyVp1U8efIE169fF4fgnzt3DgBUPt7V1RW5ubnicURERERERB9bmRtC//LlS8jlcqWh3YIgiIuIAYCWlha0tP7v+4f09HQEBgaicuXKAN7Mq960aRNmz56NPn36AADMzMzQsWNHREZGolWrVkhJSQHwZkj02xTbiv0Ky5cvR2BgIIA3w+rXrVuHmjVrFuv67t27hzZt2ojbo0aNwqBBg8TtWrVqITc3Fzdu3ICzszOAN18cXLt2DYIgIDU1FeXLl1e5vY4dOyIzMxOTJ08Wn/Q3atQIS5culdT77bffkJmZKelLUcLCwuDq6iq5B8W5p6tWrUKtWrXQrl07ldsE3iTrbz8NB94k5ACU/twU5NixY6hevTpsbGwAvBlNAEDypUZhjIyMUL16dVy5ciXfaQFEH0tmZqbS6BlNlpmZKfkvqQfjoH6MgfoxBmUD46B+jEHpEwRBpdHVQBlM4BXevYDDhw9j/Pjx4na/fv0wc+ZMcbtu3bpi8g4AVlZWACAZTq0oe/z48Xv1qW/fvmjVqhWePn2KXbt2Yfjw4diyZQscHBxUPke1atUQEhKCjIwMREVFYf369dDS0sK4ceMAAI0bN4alpSVmzZqFhQsXil8UPHjwAIBqw+bf9ueff2LBggUYNWoU3N3d8fDhQ6xYsQITJkxAYGAgZDIZnj9/jhUrVmDhwoXQ09NT6byxsbG4ceMGZsyYUaz+KNy+fRvBwcHYuXNnsY998uQJHB0d892n6v2JiIhA8+bNi93220xMTPD06dMPOgdRccXHx/8n/0G9e/euurtAYBzKAsZA/RiDsoFxUD/GoHSpmoeVuQTexMQEenp6Skm2l5cXQkJCACDfufHvPvHV1dUF8H/zvYH/uylZWVkAAGNjYwAQFydTULxuTLFfwdzcXJwf3axZM/To0QMrVqzA2rVrVb4+PT09ODk5AXizOJ2hoSEWLlyIPn36wNTUFHp6eli6dCkmTZokzi23sbHBwIEDsW3bNpiYmKjcliAImDVrFr788kuMHj1aLK9Zsyb69u2LU6dOwdvbG8uXL4etrS0aNGggXntOTg5ycnKQmpoKAwMDpekMoaGh0NHRUXp6ruo9XbBgAdq2bQsLCwtxX15eHrKzs5GamgpDQ0PJCIu3yeVyMb4KimH/jx49KvK+yOVyREZGYsWKFWKZIq6PHj1CrVq1ijwH8CaWij9LRKXF2tr6P/cE/u7du7CysirWVBoqWYyD+jEG6scYlA2Mg/oxBqXvzp07Ktctcwm8jo4O3NzcEBkZidzcXHEuuLGxsZj4qvrtRFEsLS2hq6uLuLg4ydxnxTztd+dxv01LSwt2dna4cOHCB/XBwcEBubm5SExMhKmpKQDA0dERhw4dwr179yAIAqysrPDDDz/AwcFBKXEtTHJyMpKTkyUrxAMQFwC8f/8+gDdP9M6fPw93d3elc7i7u2P9+vWS18QBwIEDB+Dl5SUZ9QCofk/j4+Nx8uRJ7N+/X3L8zp07sXPnToSHh4vz5t9lbGys9AWBo6MjDAwMcOLECfTs2TP/G/L/RUZGAnjzBYpCw4YNAQAnT55UOYFPS0vDF198oVJdopLyX/2HVF9fHwYGBuruxiePcVA/xkD9GIOygXFQP8ag9BRnlHWZW8QOAAYPHownT56I880/Fj09PfFVcG9TJI81atQo8NicnBxER0cXew78uy5evAiZTKbUlkwmg5WVFaytrfHixQuEh4cXmZi+q3LlytDX18eNGzck5devXwcAWFhYAHjzSrmgoCDJT926deHi4oKgoCBxLr7ClStXcP/+fXTo0EGpTVXv6ZIlS5TarFKlClq1aoWgoKBC56JbW1srrWZfvnx59OnTB3/99RfOnDmjdExWVpaYuB89ehSNGzeWfBHUoEEDODs7IzAwUJxP/7ZHjx7h1q1b4nZeXh4ePnwIa2vrAvtJRERERERUksrcE3jgzfD04cOHY8WKFbh58yb8/PxgZmaGtLQ0REVF4enTp6hQoUKJtDVq1CgMGDAAs2fPhp+fH86ePYuwsDDJIm+///47oqOj0ahRI5iamuLZs2fYsWMH4uPjMWvWLJXaSUtLw7Bhw9CpUyfUqlULOTk5OHv2LIKCgtCrVy9UqVJFrLtmzRrUqlULn332GeLj47F27Vo4OjqiW7duknMeO3YMmZmZuHbtGoA387orVKiAzz//HJ9//jlkMhm+/PJL/PbbbzA0NBTnwK9atQpffPGF+Fo1Ozs7pf4aGRnBwMBA8pRaITQ0FOXLl0fr1q3f+566uLgoHVeuXDmYm5vn2+bb3NzccPDgQWRnZ0tGJIwfPx5Xr17F8OHD0a9fPzRq1Ai6urq4efMmgoOD0bx5c3h5eeHo0aOSKQUKixYtgr+/P7p3747BgwfDwcEBcrkc58+fR3BwMBYuXCgunhcfH4+MjAw0aNCg0L4SERERERGVlDKZwAPApEmTUL9+fQQHB2POnDlIT0+HsbExHBwc8NNPP0leXfYhGjRogJUrV2LZsmUICQlB9erVMW/ePPj5+Yl1Pv/8c/z555/48ccfkZqaClNTUzg5OSEkJERpeHpBypUrB2tra2zZsgVJSUkoX748LC0tMWfOHKVX0aWmpmLhwoV4/vw5zMzM0KlTJ3z99ddKc8LnzJmDxMREcTsgIAAAMGbMGIwdOxYAMHnyZFSuXBn79u3Dxo0bUalSJXh4eOCbb755r6kIubm5OHToEJo3b17glyiq3NMP0bJlS/zwww84d+4cGjduLJaXK1cOGzduxG+//Yb9+/dj+/btyMvLQ61atdC5c2cMHDgQN2/exKNHj8TXx72tVq1a2LNnD9avX4/t27fj0aNH0NPTg729PQICAiSL3h0/fhwWFhbitA4iIiIiIqKPTSb8l1ZDok/G2LFjYWhoiPnz5xfruMDAQPz999/igojvq3v37mjevDnGjBnzXsdfvXoVADBw4EBcunTpg/pCnwZXV1dcvHhR3d0ocRkZGYiJiYGdnR3n2akR46B+jIH6MQZlA+OgfoxB6VPkBqo8HCyTc+CJivL111/j4MGDePbsWbGOGzly5Acn7+fPn8eDBw8wYMCADzoPERERERFRcZTZIfSaJicnp8B9MplMXE2fSoadnR0CAgLw6NEjyfoBpSE9PR0LFy5UenUhERERERHRx8QEvoQ4ODgUuM/CwgJHjhwpxd58Gr788ku1tPv2XHgiIiIiIqLSwgS+hBQ2LLuk3ltPREREREREny4m8CWEq5ETERERERHRx8RF7IiIiIiIiIg0AJ/AE6mRnZ2durtAGoJ/VoiIiIiICTyRGgUHB6u7C6RBcnNz+UYLIiIiok8Yh9ATqYlcLkdmZqa6u/FJy8zMxI0bNzQmDkzeiYiIiD5tTOCJ1EgQBHV34ZMmCAIyMzMZByIiIiLSCEzgiYiIiIiIiDQAE3giIiIiIiIiDcAEnoiIiIiIiEgDMIEnUiOZTKbuLhARERERkYZgAk+kJnp6etDX11d3NzRGbm6uurtARERERKRWfA88kRr169cPMTEx6u5GmWdnZ4fg4GB1d4OIiIiISK2YwBOpUUxMDC5duqTubhARERERkQbgEHoiIiIiIiIiDcAEnoiIiIiIiEgDMIEnIiIiIiIi0gBM4ImIiIiIiIg0ABN4IiIiIiIiIg3wn0jgjx07hmHDhsHT0xMODg5o1KgRhg8fjrCwMOTl5ZVYO0eOHEGnTp3g5OQEX19f7N69W6mOra2t0k/jxo2L3VZqairmzZsHb29vODk5oVWrVti0aZNSvdjYWIwePRru7u5wcXFBly5dcOrUKUmdqKgo+Pv7w93dHR4eHhg6dGi+ry5T5frkcjkWLlyIxo0bw8XFBYMHD0ZcXJxSvYiICHTt2hWOjo5o2rQpVqxYofQe79zcXKxfvx5t27ZFvXr10LJlSyxcuBCvXr1S6R4tXLgQ48aNUyqPiorCqFGj4OXlBUdHR/j4+GDy5Mm4evWqUt0lS5Zg4MCB4rYgCNizZw/69u2L+vXrw9HREb6+vliwYAGSkpIAAAkJCXBxcUFCQoJK/SQiIiIiIioJGv8auSVLlmDt2rVo3bo1Zs6cCVNTUzx79gx///03vv32WxgbG6NJkyYf3E5UVBTGjBmDHj16ICAgAGfOnMH333+PChUqoG3btpK6/v7+6NChg7itq6tbrLYyMjLg7+8PbW1tBAQE4LPPPsPdu3eRnp4uqXf79m306dMH3t7e+OWXX6Crq4vr168jMzNTrBMXF4chQ4bA09MTixcvhlwux9q1azFo0CCEhYXB1NS0WNc3b948hIeHY9q0aTA3N0dgYCAGDRqEAwcOoGLFigCAy5cv4+uvv0b79u0xceJE3LlzB8uWLUNmZiamTp0qnmvNmjVYs2YNxo8fD2dnZ9y+fRtLlizBkydPsHjx4kLvUVJSEn777Teld4MHBwdj7ty58PT0xPfffw9zc3MkJSUhNDQUX331Fc6fPy+pHxERge7duwN4k7xPmjQJBw8eRLdu3TB06FAYGhrizp072LFjBx48eIDVq1ejRo0a8PX1xcqVK7Fw4UJVw0pERERERPRhBA0WEREh2NjYCCtWrMh3/5UrV4Tr16+XSFtfffWV0KtXL0nZxIkTBT8/P0mZjY2NsGHDhg9qa+nSpULLli2FV69eFVqvT58+wvjx4wuts3btWsHJyUnIzMwUy+7fvy/Y2NgIe/bsEctUub5Hjx4JdnZ2wo4dO8SyFy9eCC4uLsK6desk5+ratavkXBs3bhQcHByEp0+fimW+vr7C1KlTJfWWL18uODo6CtnZ2YVe1/Lly4WOHTtKymJiYgR7e3thypQpQl5entIxR44ckWwnJCQINjY2Qnx8vCAIgvDrr78KNjY2wq5du5SOzcnJEY4ePSpunzt3TnBwcBCeP39eaD8LEh0dLURHRwuurq4CAP4U8ePq6vpe97kor169EqKioor8rNHHwxiUDYyD+jEG6scYlA2Mg/oxBqVPkRuoQqOH0G/evBmmpqYYNWpUvvudnZ1hb28vbvv7+2PEiBEICwtDmzZtUK9ePYwcORIpKSlITEzEkCFD4Orqivbt2+Ps2bPicXK5HGfPnlV60t6uXTvExsaW+FDqkJAQdO/eHQYGBgXWiY2NxYULF+Dv71/oubKzs6Gnp4dy5cqJZYon5QqqXt/JkyeRl5cnqWdiYoLGjRvj+PHjYllMTIzStAFvb29kZ2fj5MmTYllOTg4MDQ0l9SpWrAhBEAq9JgDYu3cvfH19JWVBQUGQyWSYOnUqZDKZ0jHNmzeXbB89ehTW1tawsrIC8ObPk4ODA3r06KF0rLa2Npo2bSpu169fHyYmJggNDS2yr0RERERERCVBY4fQ5+Tk4OLFi/D19YWOjuqXcePGDbx48QJTpkxBeno65s2bhxkzZiAxMRFdunTB4MGDsXbtWowdOxYRERGoUKEC7t+/j+zsbNSuXVtyrjp16gB4M0y9Ro0aYvm6deuwZMkS6Ovrw9vbG1OmTEH16tVV6l9CQgKePn2KSpUqYeTIkTh58iQMDAzQpk0bfPfdd6hQoQIA4MqVKwDeDLfv2rUrbt26BTMzM/j7+2PIkCHi+dq3b48NGzZg2bJlGDRoEORyOZYsWYJq1aqhZcuWAKDy9cXFxeGzzz6DsbGxUr2QkBBxOysrC3p6epI6iu3Y2FixrGfPnti4cSNatmwJZ2dnxMbGYtu2bejdu3ehMb137x4SExPh5uYmKT9//jwcHR1RuXLlQu7w/zly5IiY1D9+/BgPHjzAyJEjVTpWS0sL9erVw+nTpyVz6OnjyszMVOkLnuKc7+3/UuljDMoGxkH9GAP1YwzKBsZB/RiD0icIQr4PIPOjsQn8y5cvIZfLUa1aNUm5IAiSxdK0tLSgpfV/Aw3S09MRGBgoJnm3bt3Cpk2bMHv2bPTp0wcAYGZmho4dOyIyMhKtWrVCSkoKAMDIyEjSlmJbsR8AunTpgmbNmqFKlSr4999/sWbNGvTt2xf79u1TSnzz8+zZMwBvFmhr06YN1q9fj7t372Lx4sXIyMjAkiVLJPUmT56MQYMGYerUqTh58iR++eUXVKhQAb179wYAWFlZYcuWLfj6668RGBgIALCwsMDmzZvFJ/GqXl9qaqrS03tFvbfvQa1atRAdHS2pc/nyZaV7NWLECMjlcgwePFhMyjp16oSAgIBC75FiMTpbW1tJeVJSEpycnAo9ViEjIwPnzp3D8OHDxWMBKP15KkzdunWV5uDTxxUfH/9R/jG5e/duiZ+TiocxKBsYB/VjDNSPMSgbGAf1YwxK17sPQAuisQm8wrvfVBw+fBjjx48Xt/v164eZM2eK23Xr1pU8oVUMn27UqJFS2ePHj4vdn7cXNXN3d0f9+vXRrVs37Ny5E8OGDSvyeMWq+dbW1uK5vLy8oKOjg+nTp+Obb75BzZo1xXpdunQRpxB4enri8ePHCAwMFBP4+Ph4jB07Fo0bN0aXLl2QlZWFTZs2YdiwYdixYweqVKlS7GssSt++ffH9999j69at6Ny5s7iInba2tqTer7/+iqCgIHz33Xewt7fH7du3sXz5csydOxezZs0q8PxPnz6FlpYWKlWqpLRP1W+uTp8+jfLly6N+/frvdTwAVKpUCS9evEB2dnaxFyqk92NtbV3iT+Dv3r0LKysr6Ovrl9h5SXWMQdnAOKgfY6B+jEHZwDioH2NQ+u7cuaNyXY1N4E1MTKCnp6eUZHt5eYnDufObG//uU2ZF4vX2k2XFtx9ZWVkAID45T0tLkxybmpoq2Z+funXrwtraGtevXy/6ot46l4eHh6Tc09MTwJuV52vWrCleh6JcwcvLC6GhoUhPT4ehoSGWLl2KKlWq4OeffxbrNGzYEM2bN0dQUBAmTpyo8vUZGRkprYSvqPf2PejWrRv+/fdf/Pzzz/jpp5+gq6uLMWPGYOvWrTAzMwMAvHjxAgsXLsSUKVPEefzu7u4wNDTEt99+iwEDBsDa2jrfe5SVlQUdHR2lZNvc3BwPHz7M95h3HTlyBE2aNBGH6pubmwOAyscD0j8nTOBLx8f6R0RfX7/QNSfo42MMygbGQf0YA/VjDMoGxkH9GIPSU5yHiBq7iJ2Ojg7c3NwQGRkpGTJvbGwMJycnODk5qTwMoSiWlpbQ1dVVet+5YvvdueMfombNmoX2W/GlwhdffFHoeeRyOYA33+bUrVtXsq9ChQqwtLTE/fv3Aah+fbVr18azZ88kw+AV9d6+B1paWuKr6Pbt24fTp0/jyy+/RHJyMurVqwcAePDgAeRyOezs7CTnUiw6qOhbfoyNjSGXy8V7odCwYUNcu3YNL1++LPjG4M00i+PHj6NZs2ZiWdWqVWFpaSlZZK8oqamp0NXVVVqIj4iIiIiI6GPQ2AQeAAYPHownT56Ic7s/Fj09PXh4eODw4cOS8vDwcNSpU0eygN27YmJiEB8fr/LcbD09PTRu3BiRkZGS8tOnTwMAHBwcAAAuLi4wMTERy9+uV716dXGaQPXq1RETEyMZdpyeno579+7BwsKiWNfn7e0NLS0t/Pnnn2KdlJQUnDx5Ej4+PkrXUrFiRdStWxdGRkbYtm0batSoIU5VUCzq9+7IhGvXrgFAofdU8WT+3dX//f39kZeXV+C72Y8ePQrgzRz65ORkpT4PGjQI165dw549e5SOzcvLk6y0DwCJiYkFjhIgIiIiIiIqaRo7hB4AmjVrhuHDh2PFihW4efMm/Pz8YGZmhrS0NERFReHp06fiqu0fatSoURgwYABmz54NPz8/nD17FmFhYVi6dKlYZ+PGjbh//z48PDxQuXJl3L59G4GBgahatSp69uypcltjxoxB7969MWnSJHTt2hX37t3D4sWL0bFjR1haWgJ4M/R/7NixmD9/PoyNjeHm5oYTJ07gwIEDmDt3rniu3r17Y/To0Zg8eTI6d+4MuVyOTZs2QS6XS/qkyvVVrVoVPXr0wM8//wwtLS2Ym5tj7dq1qFixojjnHgCio6Nx7tw52NnZ4fXr1zhy5Aj27duH9evXi/Pgq1SpglatWmH58uXIzc2Fvb097ty5g5UrV6JRo0biCvj5cXZ2ho6ODq5duyapV7duXQQEBGDu3LlISkpC9+7dYW5ujqSkJBw4cABRUVE4d+4cIiIi4OrqChMTE8l5+/btiwsXLuD777/HxYsX0bJlSxgYGCAuLg47duyAhYWFJOm/du2a0hx6IiIiIiKij0WjE3gAmDRpEurXr4/g4GDMmTMH6enpMDY2hoODA3766Se0b9++RNpp0KABVq5ciWXLliEkJATVq1fHvHnz4OfnJ9axtrbGn3/+iYMHD+LVq1eoVKkSmjZtigkTJijNvS+Mo6Mj1q9fj0WLFmHUqFEwNjZGr1698M0330jq9e/fH4IgYOvWrQgMDISFhQXmzp0rScxbtWqFZcuWYePGjfjmm2+gq6sLe3t7BAUFiYv1qXp9ADB9+nRUqFABixcvxqtXr+Dm5iZZ0R548+XCn3/+idWrVwMA6tWrh23btsHV1VVyroULF2L16tXYvn07kpKSYGpqio4dO2Ls2LGF3h8DAwM0adIEx48fR+fOnSX7+vXrB1tbW2zcuBE//PAD0tPTUblyZXh6emLz5s0A3jyJb9eundJ5ZTIZFi9eDG9vb+zatQsHDhyAXC6HhYUFWrRoga+++kqs+/z5c1y/fh0TJ04stK9EREREREQlRSaU5JLORKXkyJEjmDRpEk6fPl2shc2SkpLg4+ODAwcO4PPPP3/v9oODg7Flyxb8+eefxVp0QkHxKryBAwfi0qVL792PT4WrqysuXrxY4ufNyMhATEwM7OzsuEiLmjAGZQPjoH6MgfoxBmUD46B+jEHpU+QGqky71ug58PTpat68OaytrbFr165iHWdubo5bt259UPKel5eHoKAgjB49+r2SdyIiIiIiovfBBL6U5eTkFPjz9mr6VDiZTIbZs2er5d2UT548QdeuXdGpU6dSb5uIiIiIiD5dGj8HXpMkJCSgZcuWBe5v2LAhtm3bVoo90mzOzs5wdnYu9XarVq2KkSNHlnq7RERERET0aWMCX4rMzMwQEhJS4P6SWjGfiIiIiIiI/nuYwJciPT09ld8HT0RERERERPQ2JvBEamRnZ6fuLmgE3iciIiIiIibwRGoVHBys7i5ojNzcXGhra6u7G0REREREasNV6InURC6XIzMzU93d0BhM3omIiIjoU8cEnkiNBEFQdxeIiIiIiEhDMIEnIiIiIiIi0gBM4ImIiIiIiIg0ABN4IiIiIiIiIg3ABJ5IjWQymbq7QEREREREGoIJPJGa6OnpQV9fX93dKDW5ubnq7gIRERERkUbje+CJ1Khfv36IiYlRdzc+Ojs7O77znoiIiIjoAzGBJ1KjmJgYXLp0Sd3dICIiIiIiDcAh9EREREREREQagAk8ERERERERkQZgAk9ERERERESkAZjAExEREREREWkAJvBEREREREREGoAJPBEREREREZEG+E8k8MeOHcOwYcPg6ekJBwcHNGrUCMOHD0dYWBjy8vJKrJ0jR46gU6dOcHJygq+vL3bv3l1o/R9//BG2trb44YcfitWOv78/bG1tlX5iY2Ml9dLS0hAQEICGDRvC1dUV48aNw5MnTyR1WrRoke+5bG1tcfnyZUndpKQkTJ06FZ6ennB2doafnx/2799f7Db37NmDL7/8Eg0bNhTv1erVqyGXyyX15HI5fvnlF3h7e8PZ2Rk9evRAZGSkyvdp4cKFGDdunFJ5VFQURo0aBS8vLzg6OsLHxweTJ0/G1atXleouWbIEAwcOFLcFQcCePXvQt29f1K9fH46OjvD19cWCBQuQlJQEAEhISICLiwsSEhJU7isREREREdGH0vj3wC9ZsgRr165F69atMXPmTJiamuLZs2f4+++/8e2338LY2BhNmjT54HaioqIwZswY9OjRAwEBAThz5gy+//57VKhQAW3btlWqf+vWLezevRuGhobv1Z6bmxumTp0qKatRo4Zke8KECbhz5w5mz56NcuXKYdmyZRg2bBh2794NHZ03oV21apVS4rxo0SLExsbC0dFRLHvy5Al69eoFa2trzJ07F4aGhrh9+7bSsaq0mZKSgiZNmmD48OEwNDREdHQ0Vq1ahcePH2Pu3LniuX766Sfs27cPEyZMgLW1Nf744w8MGzYMv//+OxwcHAq9P0lJSfjtt98QHBwsKQ8ODsbcuXPh6emJ77//Hubm5khKSkJoaCi++uornD9/XlI/IiIC3bt3B/AmeZ80aRIOHjyIbt26YejQoTA0NMSdO3ewY8cOPHjwAKtXr0aNGjXg6+uLlStXYuHChYX2k4iIiIiIqMQIGiwiIkKwsbERVqxYke/+K1euCNevXy+Rtr766iuhV69ekrKJEycKfn5++dbv16+fsHz5cqF58+bCnDlzitVW//79heHDhxda5+LFi4KNjY1w4sQJsSw2NlawtbUVDhw4UOBxr169ElxcXITZs2dLyidPniz06tVLyMnJKfE2BUEQlixZIjg7O4vnf/z4sWBnZycEBQWJdfLy8oQOHToII0eOLPRcgiAIy5cvFzp27Cgpi4mJEezt7YUpU6YIeXl5SsccOXJEsp2QkCDY2NgI8fHxgiAIwq+//irY2NgIu3btUjo2JydHOHr0qLh97tw5wcHBQXj+/HmRfc1PdHS0EB0dLbi6ugoA/vM/rq6u73WfPrZXr14JUVFRwqtXr9TdlU8WY1A2MA7qxxioH2NQNjAO6scYlD5FbqAKjR5Cv3nzZpiammLUqFH57nd2doa9vb247e/vjxEjRiAsLAxt2rRBvXr1MHLkSKSkpCAxMRFDhgyBq6sr2rdvj7Nnz4rHyeVynD17VulJe7t27RAbG6s0lHr//v1ISEjAsGHDSvBqpY4fPw4jIyM0btxYLKtduzbs7Oxw/PjxAo/7559/kJGRgY4dO4pl6enpOHjwIPr27Qttbe0SbxMATExMkJOTI05puHnzJnJzcyXnkslk8Pb2xsmTJ5We/L9r79698PX1lZQFBQVBJpNh6tSpkMlkSsc0b95csn306FFYW1vDysoKwJs/Tw4ODujRo4fSsdra2mjatKm4Xb9+fZiYmCA0NLTQfhIREREREZUUjR1Cn5OTg4sXL8LX11ccuq2KGzdu4MWLF5gyZQrS09Mxb948zJgxA4mJiejSpQsGDx6MtWvXYuzYsYiIiECFChVw//59ZGdno3bt2pJz1alTBwAQFxcnDm9PT0/Hzz//jICAAOjr67/39Z07dw4uLi7Izc1FvXr1MH78eLi7u4v74+LiYG1trZSo1q5dG3FxcQWeNywsDBYWFnBzcxPLrl+/juzsbOjo6KB///64dOkSTExM0KVLF0yYMAG6urrv1WZOTg6ys7Nx/fp1bN26FX369BHPpUjQ9fT0JMfo6elBLpcjISFB6X4r3Lt3D4mJiZJrAIDz58/D0dERlStXLvD633bkyBExqX/8+DEePHiAkSNHqnSslpYW6tWrh9OnT0vm0FPhMjMzIQiCurshyszMlPyXSh9jUDYwDurHGKgfY1A2MA7qxxiUPkEQ8n0AmR+NTeBfvnwJuVyOatWqScoFQUBubq64raWlBS2t/xtokJ6ejsDAQDHJu3XrFjZt2oTZs2ejT58+AAAzMzN07NgRkZGRaNWqFVJSUgAARkZGkrYU24r9wJs557Vq1UK7du3e+9rc3d3RuXNnWFlZ4cmTJ9i4cSMGDx6Mbdu2wdXVFQCQmpqKihUrKh1rbGyMa9eu5XveFy9e4NSpU/jqq68k5c+ePQMATJ8+HV9++SXGjBmD6OhorFixAlpaWpg0aVKx28zJyZHMY+/atSsCAgLE7Vq1agEAoqOjJXP7FQvrvX1P36VYjM7W1lZSnpSUBCcnpwKPe1tGRgbOnTuH4cOHi8cCUPrzVJi6desqzcGnwsXHx5fJfwzu3r2r7i588hiDsoFxUD/GQP0Yg7KBcVA/xqB0vftgsyAam8ArvPtNxeHDhzF+/Hhxu1+/fpg5c6a4XbduXckTWsXw6UaNGimVPX78uFh9uX37NoKDg7Fz585iHfeud1dWb9asGTp06ID//e9/WL9+/Xuf9+DBg8jOzkaHDh0k5Yph7Y0aNcK0adMAAJ6ennj16hU2bdqE0aNHo3z58sVqS0dHByEhIcjKysK1a9ewZs0afPfdd+KibzY2NmjQoAEWLVqEatWqwcrKCn/88Ye4yFxh30A9ffoUWlpaqFSpktI+Vb+5On36NMqXL4/69eu/1/EAUKlSJbx48QLZ2dniyAIqnLW1dZl7An/37l1YWVl90IgZen+MQdnAOKgfY6B+jEHZwDioH2NQ+u7cuaNyXY1N4E1MTKCnp6eUZHt5eSEkJAQA8p0b/+5TdEXi9faTZcW3H1lZWQDePGEG3rxC7W2pqamS/QsWLEDbtm1hYWEh7svLy0N2djZSU1NhaGgoGQ2gKgMDAzRt2hSHDx+WXEd+XzCkpKSI/XlXWFgYbG1tYWNjIylX3BNPT09JuZeXFwIDA3Hv3j3Y2toWu03F0/AGDRqgRo0aGD16NPr37y+WL1iwABMmTEDv3r0BABYWFvj666+xcuVKmJqaFng/srKyoKOjo5Rsm5ub4+HDhwUe97YjR46gSZMm4vQLc3NzAFD5eED654QJvGrK6j8C+vr6MDAwUHc3PmmMQdnAOKgfY6B+jEHZwDioH2NQeorzEFFjF7HT0dGBm5sbIiMjJUPmjY2N4eTkBCcnJ5WHIRTF0tISurq6SvO8FduKudrx8fHYv38/3N3dxZ9Hjx5h586dcHd3R3x8fIn0R9FmfHy80tPM+Pj4fOeOP3z4EBcvXlR6+g4An3/+eaFtKb7IKG6bb1O8su7+/ftiWc2aNbF79278888/OHDgAP766y+UL18epqamsLCwKPBcxsbGkMvlYr8UGjZsiGvXruHly5eF9kUQBBw/fhzNmjUTy6pWrQpLS0ucPHmy0GPflpqaCl1d3fd+VSAREREREVFxaGwCDwCDBw/GkydPEBgY+FHb0dPTg4eHh+QJOACEh4ejTp064hzuJUuWICgoSPJTpUoVtGrVCkFBQahevfp7tZ+RkYGjR49K5nf7+PggJSUFkZGRYll8fDxu3LgBHx8fpXOEhYUBQL4JvIWFBWxsbHD69GlJuWKYuSLBL26bb7tw4QKAN0n7u2rUqIHPP/8c2dnZCAkJQc+ePQs9l7W1NQAorf7v7++PvLy8At/NfvToUQBv5tAnJycr9XnQoEG4du0a9uzZo3RsXl6e0kr7iYmJYl+IiIiIiIg+No0dQg+8mRs+fPhwrFixAjdv3oSfnx/MzMyQlpaGqKgoPH36FBUqVCiRtkaNGoUBAwZg9uzZ8PPzw9mzZxEWFoalS5eKdVxcXJSOK1euHMzNzeHh4aFSO1FRUdiwYQNat24NCwsLPHnyBJs3b8bTp0+xfPlysZ6rqyu8vb0REBCAqVOnoly5cli6dClsbW3Rpk0bpfOGhYXBzc2twC8RvvnmG3z99df48ccf0axZM1y9ehWbNm3CkCFDxKEzqrbZr18/tG7dGrVr14aWlhauXLmCTZs2oUmTJnB2dhbr/frrrzA0NES1atWQmJiIzZs3o1y5ckW+fs/Z2Rk6Ojq4du2a+CYA4M36BgEBAZg7dy6SkpLQvXt3mJubIykpCQcOHEBUVBTOnTuHiIgIuLq6wsTERHLevn374sKFC/j+++9x8eJFtGzZEgYGBoiLi8OOHTtgYWEhSfqvXbumNIeeiIiIiIjoY9HoBB4AJk2ahPr16yM4OBhz5sxBeno6jI2N4eDggJ9++gnt27cvkXYaNGiAlStXYtmyZQgJCUH16tUxb948+Pn5lcj5FUxNTZGdnY2lS5fi5cuX0NfXh6urK+bMmSNJfgFg2bJlmD9/PmbOnImcnBx4e3tj+vTpSq/Vu3PnDm7duoVZs2YV2G6LFi2wZMkS/O9//8P27dthZmaGsWPHiqu0F6dNR0dH7Ny5Ew8fPoSOjg5q1KiBsWPHom/fvpJzyeVyrFq1Co8fP4aJiQnatGmD8ePHFznXxsDAAE2aNMHx48fRuXNnyb5+/frB1tYWGzduxA8//ID09HRUrlwZnp6e2Lx5M4A3T+Lze0uATCbD4sWL4e3tjV27duHAgQOQy+WwsLBAixYtJKv3P3/+HNevX8fEiRML7SsREREREVFJkQllaUloIhUdOXIEkyZNwunTp4u1MFpSUhJ8fHxw4MCBIuf+FyY4OBhbtmzBn3/+WaxFJxQUr8IbOHAgLl269N790BSurq64ePGiuruhJCMjAzExMbCzs+MiLWrCGJQNjIP6MQbqxxiUDYyD+jEGpU+RG6jySmyNngNPn67mzZvD2toau3btKtZx5ubmuHXr1gcl73l5eQgKCsLo0aPfK3knIiIiIiJ6H0zgS1lOTk6BP2+vpk+Fk8lkmD17tlpeS/bkyRN07doVnTp1KvW2iYiIiIjo06Xxc+A1SUJCAlq2bFng/oYNG2Lbtm2l2CPN5uzsrLQuQGmoWrUqRo4cWertEhERERHRp40JfCkyMzNDSEhIgftLasV8IiIiIiIi+u9hAl+K9PT0VFqYgIiIiIiIiOhdTOCJ1MjOzk7dXSgVn8p1EhERERF9TEzgidQoODhY3V0oNbm5udDW1lZ3N4iIiIiINBZXoSdSE7lcjszMTHV3o9QweSciIiIi+jBM4InUSBAEdXeBiIiIiIg0BBN4IiIiIiIiIg3ABJ6IiIiIiIhIAzCBJyIiIiIiItIATOCJ1Egmk6m7C0REREREpCGYwBOpiZ6eHvT19dXdDQBvXvFGRERERERlG98DT6RG/fr1Q0xMjFr7YGdn90m9j56IiIiISFMxgSdSo5iYGFy6dEnd3SAiIiIiIg3AIfREREREREREGoAJPBEREREREZEGYAJPREREREREpAGYwBMRERERERFpACbwRERERERERBrgP7UK/bFjx/Drr7/i6tWrSEtLg7GxMRwdHdGpUye0a9cOWlol833FkSNHsGzZMsTHx6N69eoYPnw4unfvLu6Pi4vDr7/+ijNnziAxMRGfffYZmjRpgvHjx6Ny5coqt5OZmYn//e9/CA8Px7Nnz1C1alV07doVQ4cOhY7O/4UuNjYWCxYswPnz56Grq4tmzZrhu+++k7T1xx9/4LvvvlNqY9iwYZg8ebK4HR4ejoMHD+LKlStISkrClClTMGTIkHz7d/nyZSxbtgxXrlyBTCbD559/jjlz5sDOzg7Am3isX78ed+7cQXp6OszNzdGqVSuMGTMGFStWFM9ja2tb4D04ceIEzMzMCr1PCxcuRGJiIlasWIEWLVogMTGx0PpjxozB2LFjxe3evXujSZMmWLFiRaHHAcCtW7cwffp0AMC8efOKrE9ERERERFRS/jMJ/JIlS7B27Vq0bt0aM2fOhKmpKZ49e4a///4b3377LYyNjdGkSZMPbicqKgpjxoxBjx49EBAQgDNnzuD7779HhQoV0LZtWwDA6dOnERUVhV69eqFu3bp4+PAhVqxYgXPnzmHfvn3Q09NTqa0ffvgBf/75JyZOnIg6derg8uXLWLFiBTIzM/HNN98AANLT0zFw4ECYm5tj0aJFeP36NZYsWYIRI0bg999/V/rSYsOGDZLk2dzcXLL/0KFDePDgAZo1a4bff/+9wL5FRkaKX1wMGzYMOTk5iI6ORmZmpljn5cuXcHZ2hr+/P0xMTHD79m2sXLkSt2/fxqZNm8R6+bUzdepU6OvrF5m8JyUl4bfffhPfY75q1SrI5XJx/5gxY+Dm5oavvvpKLKtatar4/8nJybhy5QoCAgLQuHFjsfzo0aNYs2aN0v0C3nzp0b59ewwdOhRWVlaF9o+IiIiIiKik/CcS+KNHj2Lt2rVKT1YBwM/PDwMHDpQ8sf4Qa9asgbOzM3744QcAgKenJx48eIAVK1aICXz79u3Rr18/yGQy8bhatWqhT58+iIiIgK+vb5Ht5OXl4eDBgxgyZAj69esnthUfH48DBw6ICfxvv/2GtLQ07N27F1WqVBHb6tGjB/755x+0bt1acl4HB4dCRwEsW7ZMTPoLSuBzcnLw/fffY8CAAfj222/F8qZNm0rqde7cWbLt4eEBPT09zJgxA0lJSeKXBy4uLpJ6CQkJuHv3ruTcBfn9999Rq1YtODo6AgDs7e0l+/X09FClShWlNhSOHTsGU1NTODs7S8rj4uIA5H+/atWqBTc3NwQHB+P7778vso9EREREREQl4T8xB37z5s0wNTXFqFGj8t3v7OwsSez8/f0xYsQIhIWFoU2bNqhXrx5GjhyJlJQUJCYmYsiQIXB1dUX79u1x9uxZ8Ti5XI6zZ8+KibpCu3btEBsbi4SEBABApUqVJMk78H+J5ZMnT1S6JkEQkJOTo/T0t2LFihAEQdy+ceMG6tatKybvAODk5AQTExMcOXJEpbbepso0g9OnTyMxMREDBgwo9vlNTEwAANnZ2QXWCQsLg0wmQ4cOHYo83969e1X6QqQgERERaNasWbGPa9u2LUJDQ5GTk/PebRMRERERERWHxifwOTk5uHjxIjw9PYv1lP3GjRsICgrClClTMGfOHERFRWHGjBkYN24cmjVrhpUrV6Jy5coYO3YsXr16BQC4f/8+srOzUbt2bcm56tSpA+D/ntrm58KFC5K6RdHW1ka3bt3w66+/Ijo6Gq9evcLp06exb98+9O/fX6yXlZWV75B8PT29fPvToUMH2NnZoWXLlli7di1yc3NV6s/brly5AhMTE1y9ehW+vr6wt7eHr68v9u7dm2/93NxcZGVl4fr161i9ejVatGiBGjVqFHj+AwcOwN3dXTLUPT/37t1DYmIi3Nzcin0NwJsvEU6ePInmzZsX+1g3Nze8ePECMTEx79U2ERERERFRcWn8EPqXL19CLpejWrVqknJBECTJqZaWluTpcnp6OgIDA8Xh0bdu3cKmTZswe/Zs9OnTBwBgZmaGjh07IjIyEq1atUJKSgoAwMjISNKWYlux/11ZWVlYuHAh7O3t4eXlpfK1zZo1C7NmzULPnj3FshEjRmDw4MHitpWVFf744w+8fv0a5cuXBwA8fPgQT58+hYGBgVjP1NQUY8eORb169SCTycSF+JKSkjBz5kyV+wQAT58+RWZmJgICAjBu3DjUqVMHYWFhmDp1qrhg39uaN2+OpKQkAECTJk2wePHiAs998+ZN/Pvvv+IUhcJcvXoVQOGL4BXm/PnzyM3NLVZMFD7//HNoa2sjOjoaTk5O79V+WZOZmSkZ3fEpUKzZ8PbaDVS6GIOygXFQP8ZA/RiDsoFxUD/GoPQJgqA0grsgGp/AK7x7wYcPH8b48ePF7X79+kkS1bp160rmNisWI2vUqJFS2ePHjz+ob7NmzUJCQgJ27NihcmAAYNGiRTh69CjmzZsHKysrXL58GatXr4aRkRGGDh0KAOjZsyeCgoIwc+ZMTJo0Ca9fv8aMGTOgpaUlaatJkyaSxNrb2xvlypXD1q1bMXLkyCIXi3ubIAjIysrC5MmTxdEAXl5eiIuLQ2BgoFICv27dOmRmZuLOnTtYs2YNRo4cic2bN0NbW1vp3KGhodDV1VVpWPzTp0+hpaWFSpUqqdz3t0VERMDLy0v84qM4dHR0ULFiRZWnRGiC+Pj4T/Yv6rt376q7C588xqBsYBzUjzFQP8agbGAc1I8xKF2qLnSu8Qm8iYkJ9PT0lJJsLy8vhISEAEC+c+PffYquq6sLAJI554qbmJWVBQAwNjYGAKSlpUmOTU1Nlex/29KlSxEaGorAwEDY2NiofF3//vsvNm3ahDVr1qBFixYAAHd3d+Tk5GD58uXo3bs3DA0NUbt2bfz444/48ccfsW/fPgBAmzZt4OPjIw79L4ifnx82bdqEmJiYYiXwinvn6ekpKffy8hJXg39b3bp1AQCurq5wcnJC586d8ddffymtJSAIAsLDw9GkSRNxrnxhsrKyoKOjU6wvRd529OjRAl+Rpwo9PT3xz8Z/gbW19Sf5BP7u3buwsrKCvr6+urvzSWIMygbGQf0YA/VjDMoGxkH9GIPSd+fOHZXranwCr6OjAzc3N0RGRiI3N1d8qmtsbCwObVb124yiWFpaQldXF3FxcZKnzIq55u/Ojd+2bRvWrl2LBQsWFPsVdoogKt6prmBvbw+5XI6kpCQYGhoCALp06YJ27drh7t27MDY2hrm5Odq3by8m/iXtiy++KHBfUQmtra0tdHV1cf/+faV9Fy5cwMOHD1VafR54E2O5XI6srCyUK1dOpWMUYmNjcf/+/fea/66Qlpam0hcNmuJT/gtaX19fMuWESh9jUDYwDurHGKgfY1A2MA7qxxiUnuI8kNT4RewAYPDgwXjy5AkCAwM/ajt6enrw8PDA4cOHJeXh4eGoU6eOZGG2sLAw/Pjjj5g4cSK6dOlS7LYsLCwAANevX5eUX7t2DTKZDNWrV1fqm42NDczNzREZGYm7d++ia9euhbYRHh4ObW1tpVevFcXb2xu6uro4ffq0pPz06dNwcHAo9NgrV64gOzs730XsQkNDYWBgoPIXD9bW1gAgrv5fHBEREbC3txdfZVdcycnJyMzMFPtARERERET0sWn8E3gAaNasGYYPH44VK1bg5s2b8PPzg5mZGdLS0hAVFYWnT5+iQoUKJdLWqFGjMGDAAMyePRt+fn44e/YswsLCsHTpUrHOuXPnMG3aNHh6eqJhw4a4fPmyuK9q1apFrq4OAI6OjnB0dMSsWbPw/PlzWFpaIjo6GuvWrUP37t3Fp6UZGRlYuXIl3N3dUa5cOVy+fBnr1q3DmDFjJCMChgwZAg8PD3HBt3/++Qc7d+7EgAEDYGpqKta7c+eOZAjHv//+i0OHDkFfX198z3uVKlXg7++P5cuXQyaToU6dOjhw4AAuX76MDRs2iMeOGTMGjo6OsLW1Rfny5XHz5k1s3LgRtra2aNWqleR6c3JycPjwYbRq1UrlOenOzs7Q0dHBtWvXVF7dX+F9Xx+noFhAr379+u99DiIiIiIiouL4TyTwADBp0iTUr18fwcHBmDNnDtLT02FsbAwHBwf89NNPaN++fYm006BBA6xcuRLLli1DSEgIqlevjnnz5sHPz0+sc/bsWWRnZyMyMhKRkZGS48eMGYOxY8cW2Y62tjYCAwOxfPlyrF27Fs+fP0fVqlUxdOhQDBs2TKynpaWFf//9F3/88QcyMjJQu3ZtzJo1C926dZOcz9raGrt378bjx4+Rl5cHKysrBAQEwN/fX1Lv4MGDWLVqlbi9d+9e7N27FxYWFpL3yk+aNAkGBgbYuHEjkpOTUadOHaxevRre3t5iHWdnZ4SHh2PdunUQBAEWFhbo2bMnhgwZojSt4eTJk3jx4oVK735XMDAwQJMmTXD8+HF07txZ5eNSUlJw6dIlTJ06VeVj3nXixAk0aNAAVapUee9zEBERERERFYdM+NRWraL/lCNHjmDSpEk4ffq0ynO4Q0NDsWDBApw8efK9FsDLyclBs2bNMHny5PeaHgH83xP8gQMH4tKlS+91jpLi6uqKixcvqrUP6pKRkYGYmBjY2dlxjpeaMAZlA+OgfoyB+jEGZQPjoH6MQelT5AaqvJ76PzEHnj5dzZs3h7W1NXbt2qXyMR07dsSpU6fee/X6sLAwVKhQoVijBYiIiIiIiD4UE3g1ycnJKfAnNzdX3d3TGDKZDLNnzy7VFdRlMhl+/PFH6Oj8Z2agEBERERGRBmAGogYJCQlo2bJlgfsbNmyIbdu2lWKPNJuzszOcnZ1Lrb3izLcnIiIiIiIqKUzg1cDMzAwhISEF7i+pFfOJiIiIiIjov4MJvBro6emptEABERERERERkQLnwBMRERERERFpAD6BJ1IjOzs7dXehTPSBiIiIiIiKxgSeSI2Cg4PV3QUAQG5uLrS1tdXdDSIiIiIiKgSH0BOpiVwuR2Zmprq7AQBM3omIiIiINAATeCI1EgRB3V0gIiIiIiINwQSeiIiIiIiISAMwgSciIiIiIiLSAEzgiYiIiIiIiDQAE3giIiIiIiIiDcAEnoiIiIiIiEgDMIEnIiIiIiIi0gBM4ImIiIiIiIg0ABN4IiIiIiIiIg3ABJ6IiIiIiIhIAzCBJyIiIiIiItIATOCJiIiIiIiINAATeCIiIiIiIiINwASeiIiIiIiISAMwgSciIiIiIiLSAEzgiYiIiIiIiDQAE3giIiIiIiIiDcAEnoiIiIiIiEgDMIEnIiIiIiIi0gBM4ImIiIiIiIg0ABN4IiIiIiIiIg3ABJ6IiIiIiIhIAzCBJyIiIiIiItIATOCJiIiIiIiINAATeCIiIiIiIiINwASeiIiIiIiISAMwgSciIiIiIiLSAEzgiYiIiIiIiDQAE3giIiIiIiIiDcAEnoiIiIiIiEgDMIEnIiIiIiIi0gAyQRAEdXeC6FNz8eJFCIIAXV1dyGQydXfnkyUIArKzsxkHNWIMygbGQf0YA/VjDMoGxkH9GIPSJ5fLIZPJ4ObmVmRdnVLoDxG9Q/GXIf9SVC+ZTAY9PT11d+OTxhiUDYyD+jEG6scYlA2Mg/oxBqVPJpOpnBfwCTwRERERERGRBuAceCIiIiIiIiINwASeiIiIiIiISAMwgSciIiIiIiLSAEzgiYiIiIiIiDQAE3giIiIiIiIiDcAEnoiIiIiIiEgDMIEnIiIiIiIi0gBM4ImIiIiIiIg0ABN4IiIiIiIiIg3ABJ6IiIiIiIhIAzCBJyIiIiIiItIATOCJiIiIiIiINAATeKJSFhsbi8GDB8PFxQWNGzfGzz//DLlcru5uabw//vgDtra2Sj+LFi2S1Nu1axd8fX3h5OSETp06ISIiQulcaWlpCAgIQMOGDeHq6opx48bhyZMnpXUpGuXevXuYOXMmOnfuDHt7e3To0CHfeiV53y9evIhevXrB2dkZzZs3x7p16yAIQolfm6ZQJQb+/v75fj5iY2Ml9RiD93Pw4EGMGjUKPj4+cHFxQefOnRESEqJ0T/g5+HhUiQE/Bx/fsWPH0L9/f3h6esLR0REtW7bE/PnzkZaWJql35MgRdOrUCU5OTvD19cXu3buVziWXy7Fw4UI0btwYLi4uGDx4MOLi4pTq8fcqKVViMG3atHw/C8ePH5ecizEom3TU3QGiT0lKSgoGDhwIKysrrFy5EklJSViwYAFev36NmTNnqrt7/wkbNmxAxYoVxW1zc3Px/w8cOIAZM2Zg5MiR8PT0RHh4OMaMGYPg4GC4uLiI9SZMmIA7d+5g9uzZKFeuHJYtW4Zhw4Zh9+7d0NHhX5tvu337No4dO4Z69eohLy8v319eS/K+37t3D0OGDEHjxo0xYcIE3Lp1C4sWLYK2tjaGDBlSWpddpqgSAwBwc3PD1KlTJWU1atSQbDMG72fLli2wsLDAtGnTUKlSJZw+fRozZszA48ePMWbMGAD8HHxsqsQA4OfgY3v58iWcnZ3h7+8PExMT3L59GytXrsTt27exadMmAEBUVBTGjBmDHj16ICAgAGfOnMH333+PChUqoG3btuK55s2bh/DwcEybNg3m5uYIDAzEoEGDcODAAfHfef5epUyVGABAzZo1lR5y1KlTR7LNGJRRAhGVmsDAQMHFxUV48eKFWLZjxw7Bzs5OePz4sfo69h+we/duwcbGRnj+/HmBddq0aSNMnDhRUtarVy9h6NCh4vbFixcFGxsb4cSJE2JZbGysYGtrKxw4cKDkO67hcnNzxf+fOnWq0L59e6U6JXnfZ8yYITRv3lzIysoSyxYvXiw0aNBAUvYpUSUG/fv3F4YPH17oeRiD95ff3zvTp08X3NzcxPjwc/BxqRIDfg7U4/fffxdsbGzE33O++uoroVevXpI6EydOFPz8/MTtR48eCXZ2dsKOHTvEshcvXgguLi7CunXrxDL+XqWad2NQ0L8Vb2MMyi4OoScqRcePH4eXlxdMTEzEMj8/P+Tl5eHUqVPq69gn4MGDB7h79y78/Pwk5e3atUNkZKQ41Ov48eMwMjJC48aNxTq1a9eGnZ2d0tAyArS0Cv9npKTv+/Hjx9GyZUvo6elJzpWamopLly6VxCVpnKJioCrG4P1VrlxZqczOzg7p6enIyMjg56AUFBUDVTEGJU/xO092djbkcjnOnj0redIOvLl3sbGxSEhIAACcPHkSeXl5knomJiZo3LixUhz4e1XR3o6BqhiDsosJPFEpiouLQ+3atSVlRkZGMDU1zXdOERVfhw4dYGdnh5YtW2Lt2rXIzc0FAPH+WltbS+rXqVMH2dnZePDggVjP2toaMplMUq927dqM0XsoyfuekZGBR48eKX2GateuDZlMxvgU4dy5c3BxcYGTkxP69++P8+fPS/YzBiXrwoULMDc3h6GhIT8HavJ2DBT4OSgdubm5yMrKwvXr17F69Wq0aNECNWrUwP3795Gdna107xRDtxX3Li4uDp999hmMjY2V6r19f/l7VcEKioHCvXv3UL9+fTg6OqJbt274+++/JcczBmUXJ3MSlaLU1FQYGRkplRsbGyMlJUUNPfrvMDU1xdixY1GvXj3IZDIcOXIEy5YtQ1JSEmbOnCne33fvv2JbsT81NVUyh17B2NgY165d+8hX8d9TkvddsQDPu+fS09ODvr4+P0OFcHd3R+fOnWFlZYUnT55g48aNGDx4MLZt2wZXV1cAjEFJioqKQnh4uDjXmp+D0vduDAB+DkpT8+bNkZSUBABo0qQJFi9eDODDPwtGRkaS+8vfqwpWUAyAN6NTnJyc8PnnnyMtLQ3bt2/H6NGjsXz5cvGJO2NQdjGBJ6L/hCZNmqBJkybitre3N8qVK4etW7di5MiRauwZkfqNGzdOst2sWTN06NAB//vf/7B+/Xo19eq/6fHjx/jmm2/g4eGBAQMGqLs7n6SCYsDPQelZt24dMjMzcefOHaxZswYjR47E5s2b1d2tT0pBMdDW1sbAgQMldVu0aIHevXtjxYoVStMbqOzhEHqiUmRkZKT0KhXgzTfO7w5Rog/n5+eH3NxcxMTEiPf33fufmpoKAOJ+IyMjpKenK52LMXo/JXnfFU8C3j2XXC5HZmYm41MMBgYGaNq0Ka5fvy6WMQYfLjU1FcOGDYOJiQlWrlwprk/Az0HpKSgG+eHn4OOpW7cuXF1d0bNnT/zvf//D2bNn8ddff33wZyE1NVVyf/l7VcEKikF+tLS00KZNG8TGxuL169cAGIOyjAk8USnKbx51Wloanj59qjR/iEqW4v6+e//j4uKgq6uLmjVrivXi4+OVXsUVHx/PGL2HkrzvBgYGqFatmtK5FMcxPh+GMfgwr1+/xogRI5CWlqb0Okt+DkpHYTFQFWNQ8mxtbaGrq4v79+/D0tISurq6+X4WgP/7rNSuXRvPnj1TGoL97nxr/l6lmrdjoCrGoOxiAk9Uinx8fHD69Gnxm2YAOHToELS0tCQr3lLJCA8Ph7a2Nuzt7VGzZk1YWVnh0KFDSnW8vLzElYR9fHyQkpKCyMhIsU58fDxu3LgBHx+fUu3/f0FJ33cfHx/8888/kpV0w8PDYWRkJM5hpaJlZGTg6NGjcHJyEssYg/eXk5ODCRMmIC4uDhs2bIC5ublkPz8HH19RMcgPPwel48qVK8jOzkaNGjWgp6cHDw8PHD58WFInPDwcderUERdZ8/b2hpaWFv7880+xTkpKCk6ePKkUB/5eVbS3Y5CfvLw8HDp0CF988QXKly8PgDEoyzgHnqgU9e7dG9u2bcPo0aMxYsQIJCUl4eeff0bv3r1V+mWDCjZkyBB4eHjA1tYWAPDPP/9g586dGDBgAExNTQEAY8eOxeTJk2FpaQkPDw+Eh4cjOjoav/76q3geV1dXeHt7IyAgAFOnTkW5cuWwdOlS2Nraok2bNmq5trIsMzMTx44dAwAkJiYiPT1dTFIaNmyIypUrl+h9HzJkCEJDQzFp0iT06dMH//77LzZu3IhvvvlG8jqnT0lRMVAkNK1bt4aFhQWePHmCzZs34+nTp1i+fLl4Hsbg/c2ZMwcRERGYNm0a0tPTcfnyZXGfvb099PT0+Dn4yIqKQXR0ND8HpWDMmDFwdHSEra0typcvj5s3b2Ljxo2wtbVFq1atAACjRo3CgAEDMHv2bPj5+eHs2bMICwvD0qVLxfNUrVoVPXr0wM8//wwtLS2Ym5tj7dq1qFixInr37i3W4+9VyoqKQWJiIqZNm4b27dujVq1aSElJwfbt23Ht2jWsXLlSPA9jUHbJhHfHCBHRRxUbG4u5c+fi0qVLqFChAjp37vzJ/4NfEubNm4cTJ07g8ePHyMvLg5WVFXr27Al/f3/J64B27dqF9evX4+HDh7C2tsbEiRPRvHlzybnS0tIwf/58/PXXX8jJyYG3tzemT5/Of4jykZCQgJYtW+a7LygoCB4eHgBK9r5fvHgRCxYsQExMDCpXrox+/fph2LBhSq99+lQUFYOqVavihx9+wK1bt/Dy5Uvo6+vD1dUVY8aMgbOzs6Q+Y/B+WrRogcTExHz3/fPPP+JTL34OPp6iYpCbm8vPQSlYt24dwsPDcf/+fQiCAAsLC7Ru3RpDhgyRvM7vn3/+wbJlyxAfH4/q1atj+PDh6NGjh+RccrkcS5cuxb59+/Dq1Su4ublh+vTp4ivnFPh7lVRRMXj58iW+++473LhxA8+fP4euri4cHR0xfPhwyWLAAGNQVjGBJyIiIiIiItIAnANPREREREREpAGYwBMRERERERFpACbwRERERERERBqACTwRERERERGRBmACT0RERERERKQBmMATERERERERaQAm8EREREREREQagAk8ERERlaizZ8/C1tYWZ8+eLdHz2traYuXKlSV6Tvow69evR9u2bZGXl6furhTL9u3b0axZM8jlcnV3hYioWJjAExERfcL++OMP2Nraij/29vZo0qQJpk2bhqSkpFLvz7Fjx8pckv72/Xn7p3HjxsU6T3JyMubNm4e2bdvC2dkZXl5e6NGjB3755Re8evXqI/X+40lPT8eGDRswbNgwaGlJf6WUy+XYtm0b+vTpA3d3dzg6OsLb2xsjR45EWFgYcnNz8z1nbGwsbG1t4eTkhNTU1Hzr+Pv7o0OHDpKyFi1awNbWFnPnzlWqr/hC6dChQ2JZt27dkJ2djR07dhT3somI1EpH3R0gIiIi9Rs3bhxq1KgBuVyOy5cvY8+ePbhw4QLCwsJQrly5UuvHsWPHEBwcjLFjxyrti46Ohra2dqn15W2NGzdG586dJWXly5dX+fiXL1+ie/fuSE9PR/fu3VG7dm28fPkSt27dwvbt29GnTx9UqFChpLv9UYWEhCAnJ0cpmU5OTsbQoUNx/fp1eHt7Y9SoUTA2NsazZ89w+vRpTJo0Cffu3cPo0aOVzrl//36YmpoiJSUFhw8fRs+ePYvVp507d2L48OEwNzcvtF65cuXQpUsXbNmyBf7+/pDJZMVqh4hIXZjAExEREXx8fODk5AQA6NmzJypVqoT169fjn3/+Qbt27dTcuzdK84uEd1lZWSkl8MUREhKChw8fYvv27XBzc5PsS09Ph66u7od2UWUZGRkwMDD44PP88ccfaNGihVJcvv32W8TExGDlypVo06aNZN+IESNw9epVxMfHK51PEASEhoaiQ4cOSEhIwP79+4uVwH/xxReIj4/H+vXrMX369CLr+/n5YcOGDThz5gy8vLxUboeISJ04hJ6IiIiUNGjQAADw4MEDSXlsbCzGjRuHhg0bwsnJCd26dcM///xT5PmioqIwbtw4NGvWDI6OjmjatCl++uknvH79Wqwzbdo0BAcHA5AOW1d4ew78oUOHYGtri3Pnzim1tWPHDtja2uLff//94H6XlPv370NbWxsuLi5K+wwNDZWS4CtXrmDYsGFwd3eHi4sLOnbsiK1bt0rqREZGom/fvnBxcUGDBg0watQoxMbGSuqsXLkStra2uHPnDiZNmgR3d3f07dtX3L9v3z5069YNzs7OaNiwIb755hs8evSoyOt58OABbt26hUaNGknKL126hJMnT+LLL79USt4VnJyc0KlTJ6XyCxcuIDExEe3atUO7du0QFRWFx48fF9kXBQsLC3Tu3Bk7d+5UafqHo6MjTExMSvXPARHRh2ICT0REREoSExMBAEZGRmLZ7du30atXL8TGxmLYsGGYNm0aDAwMMHr0aPz111+Fnu/QoUN4/fo1+vTpgxkzZsDb2xu//vorpkyZItbp1auXOK/8559/Fn/y06xZMxgYGODgwYNK+8LDw/HFF1/Axsbmg/utkJWVheTkZMlPcRZAs7CwQG5uLvbt21dk3VOnTqFfv36IjY3FgAEDMHXqVHh4eODo0aNindOnT2Po0KF4/vw5xowZg0GDBuHSpUvo06cPEhISlM45fvx4ZGZm4ptvvhGfaq9ZswZTp05FrVq1MG3aNAwYMACRkZHo169fgfPPFS5dugQAsLe3l5RHREQAQL4JelFCQ0NhaWkJZ2dntGjRAuXLl0dYWFixzjFq1Cjk5uZi/fr1KtW3t7fHxYsXi91XIiJ14RB6IiIiQnp6upiUXrlyBatWrYKenh6aN28u1vnxxx9RrVo17N69G3p6egCAvn37ok+fPli0aBFat25d4PknT54smTPeq1cv1KpVC0uWLMHDhw9RvXp1uLq6wsrKCqdOnSpyuHr58uXRokULHD58GNOnTxfnxj99+hTnz5/HmDFjSqTfCiEhIQgJCZGUzZ8/H926dSvyWADo3r07tmzZgmnTpmHdunVo2LAh3N3d0bRpU1SsWFGsl5ubi5kzZ8LMzAx79+6VfIEiCIL4/z///DOMjY3x+++/w8TEBADQqlUrdO3aFStXrsTChQsl7detWxeLFy8WtxMTE7Fy5UpMmDABI0eOFMvbtGmDrl274rfffpOUvysuLg4AUKNGjXzLFV+eKGRlZUkW6tPR0ZFcW3Z2Ng4dOoTevXsD+L/4hoaGYujQoQX24101a9ZEp06dxLnwZmZmRdZnAk9EmoRP4ImIiAiDBg2Cl5cXmjZtinHjxkFfXx9r1qxB1apVAbxZhO3MmTPw8/MTk/3k5GS8ePEC3t7euHv3bqHDlt9O3jMyMpCcnAxXV1cIgoAbN268V5/9/Pzw/PlzyTD6w4cPIy8vT5y3/6H9VmjZsiU2b94s+fH29la5r1WqVMG+ffvQu3dvpKamYseOHZg0aRK8vLywevVqMTm/ceMGEhISMGDAAEmCC0BcaO3JkyeIiYlB165dxeQdeJOkN2rUCMeOHVNqX5EYK/z111/Iy8uDn5+fZFRBlSpVUKtWrSJfAfjy5Uvo6OgoLbyXnp4OAEpz7Ldv3w4vLy/x5+1h/ABw/PhxvHz5UrIgXocOHXDz5k3cvn270L686+uvv0Zubi7WrVtXZF0jIyO8fv0amZmZxWqDiEhd+ASeiIiIMHPmTFhbWyMtLQ27d+/G+fPnxafVwJs53IIgYPny5Vi+fHm+53j+/HmBq38/fPgQK1aswJEjR5CSkiLZp0j6isvHxwcVK1ZEeHi4uAhZeHg47OzsYG1tXSL9VqhatarSfO/iMjMzw5w5czB79mzcvXsXJ0+exPr167FixQqYmZmhZ8+e4poD7z7BftvDhw8BQLzGt9WpUwcnT55UWqju3Sfld+/ehSAIBc5T19F5v18RFQl9RkaGZGSBr6+veE0LFixQem/8/v37UaNGDejp6eHevXsAAEtLS+jr6yM0NBQTJ05UuQ/vPoUvjOKLE65CT0Saggk8ERERwdnZWVyFvlWrVujbty8mTZqEQ4cOoUKFCmLC9dVXX6FJkyb5nsPS0jLf8tzcXAwePBgpKSkYOnQoateuDQMDAyQlJWHatGlKyZyq9PT00KpVK/z111+YNWsWnj9/josXL0qSvQ/p98cik8lgbW0Na2trNGvWDG3atCn2iuvF9e4ieXl5eZDJZFi/fn2+r+YrapV6ExMT5OTkID09HYaGhmJ57dq1AQD//vsv6tevL5ZXq1YN1apVAwAYGxvjxYsX4r709HREREQgKysr3y8UwsLC8M033xQryR41ahT279+P9evXo1WrVgXWS01Nhb6+frFeCUhEpE5M4ImIiEhCW1sbEydOxIABAxAcHIzhw4ejZs2aAABdXd1iP4n+999/cffuXSxcuBBdunQRy0+dOqVUt7hPQv38/LBnzx5ERkYiNjYWgiDAz89P3P8h/S4NNWvWhJGREZ4+fSpuA2/uWUH9rV69OgDk+yq2uLg4VKpUqcgE3NLSEoIgoEaNGvk+yS+KIlFPSEhA3bp1xfJmzZph3bp1CA0NlSTwhfnzzz+RlZWF2bNno1KlSpJ98fHxWLZsGS5cuCC+GUEVlpaW6NSpE37//XfUq1evwHoJCQnitRARaQLOgSciIiIlHh4ecHZ2xtatW5GVlYXPPvsMDRs2xO+//44nT54o1U9OTi7wXFpab37deHsRNkEQEBQUpFRXX18fAIpcBV2hUaNGMDExQXh4OA4ePAhnZ2cxCQbwQf0uSVeuXEFGRoZSeXR0NF6+fCkm0Q4ODqhRowaCgoKU7oHi/pmZmcHOzg579+6V1Pn3339x6tQpNG3atMj+tGnTBtra2li1apUkLop23n5Cnh9XV1cAwLVr1yTl9evXR+PGjbFz5078/fff+R77bnv79+9HzZo10adPH7Rt21byM2TIEBgYGCA0NLTIa3rXqFGjkJOTgw0bNhRY58aNG3Bzcyv2uYmI1IVP4ImIiChfQ4YMwfjx4/HHH3+gT58+mDVrFvr27YuOHTviyy+/RM2aNfHs2TNcvnwZjx8/xv79+/M9T+3atWFpaYmFCxciKSkJhoaGOHz4cL5JuoODAwBg3rx58Pb2hra2Ntq3b19gH3V1ddG6dWscOHAAmZmZmDp1qlKd9+13Sdq3bx9CQ0PRqlUrODo6QldXF7Gxsdi9ezfKlSsnrviupaWF2bNnY9SoUejSpQu6desGU1NTxMXF4c6dO9i4cSMAYMqUKRg2bBh69eqFHj164PXr1/j1119RsWJFyQr8BbG0tMSECROwePFiJCYmolWrVqhQoQISEhLw999/48svv8SQIUMKPL5mzZqwsbFBZGQkevToIdn3yy+/YOjQoRg9ejR8fHzQqFEjGBkZ4dmzZzh9+jTOnz8PHx8fAEBSUhLOnj0Lf3//fNvR09NDkyZNcOjQIUyfPh26uroq3W/FNXbq1Al79uzJd/+1a9fw8uVLtGzZUuVzEhGpGxN4IiIiylebNm1gaWmJTZs24csvv8Tnn3+O3bt3Y9WqVdizZw9evnyJypUrw97eHqNHjy7wPLq6uggMDMS8efOwdu1alCtXDq1bt0a/fv2UXhfXpk0b+Pv748CBA9i/fz8EQSg0gQeAdu3aYdeuXZDJZJLh8wrv2++S1KtXL5QvXx5nzpzBkSNHkJ6ejkqVKqFx48YYMWKE5H3qTZo0wdatW7F69Wps2rQJgiCgZs2a+PLLL8U6jRo1woYNG7BixQqsWLECOjo6cHd3x7fffisZgVCY4cOHw8rKClu2bMHq1asBvFmsr3HjxmjRokWRx3fv3h3Lly/H69evJXPIP/vsM+zYsQM7duzAwYMHsWrVKrx+/RqVKlWCo6MjFi1aJL4lIDw8HHl5eZLXFb6refPmOHz4MI4fP17sZFsxFz43N1dp36FDh1C9enV4enoW65xEROokE94dx0REREREVIS0tDS0atUKkydP/qgL8H0McrkcLVq0wLBhwzBw4EB1d4eISGWcA09ERERExVaxYkUMGTIEGzdufO83CajL7t27oaOjgz59+qi7K0RExcIn8ERERETvSS6XK73X/l0VK1bka8qIiKhEcA48ERER0Xu6dOkSBgwYUGid+fPno1u3bqXUIyIi+i/jE3giIiKi95SSkoLr168XWufzzz+HmZlZKfWIiIj+y5jAExEREREREWkALmJHREREREREpAGYwBMRERERERFpACbwRERERERERBqACTwRERERERGRBmACT0RERERERKQBmMATERERERERaQAm8EREREREREQagAk8ERERERERkQb4fwVoXlxDFZzrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate figure object\n",
    "figure(num=None, figsize=(10, 10), dpi=100, facecolor='w', edgecolor='k')\n",
    "#load in the 20 lardest values and their SNP label\n",
    "indexes = df.nlargest(20, \"F_Score(GAIN)\").index\n",
    "values = df.nlargest(20, \"F_Score(GAIN)\").values.ravel()\n",
    "#reverse to make the largest be at the front\n",
    "indexes = indexes[::-1]\n",
    "values = values[::-1]\n",
    "#for each different chromosome you want to colour add a index(*_i) and value (*_v) array\n",
    "#black would be colour for singular/notinteresting chromosomes\n",
    "r_i = []\n",
    "r_v = []\n",
    "b_i = []\n",
    "b_v = []\n",
    "g_i = []\n",
    "g_v = []\n",
    "y_i = []\n",
    "y_v = []\n",
    "bl_i = []\n",
    "bl_v = []\n",
    "p_i = []\n",
    "p_v = []\n",
    "br_i = []\n",
    "br_v = []\n",
    "pu_i = []\n",
    "pu_v = []\n",
    "#for each value in the top n (default 20) check which chromosome it belongs to and add it to the colour array\n",
    "i = 0\n",
    "while i < len(indexes):\n",
    "    if('Gm19' in indexes[i]):\n",
    "        r_i.append(indexes[i])\n",
    "        r_v.append(values[i])\n",
    "    elif('Gm20' in indexes[i]):\n",
    "        b_i.append(indexes[i])\n",
    "        b_v.append(values[i])\n",
    "    elif('Gm01' in indexes[i]):\n",
    "        g_i.append(indexes[i])\n",
    "        g_v.append(values[i])\n",
    "    elif('Gm11' in indexes[i]):\n",
    "        y_i.append(indexes[i])\n",
    "        y_v.append(values[i])\n",
    "    elif('Gm08' in indexes[i]):\n",
    "        p_i.append(indexes[i])\n",
    "        p_v.append(values[i])\n",
    "   # elif('Gm04' in indexes[i]):\n",
    "   #     br_i.append(indexes[i])\n",
    "   #     br_v.append(values[i])\n",
    "   # elif('Gm13' in indexes[i]):\n",
    "   #     pu_i.append(indexes[i])\n",
    "   #     pu_v.append(values[i])\n",
    "    else:\n",
    "        bl_i.append(indexes[i])\n",
    "        bl_v.append(values[i])\n",
    "    i = i + 1\n",
    "#plot each of the arrays with appropriate colour and label graph\n",
    "plt.barh(bl_i, bl_v, color=\"black\")\n",
    "plt.barh(br_i, br_v, color=\"brown\")\n",
    "plt.barh(pu_i, pu_v, color=\"purple\")\n",
    "plt.barh(y_i, y_v, color=\"yellow\")\n",
    "plt.barh(p_i, p_v, color=\"orange\")\n",
    "plt.barh(g_i, g_v, color=\"green\")\n",
    "plt.barh(b_i, b_v, color=\"blue\")\n",
    "plt.barh(r_i, r_v, color=\"red\")\n",
    "plt.title('SNP Importance XGBoost Seed Oil')\n",
    "plt.ylabel('SNP Label')\n",
    "plt.xlabel('Relative F_Score (GAIN)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt_vcf, ho_vcf, tt_pheno, ho_pheno = new_prep_data(\"Oil_Merged_filtered.csv_train_test.csv_5pcnt.csv\", \"Oil_Merged_filtered.csv_holdout.csv_5pcnt.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe = pickle.load(open(\"Oil_ohe.dat\", \"rb\"))\n",
    "tt_vcf = ohe.transform(tt_vcf)\n",
    "print(tt_vcf.shape)\n",
    "print(ho_vcf.shape)\n",
    "ho_vcf = ohe.transform(ho_vcf)\n",
    "print(ho_vcf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = RandomForestRegressor(n_estimators=100, random_state=16, max_features = 'sqrt',n_jobs=-1, verbose = 1)\n",
    "#trains the model, and makes the y shape as (m,) instead of (m,1)\n",
    "#model.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_rf_model = eval_k_fold(model, tt_vcf, tt_pheno, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds = [x for x in best_rf_model.predict(ho_vcf)]\n",
    "ss = sqrt(mean_squared_error(all_preds, ho_pheno))\n",
    "rr = r2_score(all_preds, ho_pheno)\n",
    "mm = np.mean(ho_pheno)\n",
    "error_mean = ((ss/mm)*100)\n",
    "print(\"R^2 Value of Holdout: %.2f\" % rr)\n",
    "print(\"RMSE of Holdout: %.2f\" % ss)\n",
    "print(\"Mean of Holdout: %.2f\" % mm)\n",
    "print(\"this is \"+ str((ss/mm)*100) + \"% of the mean pheno data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_ax = range(len(y_test))\n",
    "print(x_ax)\n",
    "plt.plot(x_ax, y_test, label=\"original\")\n",
    "plt.plot(x_ax, y_pred, label=\"predicted\") \n",
    "\n",
    "plt.title(\"Test and predicted data\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "i = 0\n",
    "interval = 65\n",
    "while(i<len(y_test)):\n",
    "    i = i + interval\n",
    "    if(i>len(y_test)):\n",
    "        my_range = range(i-interval,len(y_test))\n",
    "    else:\n",
    "        my_range = range(i-interval,i)\n",
    "    plt.plot(my_range, y_test[my_range], label=\"original\")\n",
    "    plt.plot(my_range, y_pred[my_range], label=\"predicted\") \n",
    "\n",
    "    plt.title(\"Test and predicted data\")\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN Model (Based upon DL Primer & GMStools)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tt_vcf, ho_vcf, tt_pheno, ho_pheno = new_prep_data('Oil_Merged_filtered.csv_train_test.csv_5pcnt.csv', 'Oil_Merged_filtered.csv_holdout.csv_5pcnt.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ohe = pickle.load(open(\"Oil_ohe.dat\", \"rb\"))\n",
    "tt_vcf = ohe.transform(tt_vcf)\n",
    "print(tt_vcf.shape)\n",
    "print(ho_vcf.shape)\n",
    "ho_vcf = ohe.transform(ho_vcf)\n",
    "print(ho_vcf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_CNN_model(train_size):    \n",
    "    #del model\n",
    "    model = Sequential()\n",
    "    model.add(Conv1D(filters=12, kernel_size=14, \n",
    "                     input_shape=(train_size, 1)))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.2))\n",
    "    model.add(Conv1D(filters=10, kernel_size=10, \n",
    "                     input_shape=(12, 1)))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.1))\n",
    "    model.add(Conv1D(filters=8, kernel_size=8, \n",
    "                     input_shape=(10, 1)))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(MaxPooling1D(pool_size=2))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(48, activation='relu'))\n",
    "    model.add(Dense(32, activation='relu'))\n",
    "    model.add(Dense(16, activation='relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dense(1, activation='linear'))\n",
    "    opt = tf.keras.optimizers.Adamax(learning_rate=0.003)#, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name=\"Adamax\"\n",
    "\n",
    "\n",
    "    model.compile(loss='mean_squared_error', optimizer=opt, \n",
    "                  metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
    "\n",
    "    print(model.summary())\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_CNN(x, y, k):\n",
    "    #model: xgboost model, should be with the best params available\n",
    "    #x: input data (eg. all samples and SNPS)\n",
    "    #y: labels\n",
    "    #k: number of folds for cross validation\n",
    "    cv = KFold(n_splits=k,shuffle=True)\n",
    "  #  fig1 = plt.figure(figsize=[12,12])\n",
    "\n",
    "   # tprs = []\n",
    "   # aucs = []\n",
    "    results = []\n",
    "   # mean_fpr = np.linspace(0,1,100)\n",
    "    low = 100\n",
    "    best = []\n",
    "    i = 1\n",
    "    for train,test in cv.split(x,y):\n",
    "        m = build_CNN_model(x.shape[1])\n",
    "        print(\"Fold:\" + str(i))\n",
    "        x = x.reshape(x.shape[0], x.shape[1], 1)\n",
    "        bs = ((x[train].shape[0])/50)\n",
    "        bs = round(bs)\n",
    "        m.fit(x[train],y[train].ravel(), validation_data=(x[test], y[test]), epochs=50, batch_size=bs)\n",
    "        print(\"fitting done. Processing fold accuracy + checking best model\")\n",
    "        #predictions = [round(value) for value in y_pred]\n",
    "        #sees how accurate the model was when testing the test set\n",
    "        all_preds = [x for x in m.predict(x[test])]\n",
    "        ss = sqrt(mean_squared_error(all_preds, y[test]))\n",
    "        rr = r2_score(all_preds, y[test])\n",
    "        mm = np.mean(y[test])\n",
    "        error_mean = ((ss/mm)*100)\n",
    "        print(\"R^2 Value is: \" + str(rr))\n",
    "        print(\"RMSE for dataset is:\" +str(ss) + \"& mean of this fold is \" + str(mm))\n",
    "        print(\"this is \"+ str((ss/mm)*100) + \"% of the mean pheno data\")\n",
    "        if(error_mean < low):\n",
    "            low = error_mean\n",
    "            best = m\n",
    "        results.append(error_mean)\n",
    "        i= i+1\n",
    "        del m\n",
    "    print(\"Training Testing Accuracy: %.2f%% (%.2f%%)\" % (np.mean(results), np.std(results)))\n",
    "    return best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#my_model = build_CNN_model()\n",
    "best_model = eval_CNN(tt_vcf, tt_pheno, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(best_model, open(\"Oil_kfold_10_CNN.pickle.dat\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_CNN = pickle.load(open(\"Oil_kfold_10_CNN.pickle.dat\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ho_vcf = ho_vcf.reshape(ho_vcf.shape[0], ho_vcf.shape[1], 1)\n",
    "all_preds = [x for x in best_model.predict(ho_vcf)]\n",
    "ss = sqrt(mean_squared_error(all_preds, ho_pheno))\n",
    "rr = r2_score(all_preds, ho_pheno)\n",
    "mm = np.mean(ho_pheno)\n",
    "error_mean = ((ss/mm)*100)\n",
    "print(\"R^2 Value of Holdout: %.2f\" % rr)\n",
    "print(\"RMSE of Holdout: %.2f\" % ss)\n",
    "print(\"Mean of Holdout: %.2f\" % mm)\n",
    "print(\"this is \"+ str((ss/mm)*100) + \"% of the mean pheno data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_x, plot_y = list(), list()\n",
    "\n",
    "ho_pheno = ho_pheno.ravel()\n",
    "print(np.isinf(ho_pheno).any())\n",
    "print(np.isinf(all_preds).any())\n",
    "for counter, i in enumerate(ho_pheno):\n",
    "    if counter <= 5:\n",
    "        print(counter, i, all_preds[counter])\n",
    "    #zoom in a bit closer\n",
    "    if(all_preds[counter] > 9):\n",
    "        plot_x.append(i)\n",
    "        plot_y.append(all_preds[counter])\n",
    "    \n",
    "#plt.plot(plot_x, plot_y, '.')\n",
    "thisplot = pd.DataFrame({'Oil':plot_x, 'preds':plot_y})\n",
    "#sns.jointplot(x=\"Oil\", y=\"preds\", data=thisplot, kind='reg' , joint_kws={'scatter_kws': {'alpha': 0.2}})\n",
    "sns.regplot(x=\"Oil\", y=\"preds\", data=thisplot)\n",
    "plt.xlabel('Real Seed Oil %')\n",
    "plt.ylabel('Predicted Seed Oil %')\n",
    "plt.title(\"XGBoost Seed Oil Prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "figure(num=None, figsize=(12, 10), dpi=100, facecolor='w', edgecolor='k')\n",
    "x_ax = range(len(ho_pheno))\n",
    "print(x_ax)\n",
    "plt.plot(x_ax, ho_pheno, label=\"Actual %\")\n",
    "plt.plot(x_ax, all_preds, label=\"Predicted %\") \n",
    "\n",
    "plt.title(\"Seed Oil % Predicted vs Actual\")\n",
    "plt.xlabel('Validation Sample')\n",
    "plt.ylabel('Seed Oil Percentage')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history['root_mean_squared_error'])\n",
    "plt.plot(history.history['val_root_mean_squared_error'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_preds = [x[0] for x in model.predict(X_test)]\n",
    "sqrt(mean_squared_error(all_preds, y_test))\n",
    "r2_score(all_preds, y_test)\n",
    "vcf = vcf.reshape(vcf.shape[0], vcf.shape[1], 1)\n",
    "d = {'targs': [x for x in pheno], 'preds': [x[0] for x in model.predict(vcf)]}\n",
    "\n",
    "testdf = pd.DataFrame(data=d)\n",
    "testdf['minus'] = testdf['targs'] - testdf['preds']\n",
    "\n",
    "ss = sqrt(mean_squared_error(testdf[\"preds\"], testdf['targs']))\n",
    "mm = np.mean(pheno)\n",
    "print(\"RMSE for dataset is:\" +str(ss) + \"& pheno is \" + str(mm))\n",
    "print(\"this is \"+ str((ss/mm)*100) + \"% of the mean pheno data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DNN model\n",
    "#### Model Based upon: Crop Yield Prediction Using Deep Neural Networks(2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "20000\n",
      "30000\n",
      "40000\n",
      "50000\n",
      "60000\n",
      "70000\n",
      "80000\n",
      "90000\n",
      "100000\n",
      "110000\n",
      "120000\n",
      "130000\n",
      "140000\n",
      "150000\n",
      "160000\n",
      "170000\n",
      "180000\n",
      "190000\n",
      "200000\n",
      "210000\n",
      "(753,)\n",
      "(753, 1)\n",
      "220000\n",
      "10000\n",
      "20000\n",
      "30000\n",
      "40000\n",
      "50000\n",
      "60000\n",
      "70000\n",
      "80000\n",
      "90000\n",
      "100000\n",
      "110000\n",
      "120000\n",
      "130000\n",
      "140000\n",
      "150000\n",
      "160000\n",
      "170000\n",
      "180000\n",
      "190000\n",
      "200000\n",
      "210000\n",
      "(189,)\n",
      "(189, 1)\n",
      "220000\n",
      "(753, 213713)\n",
      "(189, 213713)\n"
     ]
    }
   ],
   "source": [
    "tt_vcf, ho_vcf, tt_pheno, ho_pheno = new_prep_data('Oil_Merged_filtered.csv_train_test.csv_5pcnt.csv', 'Oil_Merged_filtered.csv_holdout.csv_5pcnt.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(753, 620940)\n",
      "(189, 213713)\n",
      "(189, 620940)\n"
     ]
    }
   ],
   "source": [
    "ohe = pickle.load(open(\"Oil_ohe.dat\", \"rb\"))\n",
    "tt_vcf = ohe.transform(tt_vcf)\n",
    "print(tt_vcf.shape)\n",
    "print(ho_vcf.shape)\n",
    "ho_vcf = ohe.transform(ho_vcf)\n",
    "print(ho_vcf.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#My own DNN model based upon paper\n",
    "#del model #incase its stored a previous model\n",
    "#del history #for redoing shit\n",
    "\n",
    "#do batch size as 64\n",
    "#reduce the inputs by half when you read it in\n",
    "#add XGboost and RF to the one notebook\n",
    "def build_DNN_model(x_len):\n",
    "    model = Sequential()\n",
    "\n",
    "    #add first input layer, with no normalization\n",
    "    model.add(Dense(200, input_dim = x_len))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.03))\n",
    "    \n",
    "    model.add(Dense(100))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.02))\n",
    "    \n",
    "    model.add(Dense(64))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(Dropout(0.01))\n",
    "    \n",
    "    model.add(Dense(32))\n",
    "    model.add(Activation('relu'))\n",
    "    model.add(BatchNormalization())\n",
    "    \n",
    "    model.add(Dense(16))\n",
    "    model.add(Activation('relu'))\n",
    "    #add output layer\n",
    "    model.add(Dense(1, activation='linear'))\n",
    "    opt = tf.keras.optimizers.Adamax(learning_rate=0.003)\n",
    "    model.compile(loss=\"mean_squared_error\", optimizer=opt, metrics=[tf.keras.metrics.RootMeanSquaredError()])\n",
    "    print(model.summary())\n",
    "    return model\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_DNN(x, y, k):\n",
    "    #model: xgboost model, should be with the best params available\n",
    "    #x: input data (eg. all samples and SNPS)\n",
    "    #y: labels\n",
    "    #k: number of folds for cross validation\n",
    "    cv = KFold(n_splits=k,shuffle=True)\n",
    "  #  fig1 = plt.figure(figsize=[12,12])\n",
    "\n",
    "   # tprs = []\n",
    "   # aucs = []\n",
    "    results = []\n",
    "   # mean_fpr = np.linspace(0,1,100)\n",
    "    low = 100\n",
    "    best = []\n",
    "    i = 1\n",
    "    for train,test in cv.split(x,y):\n",
    "        m = build_DNN_model(x.shape[1])\n",
    "        print(\"Fold:\" + str(i))\n",
    "        bs = ((x[train].shape[0])/50)\n",
    "        bs = round(bs)\n",
    "        m.fit(x[train],y[train].ravel(), validation_data=(x[test], y[test]), epochs=50, batch_size=bs)\n",
    "        print(\"fitting done. Processing fold accuracy + checking best model\")\n",
    "        #predictions = [round(value) for value in y_pred]\n",
    "        #sees how accurate the model was when testing the test set\n",
    "        all_preds = [x for x in m.predict(x[test])]\n",
    "        ss = sqrt(mean_squared_error(all_preds, y[test]))\n",
    "        rr = r2_score(all_preds, y[test])\n",
    "        mm = np.mean(y[test])\n",
    "        error_mean = ((ss/mm)*100)\n",
    "        print(\"R^2 Value is: \" + str(rr))\n",
    "        print(\"RMSE for dataset is:\" +str(ss) + \"& mean of this fold is \" + str(mm))\n",
    "        print(\"this is \"+ str((ss/mm)*100) + \"% of the mean pheno data\")\n",
    "        if(error_mean < low):\n",
    "            low = error_mean\n",
    "            best = m\n",
    "        results.append(error_mean)\n",
    "        i= i+1\n",
    "        del m\n",
    "    print(\"Training Testing Accuracy: %.2f%% (%.2f%%)\" % (np.mean(results), np.std(results)))\n",
    "    return best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation (Activation)      (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:1\n",
      "Train on 677 samples, validate on 76 samples\n",
      "Epoch 1/50\n",
      "677/677 [==============================] - 30s 44ms/sample - loss: 200.5510 - root_mean_squared_error: 14.1616 - val_loss: 1666.6725 - val_root_mean_squared_error: 40.8249\n",
      "Epoch 2/50\n",
      "677/677 [==============================] - 3s 5ms/sample - loss: 64.9548 - root_mean_squared_error: 8.0595 - val_loss: 577.2869 - val_root_mean_squared_error: 24.0268\n",
      "Epoch 3/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 17.4825 - root_mean_squared_error: 4.1812 - val_loss: 85.3175 - val_root_mean_squared_error: 9.2367\n",
      "Epoch 4/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 13.8731 - root_mean_squared_error: 3.7247 - val_loss: 100.3326 - val_root_mean_squared_error: 10.0166\n",
      "Epoch 5/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 11.2118 - root_mean_squared_error: 3.3484 - val_loss: 57.0371 - val_root_mean_squared_error: 7.5523\n",
      "Epoch 6/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 8.0828 - root_mean_squared_error: 2.8430 - val_loss: 11.2314 - val_root_mean_squared_error: 3.3513\n",
      "Epoch 7/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 8.5831 - root_mean_squared_error: 2.9297 - val_loss: 17.9989 - val_root_mean_squared_error: 4.2425\n",
      "Epoch 8/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 7.9408 - root_mean_squared_error: 2.8179 - val_loss: 5.7900 - val_root_mean_squared_error: 2.4062\n",
      "Epoch 9/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 6.7025 - root_mean_squared_error: 2.5889 - val_loss: 4.9529 - val_root_mean_squared_error: 2.2255\n",
      "Epoch 10/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 5.0193 - root_mean_squared_error: 2.2404 - val_loss: 4.1025 - val_root_mean_squared_error: 2.0255\n",
      "Epoch 11/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 5.2132 - root_mean_squared_error: 2.2832 - val_loss: 4.7607 - val_root_mean_squared_error: 2.1819\n",
      "Epoch 12/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 5.0992 - root_mean_squared_error: 2.2581 - val_loss: 3.3777 - val_root_mean_squared_error: 1.8379\n",
      "Epoch 13/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 5.6106 - root_mean_squared_error: 2.3687 - val_loss: 3.2966 - val_root_mean_squared_error: 1.8157\n",
      "Epoch 14/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 4.8275 - root_mean_squared_error: 2.1972 - val_loss: 2.9572 - val_root_mean_squared_error: 1.7197\n",
      "Epoch 15/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 4.2379 - root_mean_squared_error: 2.0586 - val_loss: 3.0148 - val_root_mean_squared_error: 1.7363\n",
      "Epoch 16/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 4.4869 - root_mean_squared_error: 2.1182 - val_loss: 3.9819 - val_root_mean_squared_error: 1.9955\n",
      "Epoch 17/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 3.9715 - root_mean_squared_error: 1.9929 - val_loss: 2.7774 - val_root_mean_squared_error: 1.6666\n",
      "Epoch 18/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.6454 - root_mean_squared_error: 1.9093 - val_loss: 2.9658 - val_root_mean_squared_error: 1.7222\n",
      "Epoch 19/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.7380 - root_mean_squared_error: 1.9334 - val_loss: 2.6622 - val_root_mean_squared_error: 1.6316\n",
      "Epoch 20/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.7939 - root_mean_squared_error: 1.9478 - val_loss: 3.1155 - val_root_mean_squared_error: 1.7651\n",
      "Epoch 21/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.9113 - root_mean_squared_error: 1.9777 - val_loss: 2.3346 - val_root_mean_squared_error: 1.5279\n",
      "Epoch 22/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.7603 - root_mean_squared_error: 1.9392 - val_loss: 2.7929 - val_root_mean_squared_error: 1.6712\n",
      "Epoch 23/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.2891 - root_mean_squared_error: 1.8136 - val_loss: 2.4704 - val_root_mean_squared_error: 1.5718\n",
      "Epoch 24/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 3.3214 - root_mean_squared_error: 1.8225 - val_loss: 2.6452 - val_root_mean_squared_error: 1.6264\n",
      "Epoch 25/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.5529 - root_mean_squared_error: 1.8849 - val_loss: 4.6823 - val_root_mean_squared_error: 2.1639\n",
      "Epoch 26/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 3.5037 - root_mean_squared_error: 1.8718 - val_loss: 2.5530 - val_root_mean_squared_error: 1.5978\n",
      "Epoch 27/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 3.2545 - root_mean_squared_error: 1.8040 - val_loss: 2.5089 - val_root_mean_squared_error: 1.5840\n",
      "Epoch 28/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.9195 - root_mean_squared_error: 1.7086 - val_loss: 2.1607 - val_root_mean_squared_error: 1.4699\n",
      "Epoch 29/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.2725 - root_mean_squared_error: 1.8090 - val_loss: 2.3479 - val_root_mean_squared_error: 1.5323\n",
      "Epoch 30/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.8968 - root_mean_squared_error: 1.7020 - val_loss: 2.2063 - val_root_mean_squared_error: 1.4854\n",
      "Epoch 31/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.9247 - root_mean_squared_error: 1.7102 - val_loss: 2.5318 - val_root_mean_squared_error: 1.5912\n",
      "Epoch 32/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.3147 - root_mean_squared_error: 1.8206 - val_loss: 2.2586 - val_root_mean_squared_error: 1.5029\n",
      "Epoch 33/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.8209 - root_mean_squared_error: 1.6795 - val_loss: 2.1524 - val_root_mean_squared_error: 1.4671\n",
      "Epoch 34/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.7740 - root_mean_squared_error: 1.6655 - val_loss: 2.2778 - val_root_mean_squared_error: 1.5092\n",
      "Epoch 35/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.6155 - root_mean_squared_error: 1.6173 - val_loss: 2.2834 - val_root_mean_squared_error: 1.5111\n",
      "Epoch 36/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.6853 - root_mean_squared_error: 1.6387 - val_loss: 2.0968 - val_root_mean_squared_error: 1.4480\n",
      "Epoch 37/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.7986 - root_mean_squared_error: 1.6729 - val_loss: 2.5167 - val_root_mean_squared_error: 1.5864\n",
      "Epoch 38/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.4670 - root_mean_squared_error: 1.5707 - val_loss: 2.3658 - val_root_mean_squared_error: 1.5381\n",
      "Epoch 39/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.4937 - root_mean_squared_error: 1.5791 - val_loss: 2.4488 - val_root_mean_squared_error: 1.5649\n",
      "Epoch 40/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.6834 - root_mean_squared_error: 1.6381 - val_loss: 2.1786 - val_root_mean_squared_error: 1.4760\n",
      "Epoch 41/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 2.3433 - root_mean_squared_error: 1.5308 - val_loss: 5.0884 - val_root_mean_squared_error: 2.2558\n",
      "Epoch 42/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 2.6357 - root_mean_squared_error: 1.6235 - val_loss: 2.2431 - val_root_mean_squared_error: 1.4977\n",
      "Epoch 43/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.8002 - root_mean_squared_error: 1.6734 - val_loss: 2.3810 - val_root_mean_squared_error: 1.5431\n",
      "Epoch 44/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.2717 - root_mean_squared_error: 1.5072 - val_loss: 2.1814 - val_root_mean_squared_error: 1.4770\n",
      "Epoch 45/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.1735 - root_mean_squared_error: 1.4743 - val_loss: 2.2225 - val_root_mean_squared_error: 1.4908\n",
      "Epoch 46/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.2946 - root_mean_squared_error: 1.5148 - val_loss: 2.0879 - val_root_mean_squared_error: 1.4450\n",
      "Epoch 47/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.8942 - root_mean_squared_error: 1.7012 - val_loss: 2.7766 - val_root_mean_squared_error: 1.6663\n",
      "Epoch 48/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.1019 - root_mean_squared_error: 1.4498 - val_loss: 2.5261 - val_root_mean_squared_error: 1.5894\n",
      "Epoch 49/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.0980 - root_mean_squared_error: 1.4484 - val_loss: 2.3704 - val_root_mean_squared_error: 1.5396\n",
      "Epoch 50/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.8830 - root_mean_squared_error: 1.3722 - val_loss: 2.2497 - val_root_mean_squared_error: 1.4999\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.7111720459789871\n",
      "RMSE for dataset is:1.4998882569970948& mean of this fold is 17.680264\n",
      "this is 8.48340442075929% of the mean pheno data\n",
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_6 (Dense)              (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:2\n",
      "Train on 677 samples, validate on 76 samples\n",
      "Epoch 1/50\n",
      "677/677 [==============================] - 21s 31ms/sample - loss: 282.4758 - root_mean_squared_error: 16.8070 - val_loss: 43.7289 - val_root_mean_squared_error: 6.6128\n",
      "Epoch 2/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 162.1212 - root_mean_squared_error: 12.7327 - val_loss: 48.2285 - val_root_mean_squared_error: 6.9447\n",
      "Epoch 3/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 52.0639 - root_mean_squared_error: 7.2155 - val_loss: 125.2580 - val_root_mean_squared_error: 11.1919\n",
      "Epoch 4/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 19.6915 - root_mean_squared_error: 4.4375 - val_loss: 78.8524 - val_root_mean_squared_error: 8.8799\n",
      "Epoch 5/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 13.8347 - root_mean_squared_error: 3.7195 - val_loss: 28.6050 - val_root_mean_squared_error: 5.3484\n",
      "Epoch 6/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 13.5013 - root_mean_squared_error: 3.6744 - val_loss: 16.8827 - val_root_mean_squared_error: 4.1089\n",
      "Epoch 7/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 8.6505 - root_mean_squared_error: 2.9412 - val_loss: 5.7549 - val_root_mean_squared_error: 2.3989\n",
      "Epoch 8/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 8.2863 - root_mean_squared_error: 2.8786 - val_loss: 5.9413 - val_root_mean_squared_error: 2.4375\n",
      "Epoch 9/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 7.4973 - root_mean_squared_error: 2.7381 - val_loss: 5.2945 - val_root_mean_squared_error: 2.3010\n",
      "Epoch 10/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 6.1403 - root_mean_squared_error: 2.4780 - val_loss: 4.6268 - val_root_mean_squared_error: 2.1510\n",
      "Epoch 11/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 5.6859 - root_mean_squared_error: 2.3845 - val_loss: 4.1936 - val_root_mean_squared_error: 2.0478\n",
      "Epoch 12/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 5.4585 - root_mean_squared_error: 2.3364 - val_loss: 4.2749 - val_root_mean_squared_error: 2.0676\n",
      "Epoch 13/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 5.2009 - root_mean_squared_error: 2.2806 - val_loss: 4.1891 - val_root_mean_squared_error: 2.0467\n",
      "Epoch 14/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 4.5008 - root_mean_squared_error: 2.1215 - val_loss: 3.1152 - val_root_mean_squared_error: 1.7650\n",
      "Epoch 15/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 4.6943 - root_mean_squared_error: 2.1666 - val_loss: 4.2228 - val_root_mean_squared_error: 2.0549\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 16/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 4.0312 - root_mean_squared_error: 2.0078 - val_loss: 3.9718 - val_root_mean_squared_error: 1.9929\n",
      "Epoch 17/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 4.1342 - root_mean_squared_error: 2.0333 - val_loss: 3.9362 - val_root_mean_squared_error: 1.9840\n",
      "Epoch 18/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 4.1380 - root_mean_squared_error: 2.0342 - val_loss: 4.8901 - val_root_mean_squared_error: 2.2113\n",
      "Epoch 19/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.6570 - root_mean_squared_error: 1.9123 - val_loss: 3.4819 - val_root_mean_squared_error: 1.8660\n",
      "Epoch 20/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.7674 - root_mean_squared_error: 1.9410 - val_loss: 3.4550 - val_root_mean_squared_error: 1.8588\n",
      "Epoch 21/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.5733 - root_mean_squared_error: 1.8903 - val_loss: 4.2281 - val_root_mean_squared_error: 2.0562\n",
      "Epoch 22/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.1881 - root_mean_squared_error: 1.7855 - val_loss: 3.8557 - val_root_mean_squared_error: 1.9636\n",
      "Epoch 23/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.2572 - root_mean_squared_error: 1.8048 - val_loss: 3.2741 - val_root_mean_squared_error: 1.8095\n",
      "Epoch 24/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.5538 - root_mean_squared_error: 1.5980 - val_loss: 3.1225 - val_root_mean_squared_error: 1.7670\n",
      "Epoch 25/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.0883 - root_mean_squared_error: 1.7574 - val_loss: 2.7621 - val_root_mean_squared_error: 1.6620\n",
      "Epoch 26/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.2760 - root_mean_squared_error: 1.5086 - val_loss: 2.7191 - val_root_mean_squared_error: 1.6490\n",
      "Epoch 27/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.4464 - root_mean_squared_error: 1.5641 - val_loss: 3.5427 - val_root_mean_squared_error: 1.8822\n",
      "Epoch 28/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.5266 - root_mean_squared_error: 1.5895 - val_loss: 2.5476 - val_root_mean_squared_error: 1.5961\n",
      "Epoch 29/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.7460 - root_mean_squared_error: 1.6571 - val_loss: 2.9900 - val_root_mean_squared_error: 1.7292\n",
      "Epoch 30/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.4978 - root_mean_squared_error: 1.5804 - val_loss: 3.0369 - val_root_mean_squared_error: 1.7427\n",
      "Epoch 31/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.2546 - root_mean_squared_error: 1.5015 - val_loss: 2.9446 - val_root_mean_squared_error: 1.7160\n",
      "Epoch 32/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.1974 - root_mean_squared_error: 1.4824 - val_loss: 5.5627 - val_root_mean_squared_error: 2.3585\n",
      "Epoch 33/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.2399 - root_mean_squared_error: 1.4966 - val_loss: 2.3694 - val_root_mean_squared_error: 1.5393\n",
      "Epoch 34/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.8235 - root_mean_squared_error: 1.3504 - val_loss: 3.7571 - val_root_mean_squared_error: 1.9383\n",
      "Epoch 35/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.3280 - root_mean_squared_error: 1.5258 - val_loss: 2.9898 - val_root_mean_squared_error: 1.7291\n",
      "Epoch 36/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.0593 - root_mean_squared_error: 1.4350 - val_loss: 2.6421 - val_root_mean_squared_error: 1.6254\n",
      "Epoch 37/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.9570 - root_mean_squared_error: 1.3989 - val_loss: 2.5221 - val_root_mean_squared_error: 1.5881\n",
      "Epoch 38/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.7998 - root_mean_squared_error: 1.3416 - val_loss: 2.4595 - val_root_mean_squared_error: 1.5683\n",
      "Epoch 39/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.9943 - root_mean_squared_error: 1.4122 - val_loss: 2.5016 - val_root_mean_squared_error: 1.5817\n",
      "Epoch 40/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.6796 - root_mean_squared_error: 1.2960 - val_loss: 2.8691 - val_root_mean_squared_error: 1.6938\n",
      "Epoch 41/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.9042 - root_mean_squared_error: 1.3799 - val_loss: 2.6269 - val_root_mean_squared_error: 1.6208\n",
      "Epoch 42/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.3921 - root_mean_squared_error: 1.1799 - val_loss: 2.9318 - val_root_mean_squared_error: 1.7123\n",
      "Epoch 43/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.3425 - root_mean_squared_error: 1.1587 - val_loss: 3.5557 - val_root_mean_squared_error: 1.8857\n",
      "Epoch 44/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.3146 - root_mean_squared_error: 1.1466 - val_loss: 2.2876 - val_root_mean_squared_error: 1.5125\n",
      "Epoch 45/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.4176 - root_mean_squared_error: 1.1906 - val_loss: 2.6718 - val_root_mean_squared_error: 1.6346\n",
      "Epoch 46/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.4298 - root_mean_squared_error: 1.1957 - val_loss: 2.7500 - val_root_mean_squared_error: 1.6583\n",
      "Epoch 47/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.5370 - root_mean_squared_error: 1.2398 - val_loss: 2.3913 - val_root_mean_squared_error: 1.5464\n",
      "Epoch 48/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.1659 - root_mean_squared_error: 1.0797 - val_loss: 2.6289 - val_root_mean_squared_error: 1.6214\n",
      "Epoch 49/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.3575 - root_mean_squared_error: 1.1651 - val_loss: 3.6619 - val_root_mean_squared_error: 1.9136\n",
      "Epoch 50/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.3450 - root_mean_squared_error: 1.1598 - val_loss: 2.7587 - val_root_mean_squared_error: 1.6609\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.7128065966093098\n",
      "RMSE for dataset is:1.6609416225952776& mean of this fold is 17.732895\n",
      "this is 9.366443731830259% of the mean pheno data\n",
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_12 (Dense)             (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_16 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 677 samples, validate on 76 samples\n",
      "Epoch 1/50\n",
      "677/677 [==============================] - 18s 27ms/sample - loss: 242.7709 - root_mean_squared_error: 15.5811 - val_loss: 28.1638 - val_root_mean_squared_error: 5.3070\n",
      "Epoch 2/50\n",
      "677/677 [==============================] - 4s 6ms/sample - loss: 98.3711 - root_mean_squared_error: 9.9182 - val_loss: 217.4819 - val_root_mean_squared_error: 14.7473\n",
      "Epoch 3/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 21.7095 - root_mean_squared_error: 4.6593 - val_loss: 103.8996 - val_root_mean_squared_error: 10.1931\n",
      "Epoch 4/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 12.5126 - root_mean_squared_error: 3.5373 - val_loss: 30.1841 - val_root_mean_squared_error: 5.4940\n",
      "Epoch 5/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 11.8035 - root_mean_squared_error: 3.4356 - val_loss: 11.5277 - val_root_mean_squared_error: 3.3952\n",
      "Epoch 6/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 9.4063 - root_mean_squared_error: 3.0670 - val_loss: 6.8941 - val_root_mean_squared_error: 2.6257\n",
      "Epoch 7/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 9.7162 - root_mean_squared_error: 3.1171 - val_loss: 6.1643 - val_root_mean_squared_error: 2.4828\n",
      "Epoch 8/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 6.8454 - root_mean_squared_error: 2.6164 - val_loss: 6.2805 - val_root_mean_squared_error: 2.5061\n",
      "Epoch 9/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 7.2211 - root_mean_squared_error: 2.6872 - val_loss: 6.1688 - val_root_mean_squared_error: 2.4837\n",
      "Epoch 10/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 6.3389 - root_mean_squared_error: 2.5177 - val_loss: 4.9645 - val_root_mean_squared_error: 2.2281\n",
      "Epoch 11/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 6.5389 - root_mean_squared_error: 2.5571 - val_loss: 5.8476 - val_root_mean_squared_error: 2.4182\n",
      "Epoch 12/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 6.3150 - root_mean_squared_error: 2.5130 - val_loss: 5.3083 - val_root_mean_squared_error: 2.3040\n",
      "Epoch 13/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 5.3212 - root_mean_squared_error: 2.3068 - val_loss: 5.9387 - val_root_mean_squared_error: 2.4369\n",
      "Epoch 14/50\n",
      "677/677 [==============================] - 3s 5ms/sample - loss: 5.8203 - root_mean_squared_error: 2.4125 - val_loss: 6.6344 - val_root_mean_squared_error: 2.5757\n",
      "Epoch 15/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 5.0517 - root_mean_squared_error: 2.2476 - val_loss: 5.3776 - val_root_mean_squared_error: 2.3190\n",
      "Epoch 16/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 6.5351 - root_mean_squared_error: 2.5564 - val_loss: 3.0515 - val_root_mean_squared_error: 1.7468\n",
      "Epoch 17/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 5.1992 - root_mean_squared_error: 2.2802 - val_loss: 2.9740 - val_root_mean_squared_error: 1.7245\n",
      "Epoch 18/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 4.1270 - root_mean_squared_error: 2.0315 - val_loss: 3.4928 - val_root_mean_squared_error: 1.8689\n",
      "Epoch 19/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 4.0829 - root_mean_squared_error: 2.0206 - val_loss: 3.4889 - val_root_mean_squared_error: 1.8679\n",
      "Epoch 20/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.7300 - root_mean_squared_error: 1.9313 - val_loss: 3.1758 - val_root_mean_squared_error: 1.7821\n",
      "Epoch 21/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.3002 - root_mean_squared_error: 1.8166 - val_loss: 3.2574 - val_root_mean_squared_error: 1.8048\n",
      "Epoch 22/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.9416 - root_mean_squared_error: 1.9853 - val_loss: 4.2417 - val_root_mean_squared_error: 2.0595\n",
      "Epoch 23/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.9771 - root_mean_squared_error: 1.9943 - val_loss: 4.7889 - val_root_mean_squared_error: 2.1884\n",
      "Epoch 24/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.8344 - root_mean_squared_error: 1.9582 - val_loss: 5.9690 - val_root_mean_squared_error: 2.4432\n",
      "Epoch 25/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 3.6974 - root_mean_squared_error: 1.9229 - val_loss: 4.2175 - val_root_mean_squared_error: 2.0537\n",
      "Epoch 26/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.6425 - root_mean_squared_error: 1.9085 - val_loss: 3.5817 - val_root_mean_squared_error: 1.8925\n",
      "Epoch 27/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.2780 - root_mean_squared_error: 1.8105 - val_loss: 3.1904 - val_root_mean_squared_error: 1.7862\n",
      "Epoch 28/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 2.9720 - root_mean_squared_error: 1.7240 - val_loss: 4.0604 - val_root_mean_squared_error: 2.0150\n",
      "Epoch 29/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.7355 - root_mean_squared_error: 1.6539 - val_loss: 3.4751 - val_root_mean_squared_error: 1.8642\n",
      "Epoch 30/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 2.6015 - root_mean_squared_error: 1.6129 - val_loss: 3.6566 - val_root_mean_squared_error: 1.9122\n",
      "Epoch 31/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 2.5251 - root_mean_squared_error: 1.5890 - val_loss: 2.9539 - val_root_mean_squared_error: 1.7187\n",
      "Epoch 32/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.4676 - root_mean_squared_error: 1.8622 - val_loss: 6.1404 - val_root_mean_squared_error: 2.4780\n",
      "Epoch 33/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.9146 - root_mean_squared_error: 1.7072 - val_loss: 3.0421 - val_root_mean_squared_error: 1.7441\n",
      "Epoch 34/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.3933 - root_mean_squared_error: 1.5470 - val_loss: 3.2938 - val_root_mean_squared_error: 1.8149\n",
      "Epoch 35/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 3.1787 - root_mean_squared_error: 1.7829 - val_loss: 3.0102 - val_root_mean_squared_error: 1.7350\n",
      "Epoch 36/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.7817 - root_mean_squared_error: 1.6678 - val_loss: 3.0658 - val_root_mean_squared_error: 1.7509\n",
      "Epoch 37/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.2752 - root_mean_squared_error: 1.5084 - val_loss: 2.9188 - val_root_mean_squared_error: 1.7084\n",
      "Epoch 38/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.7466 - root_mean_squared_error: 1.6573 - val_loss: 3.5407 - val_root_mean_squared_error: 1.8817\n",
      "Epoch 39/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.4679 - root_mean_squared_error: 1.5710 - val_loss: 3.2776 - val_root_mean_squared_error: 1.8104\n",
      "Epoch 40/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 2.2742 - root_mean_squared_error: 1.5080 - val_loss: 2.9330 - val_root_mean_squared_error: 1.7126\n",
      "Epoch 41/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.4659 - root_mean_squared_error: 1.5703 - val_loss: 3.1717 - val_root_mean_squared_error: 1.7809\n",
      "Epoch 42/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.2462 - root_mean_squared_error: 1.4987 - val_loss: 3.2239 - val_root_mean_squared_error: 1.7955\n",
      "Epoch 43/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.1470 - root_mean_squared_error: 1.4653 - val_loss: 4.9195 - val_root_mean_squared_error: 2.2180\n",
      "Epoch 44/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.3420 - root_mean_squared_error: 1.5304 - val_loss: 4.1346 - val_root_mean_squared_error: 2.0334\n",
      "Epoch 45/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.2984 - root_mean_squared_error: 1.5161 - val_loss: 3.0800 - val_root_mean_squared_error: 1.7550\n",
      "Epoch 46/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.1505 - root_mean_squared_error: 1.4665 - val_loss: 2.8607 - val_root_mean_squared_error: 1.6914\n",
      "Epoch 47/50\n",
      "677/677 [==============================] - 3s 4ms/sample - loss: 2.1001 - root_mean_squared_error: 1.4492 - val_loss: 2.9717 - val_root_mean_squared_error: 1.7239\n",
      "Epoch 48/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 2.0605 - root_mean_squared_error: 1.4354 - val_loss: 2.9676 - val_root_mean_squared_error: 1.7227\n",
      "Epoch 49/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.9618 - root_mean_squared_error: 1.4007 - val_loss: 2.8558 - val_root_mean_squared_error: 1.6899\n",
      "Epoch 50/50\n",
      "677/677 [==============================] - 2s 4ms/sample - loss: 1.6750 - root_mean_squared_error: 1.2942 - val_loss: 2.9307 - val_root_mean_squared_error: 1.7119\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.769120598164419\n",
      "RMSE for dataset is:1.7119206081859315& mean of this fold is 17.598684\n",
      "this is 9.727548820932913% of the mean pheno data\n",
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_18 (Dense)             (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation_15 (Activation)   (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_16 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_17 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_11 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_21 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_18 (Activation)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_22 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_19 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_23 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:4\n",
      "Train on 678 samples, validate on 75 samples\n",
      "Epoch 1/50\n",
      "678/678 [==============================] - 19s 28ms/sample - loss: 241.9418 - root_mean_squared_error: 15.5545 - val_loss: 53.5810 - val_root_mean_squared_error: 7.3199\n",
      "Epoch 2/50\n",
      "678/678 [==============================] - 7s 10ms/sample - loss: 102.7072 - root_mean_squared_error: 10.1345 - val_loss: 56.9917 - val_root_mean_squared_error: 7.5493\n",
      "Epoch 3/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 31.6974 - root_mean_squared_error: 5.6300 - val_loss: 51.8665 - val_root_mean_squared_error: 7.2018\n",
      "Epoch 4/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 22.3251 - root_mean_squared_error: 4.7249 - val_loss: 121.6084 - val_root_mean_squared_error: 11.0276\n",
      "Epoch 5/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 15.6561 - root_mean_squared_error: 3.9568 - val_loss: 101.6338 - val_root_mean_squared_error: 10.0814\n",
      "Epoch 6/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 11.7047 - root_mean_squared_error: 3.4212 - val_loss: 70.1905 - val_root_mean_squared_error: 8.3780\n",
      "Epoch 7/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.3571 - root_mean_squared_error: 2.5213 - val_loss: 30.6013 - val_root_mean_squared_error: 5.5318\n",
      "Epoch 8/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.4883 - root_mean_squared_error: 2.5472 - val_loss: 18.0963 - val_root_mean_squared_error: 4.2540\n",
      "Epoch 9/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.1480 - root_mean_squared_error: 2.2689 - val_loss: 10.3345 - val_root_mean_squared_error: 3.2147\n",
      "Epoch 10/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.3765 - root_mean_squared_error: 2.0920 - val_loss: 4.8736 - val_root_mean_squared_error: 2.2076\n",
      "Epoch 11/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 4.6024 - root_mean_squared_error: 2.1453 - val_loss: 3.7670 - val_root_mean_squared_error: 1.9409\n",
      "Epoch 12/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 4.8852 - root_mean_squared_error: 2.2102 - val_loss: 4.3095 - val_root_mean_squared_error: 2.0759\n",
      "Epoch 13/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.1724 - root_mean_squared_error: 2.0426 - val_loss: 3.1728 - val_root_mean_squared_error: 1.7812\n",
      "Epoch 14/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.9256 - root_mean_squared_error: 1.9813 - val_loss: 3.9798 - val_root_mean_squared_error: 1.9949\n",
      "Epoch 15/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.6240 - root_mean_squared_error: 1.9037 - val_loss: 3.4369 - val_root_mean_squared_error: 1.8539\n",
      "Epoch 16/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.9252 - root_mean_squared_error: 1.9812 - val_loss: 3.6494 - val_root_mean_squared_error: 1.9103\n",
      "Epoch 17/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.8516 - root_mean_squared_error: 1.9625 - val_loss: 3.2661 - val_root_mean_squared_error: 1.8072\n",
      "Epoch 18/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.8104 - root_mean_squared_error: 1.9520 - val_loss: 3.4756 - val_root_mean_squared_error: 1.8643\n",
      "Epoch 19/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.8072 - root_mean_squared_error: 1.9512 - val_loss: 3.7237 - val_root_mean_squared_error: 1.9297\n",
      "Epoch 20/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2042 - root_mean_squared_error: 1.7900 - val_loss: 3.2114 - val_root_mean_squared_error: 1.7921\n",
      "Epoch 21/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.6119 - root_mean_squared_error: 1.9005 - val_loss: 3.3012 - val_root_mean_squared_error: 1.8169\n",
      "Epoch 22/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2404 - root_mean_squared_error: 1.8001 - val_loss: 5.0569 - val_root_mean_squared_error: 2.2488\n",
      "Epoch 23/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.4489 - root_mean_squared_error: 1.8571 - val_loss: 4.5268 - val_root_mean_squared_error: 2.1276\n",
      "Epoch 24/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.7353 - root_mean_squared_error: 1.6539 - val_loss: 3.2597 - val_root_mean_squared_error: 1.8055\n",
      "Epoch 25/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 3.2914 - root_mean_squared_error: 1.8142 - val_loss: 4.0297 - val_root_mean_squared_error: 2.0074\n",
      "Epoch 26/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8146 - root_mean_squared_error: 1.6777 - val_loss: 3.2719 - val_root_mean_squared_error: 1.8088\n",
      "Epoch 27/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.3755 - root_mean_squared_error: 1.5413 - val_loss: 3.8564 - val_root_mean_squared_error: 1.9638\n",
      "Epoch 28/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.1975 - root_mean_squared_error: 1.4824 - val_loss: 3.4317 - val_root_mean_squared_error: 1.8525\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8805 - root_mean_squared_error: 1.6972 - val_loss: 3.6245 - val_root_mean_squared_error: 1.9038\n",
      "Epoch 30/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.9736 - root_mean_squared_error: 1.7244 - val_loss: 3.8053 - val_root_mean_squared_error: 1.9507\n",
      "Epoch 31/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.4277 - root_mean_squared_error: 1.8514 - val_loss: 6.7148 - val_root_mean_squared_error: 2.5913\n",
      "Epoch 32/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.3570 - root_mean_squared_error: 1.5353 - val_loss: 3.4293 - val_root_mean_squared_error: 1.8518\n",
      "Epoch 33/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5378 - root_mean_squared_error: 1.5931 - val_loss: 3.6220 - val_root_mean_squared_error: 1.9032\n",
      "Epoch 34/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.9523 - root_mean_squared_error: 1.7182 - val_loss: 4.1777 - val_root_mean_squared_error: 2.0439\n",
      "Epoch 35/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.0057 - root_mean_squared_error: 1.4162 - val_loss: 3.3642 - val_root_mean_squared_error: 1.8342\n",
      "Epoch 36/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.6554 - root_mean_squared_error: 1.6295 - val_loss: 3.3556 - val_root_mean_squared_error: 1.8318\n",
      "Epoch 37/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.4981 - root_mean_squared_error: 1.5806 - val_loss: 4.1008 - val_root_mean_squared_error: 2.0250\n",
      "Epoch 38/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.1333 - root_mean_squared_error: 1.4606 - val_loss: 3.6209 - val_root_mean_squared_error: 1.9029\n",
      "Epoch 39/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7850 - root_mean_squared_error: 1.3360 - val_loss: 4.7901 - val_root_mean_squared_error: 2.1886\n",
      "Epoch 40/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.5100 - root_mean_squared_error: 1.8735 - val_loss: 11.7191 - val_root_mean_squared_error: 3.4233\n",
      "Epoch 41/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.6982 - root_mean_squared_error: 1.9231 - val_loss: 3.2997 - val_root_mean_squared_error: 1.8165\n",
      "Epoch 42/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8785 - root_mean_squared_error: 1.6966 - val_loss: 4.4259 - val_root_mean_squared_error: 2.1038\n",
      "Epoch 43/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.0605 - root_mean_squared_error: 1.7494 - val_loss: 4.8609 - val_root_mean_squared_error: 2.2047\n",
      "Epoch 44/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8975 - root_mean_squared_error: 1.7022 - val_loss: 3.2125 - val_root_mean_squared_error: 1.7924\n",
      "Epoch 45/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.4370 - root_mean_squared_error: 1.8539 - val_loss: 4.0082 - val_root_mean_squared_error: 2.0021\n",
      "Epoch 46/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.4767 - root_mean_squared_error: 1.5738 - val_loss: 2.9891 - val_root_mean_squared_error: 1.7289\n",
      "Epoch 47/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.3898 - root_mean_squared_error: 1.5459 - val_loss: 3.3058 - val_root_mean_squared_error: 1.8182\n",
      "Epoch 48/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7825 - root_mean_squared_error: 1.3351 - val_loss: 3.3272 - val_root_mean_squared_error: 1.8241\n",
      "Epoch 49/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7030 - root_mean_squared_error: 1.3050 - val_loss: 3.1327 - val_root_mean_squared_error: 1.7699\n",
      "Epoch 50/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.9365 - root_mean_squared_error: 1.3916 - val_loss: 4.0571 - val_root_mean_squared_error: 2.0142\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.6057508947945877\n",
      "RMSE for dataset is:2.0142334858810513& mean of this fold is 17.310667\n",
      "this is 11.635793591683406% of the mean pheno data\n",
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_24 (Dense)             (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation_20 (Activation)   (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_12 (Dropout)         (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_25 (Dense)             (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_21 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_22 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_14 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_27 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_23 (Activation)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_28 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_24 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_29 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:5\n",
      "Train on 678 samples, validate on 75 samples\n",
      "Epoch 1/50\n",
      "678/678 [==============================] - 21s 31ms/sample - loss: 255.3719 - root_mean_squared_error: 15.9804 - val_loss: 63.7556 - val_root_mean_squared_error: 7.9847\n",
      "Epoch 2/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 118.3255 - root_mean_squared_error: 10.8778 - val_loss: 31.7598 - val_root_mean_squared_error: 5.6356\n",
      "Epoch 3/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 32.0584 - root_mean_squared_error: 5.6620 - val_loss: 121.3862 - val_root_mean_squared_error: 11.0175\n",
      "Epoch 4/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 16.2587 - root_mean_squared_error: 4.0322 - val_loss: 97.1449 - val_root_mean_squared_error: 9.8562\n",
      "Epoch 5/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 12.0012 - root_mean_squared_error: 3.4643 - val_loss: 10.1637 - val_root_mean_squared_error: 3.1881\n",
      "Epoch 6/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 11.4152 - root_mean_squared_error: 3.3786 - val_loss: 10.5808 - val_root_mean_squared_error: 3.2528\n",
      "Epoch 7/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 11.8586 - root_mean_squared_error: 3.4436 - val_loss: 9.0515 - val_root_mean_squared_error: 3.0086\n",
      "Epoch 8/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 8.8237 - root_mean_squared_error: 2.9705 - val_loss: 6.0222 - val_root_mean_squared_error: 2.4540\n",
      "Epoch 9/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 7.9018 - root_mean_squared_error: 2.8110 - val_loss: 3.0929 - val_root_mean_squared_error: 1.7587\n",
      "Epoch 10/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.2562 - root_mean_squared_error: 2.5012 - val_loss: 4.4862 - val_root_mean_squared_error: 2.1181\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.8675 - root_mean_squared_error: 2.6206 - val_loss: 3.1410 - val_root_mean_squared_error: 1.7723\n",
      "Epoch 12/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.8636 - root_mean_squared_error: 2.2054 - val_loss: 5.0890 - val_root_mean_squared_error: 2.2559\n",
      "Epoch 13/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.5701 - root_mean_squared_error: 2.3601 - val_loss: 5.0951 - val_root_mean_squared_error: 2.2572\n",
      "Epoch 14/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.9471 - root_mean_squared_error: 2.2242 - val_loss: 2.9930 - val_root_mean_squared_error: 1.7300\n",
      "Epoch 15/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.8427 - root_mean_squared_error: 1.9603 - val_loss: 3.1979 - val_root_mean_squared_error: 1.7883\n",
      "Epoch 16/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 4.1891 - root_mean_squared_error: 2.0467 - val_loss: 3.2526 - val_root_mean_squared_error: 1.8035\n",
      "Epoch 17/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.0363 - root_mean_squared_error: 2.2442 - val_loss: 7.0327 - val_root_mean_squared_error: 2.6519\n",
      "Epoch 18/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.4867 - root_mean_squared_error: 2.1182 - val_loss: 4.0795 - val_root_mean_squared_error: 2.0198\n",
      "Epoch 19/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.4412 - root_mean_squared_error: 1.8550 - val_loss: 3.3821 - val_root_mean_squared_error: 1.8391\n",
      "Epoch 20/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.9337 - root_mean_squared_error: 1.9834 - val_loss: 4.0607 - val_root_mean_squared_error: 2.0151\n",
      "Epoch 21/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.1221 - root_mean_squared_error: 1.7670 - val_loss: 3.4570 - val_root_mean_squared_error: 1.8593\n",
      "Epoch 22/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.3229 - root_mean_squared_error: 1.8229 - val_loss: 3.1476 - val_root_mean_squared_error: 1.7741\n",
      "Epoch 23/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.0474 - root_mean_squared_error: 1.7457 - val_loss: 3.1100 - val_root_mean_squared_error: 1.7635\n",
      "Epoch 24/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2020 - root_mean_squared_error: 1.7894 - val_loss: 2.7600 - val_root_mean_squared_error: 1.6613\n",
      "Epoch 25/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.1890 - root_mean_squared_error: 1.7858 - val_loss: 3.2900 - val_root_mean_squared_error: 1.8138\n",
      "Epoch 26/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.0138 - root_mean_squared_error: 1.7360 - val_loss: 2.7709 - val_root_mean_squared_error: 1.6646\n",
      "Epoch 27/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8695 - root_mean_squared_error: 1.6939 - val_loss: 2.7742 - val_root_mean_squared_error: 1.6656\n",
      "Epoch 28/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.6825 - root_mean_squared_error: 1.6378 - val_loss: 2.8814 - val_root_mean_squared_error: 1.6975\n",
      "Epoch 29/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.3641 - root_mean_squared_error: 1.5376 - val_loss: 2.9017 - val_root_mean_squared_error: 1.7034\n",
      "Epoch 30/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5530 - root_mean_squared_error: 1.5978 - val_loss: 3.3682 - val_root_mean_squared_error: 1.8353\n",
      "Epoch 31/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8496 - root_mean_squared_error: 1.6881 - val_loss: 2.8702 - val_root_mean_squared_error: 1.6942\n",
      "Epoch 32/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.3129 - root_mean_squared_error: 1.5208 - val_loss: 2.7743 - val_root_mean_squared_error: 1.6656\n",
      "Epoch 33/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.7397 - root_mean_squared_error: 1.6552 - val_loss: 2.8361 - val_root_mean_squared_error: 1.6841\n",
      "Epoch 34/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5651 - root_mean_squared_error: 1.6016 - val_loss: 2.8077 - val_root_mean_squared_error: 1.6756\n",
      "Epoch 35/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2343 - root_mean_squared_error: 1.4948 - val_loss: 2.9205 - val_root_mean_squared_error: 1.7089\n",
      "Epoch 36/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5397 - root_mean_squared_error: 1.5937 - val_loss: 3.5697 - val_root_mean_squared_error: 1.8894\n",
      "Epoch 37/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5421 - root_mean_squared_error: 1.5944 - val_loss: 3.2925 - val_root_mean_squared_error: 1.8145\n",
      "Epoch 38/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.4039 - root_mean_squared_error: 1.5505 - val_loss: 2.8509 - val_root_mean_squared_error: 1.6885\n",
      "Epoch 39/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8674 - root_mean_squared_error: 1.3665 - val_loss: 2.8096 - val_root_mean_squared_error: 1.6762\n",
      "Epoch 40/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.2621 - root_mean_squared_error: 1.5040 - val_loss: 2.7877 - val_root_mean_squared_error: 1.6696\n",
      "Epoch 41/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.0431 - root_mean_squared_error: 1.4294 - val_loss: 2.8299 - val_root_mean_squared_error: 1.6822\n",
      "Epoch 42/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7955 - root_mean_squared_error: 1.3400 - val_loss: 2.8551 - val_root_mean_squared_error: 1.6897\n",
      "Epoch 43/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.0158 - root_mean_squared_error: 1.4198 - val_loss: 2.8231 - val_root_mean_squared_error: 1.6802\n",
      "Epoch 44/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7178 - root_mean_squared_error: 1.3107 - val_loss: 2.8023 - val_root_mean_squared_error: 1.6740\n",
      "Epoch 45/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.0627 - root_mean_squared_error: 1.4362 - val_loss: 2.9993 - val_root_mean_squared_error: 1.7318\n",
      "Epoch 46/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7052 - root_mean_squared_error: 1.3058 - val_loss: 2.9177 - val_root_mean_squared_error: 1.7081\n",
      "Epoch 47/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7383 - root_mean_squared_error: 1.3184 - val_loss: 3.0824 - val_root_mean_squared_error: 1.7557\n",
      "Epoch 48/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 1.5569 - root_mean_squared_error: 1.2477 - val_loss: 2.8129 - val_root_mean_squared_error: 1.6772\n",
      "Epoch 49/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.9598 - root_mean_squared_error: 1.3999 - val_loss: 2.7044 - val_root_mean_squared_error: 1.6445\n",
      "Epoch 50/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8985 - root_mean_squared_error: 1.3779 - val_loss: 3.0599 - val_root_mean_squared_error: 1.7493\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.6394148504457464\n",
      "RMSE for dataset is:1.7492537951142708& mean of this fold is 17.293335\n",
      "this is 10.115190615722861% of the mean pheno data\n",
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_30 (Dense)             (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation_25 (Activation)   (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_15 (Dropout)         (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_31 (Dense)             (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_26 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_16 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_27 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_17 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_33 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_28 (Activation)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_34 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_29 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_35 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 678 samples, validate on 75 samples\n",
      "Epoch 1/50\n",
      "678/678 [==============================] - 11s 17ms/sample - loss: 242.0163 - root_mean_squared_error: 15.5569 - val_loss: 818.0554 - val_root_mean_squared_error: 28.6017\n",
      "Epoch 2/50\n",
      "678/678 [==============================] - 3s 5ms/sample - loss: 102.5533 - root_mean_squared_error: 10.1269 - val_loss: 640.4510 - val_root_mean_squared_error: 25.3071\n",
      "Epoch 3/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 31.2322 - root_mean_squared_error: 5.5886 - val_loss: 169.4731 - val_root_mean_squared_error: 13.0182\n",
      "Epoch 4/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 19.0757 - root_mean_squared_error: 4.3676 - val_loss: 10.4904 - val_root_mean_squared_error: 3.2389\n",
      "Epoch 5/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 12.4396 - root_mean_squared_error: 3.5270 - val_loss: 7.4874 - val_root_mean_squared_error: 2.7363\n",
      "Epoch 6/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 9.0437 - root_mean_squared_error: 3.0073 - val_loss: 9.2864 - val_root_mean_squared_error: 3.0474\n",
      "Epoch 7/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 9.3829 - root_mean_squared_error: 3.0632 - val_loss: 9.0546 - val_root_mean_squared_error: 3.0091\n",
      "Epoch 8/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 7.2074 - root_mean_squared_error: 2.6847 - val_loss: 6.3039 - val_root_mean_squared_error: 2.5108\n",
      "Epoch 9/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 7.1312 - root_mean_squared_error: 2.6704 - val_loss: 10.1250 - val_root_mean_squared_error: 3.1820\n",
      "Epoch 10/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.8636 - root_mean_squared_error: 2.4215 - val_loss: 8.3426 - val_root_mean_squared_error: 2.8884\n",
      "Epoch 11/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.5316 - root_mean_squared_error: 2.3519 - val_loss: 9.0713 - val_root_mean_squared_error: 3.0119\n",
      "Epoch 12/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.1739 - root_mean_squared_error: 2.2746 - val_loss: 6.0422 - val_root_mean_squared_error: 2.4581\n",
      "Epoch 13/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.8533 - root_mean_squared_error: 2.2030 - val_loss: 6.0679 - val_root_mean_squared_error: 2.4633\n",
      "Epoch 14/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.5738 - root_mean_squared_error: 2.1386 - val_loss: 5.1942 - val_root_mean_squared_error: 2.2791\n",
      "Epoch 15/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 3.6417 - root_mean_squared_error: 1.9083 - val_loss: 5.7681 - val_root_mean_squared_error: 2.4017\n",
      "Epoch 16/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.1868 - root_mean_squared_error: 2.0462 - val_loss: 4.9929 - val_root_mean_squared_error: 2.2345\n",
      "Epoch 17/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.5911 - root_mean_squared_error: 1.8950 - val_loss: 4.9493 - val_root_mean_squared_error: 2.2247\n",
      "Epoch 18/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.8598 - root_mean_squared_error: 1.9646 - val_loss: 4.4765 - val_root_mean_squared_error: 2.1158\n",
      "Epoch 19/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.8478 - root_mean_squared_error: 1.9616 - val_loss: 5.9140 - val_root_mean_squared_error: 2.4319\n",
      "Epoch 20/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.0796 - root_mean_squared_error: 2.0198 - val_loss: 3.9260 - val_root_mean_squared_error: 1.9814\n",
      "Epoch 21/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2699 - root_mean_squared_error: 1.8083 - val_loss: 3.9836 - val_root_mean_squared_error: 1.9959\n",
      "Epoch 22/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.3011 - root_mean_squared_error: 1.8169 - val_loss: 4.1846 - val_root_mean_squared_error: 2.0456\n",
      "Epoch 23/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.6570 - root_mean_squared_error: 1.6300 - val_loss: 4.1416 - val_root_mean_squared_error: 2.0351\n",
      "Epoch 24/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2580 - root_mean_squared_error: 1.8050 - val_loss: 3.8152 - val_root_mean_squared_error: 1.9532\n",
      "Epoch 25/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.9543 - root_mean_squared_error: 1.7188 - val_loss: 3.8039 - val_root_mean_squared_error: 1.9504\n",
      "Epoch 26/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.1197 - root_mean_squared_error: 1.7663 - val_loss: 4.5376 - val_root_mean_squared_error: 2.1302\n",
      "Epoch 27/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8518 - root_mean_squared_error: 1.6887 - val_loss: 4.0629 - val_root_mean_squared_error: 2.0157\n",
      "Epoch 28/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.9718 - root_mean_squared_error: 1.7239 - val_loss: 3.6836 - val_root_mean_squared_error: 1.9193\n",
      "Epoch 29/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5655 - root_mean_squared_error: 1.6017 - val_loss: 3.6516 - val_root_mean_squared_error: 1.9109\n",
      "Epoch 30/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.6669 - root_mean_squared_error: 1.6331 - val_loss: 3.6785 - val_root_mean_squared_error: 1.9179\n",
      "Epoch 31/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.6220 - root_mean_squared_error: 1.6193 - val_loss: 3.7441 - val_root_mean_squared_error: 1.9350\n",
      "Epoch 32/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2142 - root_mean_squared_error: 1.4880 - val_loss: 3.9602 - val_root_mean_squared_error: 1.9900\n",
      "Epoch 33/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2090 - root_mean_squared_error: 1.4863 - val_loss: 4.0124 - val_root_mean_squared_error: 2.0031\n",
      "Epoch 34/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.1384 - root_mean_squared_error: 1.4623 - val_loss: 3.9899 - val_root_mean_squared_error: 1.9975\n",
      "Epoch 35/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.5389 - root_mean_squared_error: 1.5934 - val_loss: 3.8364 - val_root_mean_squared_error: 1.9587\n",
      "Epoch 36/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2417 - root_mean_squared_error: 1.4972 - val_loss: 3.8270 - val_root_mean_squared_error: 1.9563\n",
      "Epoch 37/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.3405 - root_mean_squared_error: 1.5299 - val_loss: 4.0036 - val_root_mean_squared_error: 2.0009\n",
      "Epoch 38/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.3904 - root_mean_squared_error: 1.5461 - val_loss: 3.7280 - val_root_mean_squared_error: 1.9308\n",
      "Epoch 39/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2273 - root_mean_squared_error: 1.4924 - val_loss: 3.8820 - val_root_mean_squared_error: 1.9703\n",
      "Epoch 40/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2008 - root_mean_squared_error: 1.4835 - val_loss: 4.1755 - val_root_mean_squared_error: 2.0434\n",
      "Epoch 41/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2259 - root_mean_squared_error: 1.4919 - val_loss: 4.2763 - val_root_mean_squared_error: 2.0679\n",
      "Epoch 42/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8543 - root_mean_squared_error: 1.3617 - val_loss: 3.7573 - val_root_mean_squared_error: 1.9384\n",
      "Epoch 43/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.4729 - root_mean_squared_error: 1.5725 - val_loss: 3.5035 - val_root_mean_squared_error: 1.8718\n",
      "Epoch 44/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8985 - root_mean_squared_error: 1.3778 - val_loss: 3.5441 - val_root_mean_squared_error: 1.8826\n",
      "Epoch 45/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.3951 - root_mean_squared_error: 1.5476 - val_loss: 4.3463 - val_root_mean_squared_error: 2.0848\n",
      "Epoch 46/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8148 - root_mean_squared_error: 1.3471 - val_loss: 3.5335 - val_root_mean_squared_error: 1.8798\n",
      "Epoch 47/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8809 - root_mean_squared_error: 1.3714 - val_loss: 3.7031 - val_root_mean_squared_error: 1.9243\n",
      "Epoch 48/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 1.6487 - root_mean_squared_error: 1.2840 - val_loss: 3.6530 - val_root_mean_squared_error: 1.9113\n",
      "Epoch 49/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7514 - root_mean_squared_error: 1.3234 - val_loss: 3.6632 - val_root_mean_squared_error: 1.9139\n",
      "Epoch 50/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.4888 - root_mean_squared_error: 1.2202 - val_loss: 4.3249 - val_root_mean_squared_error: 2.0796\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.7368981721922512\n",
      "RMSE for dataset is:2.0796365618468333& mean of this fold is 16.426668\n",
      "this is 12.660124017177196% of the mean pheno data\n",
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_36 (Dense)             (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation_30 (Activation)   (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_18 (Dropout)         (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_37 (Dense)             (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_31 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_19 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_38 (Dense)             (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_32 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_20 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_39 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_33 (Activation)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_40 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_34 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_41 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:7\n",
      "Train on 678 samples, validate on 75 samples\n",
      "Epoch 1/50\n",
      "678/678 [==============================] - 24s 36ms/sample - loss: 259.2287 - root_mean_squared_error: 16.1006 - val_loss: 74.2364 - val_root_mean_squared_error: 8.6161\n",
      "Epoch 2/50\n",
      "678/678 [==============================] - 6s 9ms/sample - loss: 132.1359 - root_mean_squared_error: 11.4950 - val_loss: 42.6992 - val_root_mean_squared_error: 6.5345\n",
      "Epoch 3/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 37.6464 - root_mean_squared_error: 6.1357 - val_loss: 53.0265 - val_root_mean_squared_error: 7.2819\n",
      "Epoch 4/50\n",
      "678/678 [==============================] - 4s 6ms/sample - loss: 12.6126 - root_mean_squared_error: 3.5514 - val_loss: 31.0619 - val_root_mean_squared_error: 5.5733\n",
      "Epoch 5/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 11.6934 - root_mean_squared_error: 3.4196 - val_loss: 15.5378 - val_root_mean_squared_error: 3.9418\n",
      "Epoch 6/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 9.4626 - root_mean_squared_error: 3.0761 - val_loss: 12.1842 - val_root_mean_squared_error: 3.4906\n",
      "Epoch 7/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 10.1782 - root_mean_squared_error: 3.1903 - val_loss: 6.4231 - val_root_mean_squared_error: 2.5344\n",
      "Epoch 8/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 8.7265 - root_mean_squared_error: 2.9541 - val_loss: 40.0197 - val_root_mean_squared_error: 6.3261\n",
      "Epoch 9/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.2058 - root_mean_squared_error: 2.4911 - val_loss: 9.8499 - val_root_mean_squared_error: 3.1384\n",
      "Epoch 10/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.5745 - root_mean_squared_error: 2.3610 - val_loss: 6.2120 - val_root_mean_squared_error: 2.4924\n",
      "Epoch 11/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.6285 - root_mean_squared_error: 2.5746 - val_loss: 13.4616 - val_root_mean_squared_error: 3.6690\n",
      "Epoch 12/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.5949 - root_mean_squared_error: 2.3654 - val_loss: 6.2995 - val_root_mean_squared_error: 2.5099\n",
      "Epoch 13/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.7412 - root_mean_squared_error: 2.1774 - val_loss: 3.9773 - val_root_mean_squared_error: 1.9943\n",
      "Epoch 14/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.9419 - root_mean_squared_error: 1.9854 - val_loss: 3.7944 - val_root_mean_squared_error: 1.9479\n",
      "Epoch 15/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.0403 - root_mean_squared_error: 2.0101 - val_loss: 3.1193 - val_root_mean_squared_error: 1.7661\n",
      "Epoch 16/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.4904 - root_mean_squared_error: 1.8683 - val_loss: 3.0475 - val_root_mean_squared_error: 1.7457\n",
      "Epoch 17/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 3.7375 - root_mean_squared_error: 1.9333 - val_loss: 2.9434 - val_root_mean_squared_error: 1.7156\n",
      "Epoch 18/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.1334 - root_mean_squared_error: 1.7701 - val_loss: 4.5294 - val_root_mean_squared_error: 2.1282\n",
      "Epoch 19/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2818 - root_mean_squared_error: 1.8116 - val_loss: 3.2324 - val_root_mean_squared_error: 1.7979\n",
      "Epoch 20/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.5568 - root_mean_squared_error: 1.8859 - val_loss: 2.9292 - val_root_mean_squared_error: 1.7115\n",
      "Epoch 21/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.5459 - root_mean_squared_error: 1.8831 - val_loss: 2.9826 - val_root_mean_squared_error: 1.7270\n",
      "Epoch 22/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.0199 - root_mean_squared_error: 1.7378 - val_loss: 2.8134 - val_root_mean_squared_error: 1.6773\n",
      "Epoch 23/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2744 - root_mean_squared_error: 1.8095 - val_loss: 2.6060 - val_root_mean_squared_error: 1.6143\n",
      "Epoch 24/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.0482 - root_mean_squared_error: 1.7459 - val_loss: 3.1563 - val_root_mean_squared_error: 1.7766\n",
      "Epoch 25/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.9048 - root_mean_squared_error: 1.7043 - val_loss: 3.0115 - val_root_mean_squared_error: 1.7354\n",
      "Epoch 26/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5903 - root_mean_squared_error: 1.6094 - val_loss: 3.2185 - val_root_mean_squared_error: 1.7940\n",
      "Epoch 27/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.9051 - root_mean_squared_error: 1.7044 - val_loss: 3.9479 - val_root_mean_squared_error: 1.9869\n",
      "Epoch 28/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.7144 - root_mean_squared_error: 1.6476 - val_loss: 5.5367 - val_root_mean_squared_error: 2.3530\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.9452 - root_mean_squared_error: 1.9862 - val_loss: 9.3837 - val_root_mean_squared_error: 3.0633\n",
      "Epoch 30/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8755 - root_mean_squared_error: 1.6957 - val_loss: 5.2040 - val_root_mean_squared_error: 2.2812\n",
      "Epoch 31/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8661 - root_mean_squared_error: 1.6930 - val_loss: 3.2057 - val_root_mean_squared_error: 1.7905\n",
      "Epoch 32/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8016 - root_mean_squared_error: 1.6738 - val_loss: 3.6113 - val_root_mean_squared_error: 1.9003\n",
      "Epoch 33/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.0415 - root_mean_squared_error: 1.7440 - val_loss: 3.9331 - val_root_mean_squared_error: 1.9832\n",
      "Epoch 34/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.0604 - root_mean_squared_error: 1.4354 - val_loss: 3.1382 - val_root_mean_squared_error: 1.7715\n",
      "Epoch 35/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.5432 - root_mean_squared_error: 1.5948 - val_loss: 2.9289 - val_root_mean_squared_error: 1.7114\n",
      "Epoch 36/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.1357 - root_mean_squared_error: 1.4614 - val_loss: 2.9060 - val_root_mean_squared_error: 1.7047\n",
      "Epoch 37/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.9872 - root_mean_squared_error: 1.4097 - val_loss: 3.0889 - val_root_mean_squared_error: 1.7575\n",
      "Epoch 38/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 1.9227 - root_mean_squared_error: 1.3866 - val_loss: 2.9757 - val_root_mean_squared_error: 1.7250\n",
      "Epoch 39/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 1.8737 - root_mean_squared_error: 1.3688 - val_loss: 3.7391 - val_root_mean_squared_error: 1.9337\n",
      "Epoch 40/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.9381 - root_mean_squared_error: 1.3922 - val_loss: 3.0840 - val_root_mean_squared_error: 1.7561\n",
      "Epoch 41/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.6624 - root_mean_squared_error: 1.2893 - val_loss: 3.0739 - val_root_mean_squared_error: 1.7532\n",
      "Epoch 42/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.6265 - root_mean_squared_error: 1.2753 - val_loss: 2.9343 - val_root_mean_squared_error: 1.7130\n",
      "Epoch 43/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.5223 - root_mean_squared_error: 1.2338 - val_loss: 3.0247 - val_root_mean_squared_error: 1.7392\n",
      "Epoch 44/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.3843 - root_mean_squared_error: 1.1766 - val_loss: 5.9274 - val_root_mean_squared_error: 2.4346\n",
      "Epoch 45/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7522 - root_mean_squared_error: 1.3237 - val_loss: 2.9264 - val_root_mean_squared_error: 1.7107\n",
      "Epoch 46/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8635 - root_mean_squared_error: 1.3651 - val_loss: 2.8566 - val_root_mean_squared_error: 1.6901\n",
      "Epoch 47/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 1.4783 - root_mean_squared_error: 1.2159 - val_loss: 3.0494 - val_root_mean_squared_error: 1.7462\n",
      "Epoch 48/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.3038 - root_mean_squared_error: 1.1418 - val_loss: 3.2380 - val_root_mean_squared_error: 1.7994\n",
      "Epoch 49/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.5144 - root_mean_squared_error: 1.2306 - val_loss: 2.8694 - val_root_mean_squared_error: 1.6939\n",
      "Epoch 50/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.1561 - root_mean_squared_error: 1.0752 - val_loss: 2.8179 - val_root_mean_squared_error: 1.6787\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.733054046025327\n",
      "RMSE for dataset is:1.6786573932978361& mean of this fold is 17.1\n",
      "this is 9.816709683333688% of the mean pheno data\n",
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_42 (Dense)             (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation_35 (Activation)   (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_21 (Dropout)         (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_43 (Dense)             (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_36 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_22 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_44 (Dense)             (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_37 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_23 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_45 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_38 (Activation)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_46 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_39 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_47 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:8\n",
      "Train on 678 samples, validate on 75 samples\n",
      "Epoch 1/50\n",
      "678/678 [==============================] - 15s 23ms/sample - loss: 266.4575 - root_mean_squared_error: 16.3235 - val_loss: 18.8318 - val_root_mean_squared_error: 4.3396\n",
      "Epoch 2/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 154.1099 - root_mean_squared_error: 12.4141 - val_loss: 69.2256 - val_root_mean_squared_error: 8.3202\n",
      "Epoch 3/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 37.7420 - root_mean_squared_error: 6.1435 - val_loss: 79.6970 - val_root_mean_squared_error: 8.9273\n",
      "Epoch 4/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 13.5891 - root_mean_squared_error: 3.6863 - val_loss: 19.9907 - val_root_mean_squared_error: 4.4711\n",
      "Epoch 5/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 9.5064 - root_mean_squared_error: 3.0833 - val_loss: 11.7631 - val_root_mean_squared_error: 3.4297\n",
      "Epoch 6/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 9.0389 - root_mean_squared_error: 3.0065 - val_loss: 20.8342 - val_root_mean_squared_error: 4.5645\n",
      "Epoch 7/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 8.9135 - root_mean_squared_error: 2.9855 - val_loss: 4.4226 - val_root_mean_squared_error: 2.1030\n",
      "Epoch 8/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 7.3933 - root_mean_squared_error: 2.7191 - val_loss: 5.6818 - val_root_mean_squared_error: 2.3837\n",
      "Epoch 9/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.8310 - root_mean_squared_error: 2.4147 - val_loss: 4.2332 - val_root_mean_squared_error: 2.0575\n",
      "Epoch 10/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.6840 - root_mean_squared_error: 2.3841 - val_loss: 4.2987 - val_root_mean_squared_error: 2.0733\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 11/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.6001 - root_mean_squared_error: 2.3664 - val_loss: 3.7941 - val_root_mean_squared_error: 1.9479\n",
      "Epoch 12/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.2884 - root_mean_squared_error: 2.2997 - val_loss: 3.4308 - val_root_mean_squared_error: 1.8523\n",
      "Epoch 13/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.2793 - root_mean_squared_error: 2.2977 - val_loss: 3.1883 - val_root_mean_squared_error: 1.7856\n",
      "Epoch 14/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.0959 - root_mean_squared_error: 2.2574 - val_loss: 3.3802 - val_root_mean_squared_error: 1.8385\n",
      "Epoch 15/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.5344 - root_mean_squared_error: 2.1294 - val_loss: 3.4224 - val_root_mean_squared_error: 1.8500\n",
      "Epoch 16/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.4332 - root_mean_squared_error: 2.1055 - val_loss: 3.5575 - val_root_mean_squared_error: 1.8861\n",
      "Epoch 17/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.9605 - root_mean_squared_error: 2.2272 - val_loss: 3.4527 - val_root_mean_squared_error: 1.8582\n",
      "Epoch 18/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.7894 - root_mean_squared_error: 1.9466 - val_loss: 3.0801 - val_root_mean_squared_error: 1.7550\n",
      "Epoch 19/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.0269 - root_mean_squared_error: 2.0067 - val_loss: 3.0263 - val_root_mean_squared_error: 1.7396\n",
      "Epoch 20/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.4911 - root_mean_squared_error: 2.1192 - val_loss: 2.9559 - val_root_mean_squared_error: 1.7193\n",
      "Epoch 21/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.0082 - root_mean_squared_error: 2.0020 - val_loss: 3.0971 - val_root_mean_squared_error: 1.7599\n",
      "Epoch 22/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 3.5784 - root_mean_squared_error: 1.8917 - val_loss: 2.9203 - val_root_mean_squared_error: 1.7089\n",
      "Epoch 23/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.4015 - root_mean_squared_error: 1.8443 - val_loss: 2.9257 - val_root_mean_squared_error: 1.7105\n",
      "Epoch 24/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.7635 - root_mean_squared_error: 1.9400 - val_loss: 2.9904 - val_root_mean_squared_error: 1.7293\n",
      "Epoch 25/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.3488 - root_mean_squared_error: 1.8300 - val_loss: 3.5536 - val_root_mean_squared_error: 1.8851\n",
      "Epoch 26/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2385 - root_mean_squared_error: 1.7996 - val_loss: 3.1145 - val_root_mean_squared_error: 1.7648\n",
      "Epoch 27/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2696 - root_mean_squared_error: 1.8082 - val_loss: 2.9508 - val_root_mean_squared_error: 1.7178\n",
      "Epoch 28/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.5321 - root_mean_squared_error: 1.8794 - val_loss: 3.4178 - val_root_mean_squared_error: 1.8487\n",
      "Epoch 29/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 3.2807 - root_mean_squared_error: 1.8113 - val_loss: 2.8013 - val_root_mean_squared_error: 1.6737\n",
      "Epoch 30/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2027 - root_mean_squared_error: 1.7896 - val_loss: 3.3988 - val_root_mean_squared_error: 1.8436\n",
      "Epoch 31/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2410 - root_mean_squared_error: 1.8003 - val_loss: 3.0784 - val_root_mean_squared_error: 1.7545\n",
      "Epoch 32/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.3278 - root_mean_squared_error: 1.8242 - val_loss: 3.2402 - val_root_mean_squared_error: 1.8001\n",
      "Epoch 33/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.7296 - root_mean_squared_error: 1.6521 - val_loss: 2.5472 - val_root_mean_squared_error: 1.5960\n",
      "Epoch 34/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5800 - root_mean_squared_error: 1.6062 - val_loss: 2.5412 - val_root_mean_squared_error: 1.5941\n",
      "Epoch 35/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.1237 - root_mean_squared_error: 1.7674 - val_loss: 2.8902 - val_root_mean_squared_error: 1.7001\n",
      "Epoch 36/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.8435 - root_mean_squared_error: 1.6863 - val_loss: 2.5658 - val_root_mean_squared_error: 1.6018\n",
      "Epoch 37/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5647 - root_mean_squared_error: 1.6015 - val_loss: 3.0003 - val_root_mean_squared_error: 1.7321\n",
      "Epoch 38/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8022 - root_mean_squared_error: 1.6740 - val_loss: 2.6193 - val_root_mean_squared_error: 1.6184\n",
      "Epoch 39/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.7786 - root_mean_squared_error: 1.6669 - val_loss: 2.7135 - val_root_mean_squared_error: 1.6473\n",
      "Epoch 40/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.6920 - root_mean_squared_error: 1.6407 - val_loss: 2.7341 - val_root_mean_squared_error: 1.6535\n",
      "Epoch 41/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.4660 - root_mean_squared_error: 1.5704 - val_loss: 2.8740 - val_root_mean_squared_error: 1.6953\n",
      "Epoch 42/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.4688 - root_mean_squared_error: 1.5712 - val_loss: 2.7180 - val_root_mean_squared_error: 1.6486\n",
      "Epoch 43/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.1137 - root_mean_squared_error: 1.4539 - val_loss: 2.7009 - val_root_mean_squared_error: 1.6435\n",
      "Epoch 44/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.3299 - root_mean_squared_error: 1.5264 - val_loss: 2.6056 - val_root_mean_squared_error: 1.6142\n",
      "Epoch 45/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.9568 - root_mean_squared_error: 1.3988 - val_loss: 2.7918 - val_root_mean_squared_error: 1.6709\n",
      "Epoch 46/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.0197 - root_mean_squared_error: 1.4212 - val_loss: 2.8692 - val_root_mean_squared_error: 1.6939\n",
      "Epoch 47/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.1410 - root_mean_squared_error: 1.4632 - val_loss: 2.7009 - val_root_mean_squared_error: 1.6435\n",
      "Epoch 48/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.9219 - root_mean_squared_error: 1.3863 - val_loss: 2.9182 - val_root_mean_squared_error: 1.7083\n",
      "Epoch 49/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7877 - root_mean_squared_error: 1.3371 - val_loss: 2.6409 - val_root_mean_squared_error: 1.6251\n",
      "Epoch 50/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8804 - root_mean_squared_error: 1.3713 - val_loss: 5.5001 - val_root_mean_squared_error: 2.3452\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.15012478837455012\n",
      "RMSE for dataset is:2.345238683302605& mean of this fold is 17.569332\n",
      "this is 13.3484794237499% of the mean pheno data\n",
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_48 (Dense)             (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation_40 (Activation)   (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_24 (Dropout)         (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_49 (Dense)             (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_41 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_25 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_50 (Dense)             (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_42 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_26 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_51 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_43 (Activation)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_52 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_44 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_53 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 678 samples, validate on 75 samples\n",
      "Epoch 1/50\n",
      "678/678 [==============================] - 16s 24ms/sample - loss: 256.6408 - root_mean_squared_error: 16.0200 - val_loss: 93.5879 - val_root_mean_squared_error: 9.6741\n",
      "Epoch 2/50\n",
      "678/678 [==============================] - 3s 5ms/sample - loss: 118.9338 - root_mean_squared_error: 10.9057 - val_loss: 121.0670 - val_root_mean_squared_error: 11.0030\n",
      "Epoch 3/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 31.5218 - root_mean_squared_error: 5.6144 - val_loss: 49.3055 - val_root_mean_squared_error: 7.0218\n",
      "Epoch 4/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 18.6057 - root_mean_squared_error: 4.3134 - val_loss: 31.2479 - val_root_mean_squared_error: 5.5900\n",
      "Epoch 5/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 15.4611 - root_mean_squared_error: 3.9321 - val_loss: 18.4963 - val_root_mean_squared_error: 4.3007\n",
      "Epoch 6/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 14.7841 - root_mean_squared_error: 3.8450 - val_loss: 38.4849 - val_root_mean_squared_error: 6.2036\n",
      "Epoch 7/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 10.1184 - root_mean_squared_error: 3.1809 - val_loss: 15.6668 - val_root_mean_squared_error: 3.9581\n",
      "Epoch 8/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 8.0023 - root_mean_squared_error: 2.8288 - val_loss: 12.1182 - val_root_mean_squared_error: 3.4811\n",
      "Epoch 9/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.8807 - root_mean_squared_error: 2.6231 - val_loss: 10.4618 - val_root_mean_squared_error: 3.2345\n",
      "Epoch 10/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.3625 - root_mean_squared_error: 2.5224 - val_loss: 10.7441 - val_root_mean_squared_error: 3.2778\n",
      "Epoch 11/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.0169 - root_mean_squared_error: 2.4529 - val_loss: 6.3843 - val_root_mean_squared_error: 2.5267\n",
      "Epoch 12/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.7043 - root_mean_squared_error: 2.1690 - val_loss: 4.7668 - val_root_mean_squared_error: 2.1833\n",
      "Epoch 13/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.9062 - root_mean_squared_error: 2.2150 - val_loss: 5.5489 - val_root_mean_squared_error: 2.3556\n",
      "Epoch 14/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.2839 - root_mean_squared_error: 2.0698 - val_loss: 5.0683 - val_root_mean_squared_error: 2.2513\n",
      "Epoch 15/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.2314 - root_mean_squared_error: 2.0570 - val_loss: 5.5524 - val_root_mean_squared_error: 2.3564\n",
      "Epoch 16/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.4835 - root_mean_squared_error: 2.1174 - val_loss: 5.9284 - val_root_mean_squared_error: 2.4348\n",
      "Epoch 17/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.7907 - root_mean_squared_error: 1.9470 - val_loss: 5.2718 - val_root_mean_squared_error: 2.2960\n",
      "Epoch 18/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.9772 - root_mean_squared_error: 1.9943 - val_loss: 5.6603 - val_root_mean_squared_error: 2.3791\n",
      "Epoch 19/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.9453 - root_mean_squared_error: 1.9863 - val_loss: 5.7080 - val_root_mean_squared_error: 2.3891\n",
      "Epoch 20/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.1967 - root_mean_squared_error: 1.7879 - val_loss: 6.1375 - val_root_mean_squared_error: 2.4774\n",
      "Epoch 21/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2982 - root_mean_squared_error: 1.8161 - val_loss: 4.7436 - val_root_mean_squared_error: 2.1780\n",
      "Epoch 22/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2929 - root_mean_squared_error: 1.8146 - val_loss: 5.7917 - val_root_mean_squared_error: 2.4066\n",
      "Epoch 23/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.5276 - root_mean_squared_error: 1.8782 - val_loss: 4.7168 - val_root_mean_squared_error: 2.1718\n",
      "Epoch 24/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.7014 - root_mean_squared_error: 1.9239 - val_loss: 6.8956 - val_root_mean_squared_error: 2.6260\n",
      "Epoch 25/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.5551 - root_mean_squared_error: 1.8855 - val_loss: 4.7445 - val_root_mean_squared_error: 2.1782\n",
      "Epoch 26/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.9682 - root_mean_squared_error: 1.7228 - val_loss: 4.8118 - val_root_mean_squared_error: 2.1936\n",
      "Epoch 27/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.9854 - root_mean_squared_error: 1.7278 - val_loss: 5.7777 - val_root_mean_squared_error: 2.4037\n",
      "Epoch 28/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.0810 - root_mean_squared_error: 1.7553 - val_loss: 5.3542 - val_root_mean_squared_error: 2.3139\n",
      "Epoch 29/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.4075 - root_mean_squared_error: 1.8459 - val_loss: 5.8149 - val_root_mean_squared_error: 2.4114\n",
      "Epoch 30/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.0745 - root_mean_squared_error: 1.7534 - val_loss: 4.2988 - val_root_mean_squared_error: 2.0734\n",
      "Epoch 31/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.6458 - root_mean_squared_error: 1.6266 - val_loss: 4.5454 - val_root_mean_squared_error: 2.1320\n",
      "Epoch 32/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5233 - root_mean_squared_error: 1.5885 - val_loss: 4.9130 - val_root_mean_squared_error: 2.2165\n",
      "Epoch 33/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.9222 - root_mean_squared_error: 1.7095 - val_loss: 4.8934 - val_root_mean_squared_error: 2.2121\n",
      "Epoch 34/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.4846 - root_mean_squared_error: 1.5763 - val_loss: 4.4551 - val_root_mean_squared_error: 2.1107\n",
      "Epoch 35/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.4892 - root_mean_squared_error: 1.5777 - val_loss: 6.2943 - val_root_mean_squared_error: 2.5088\n",
      "Epoch 36/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.2044 - root_mean_squared_error: 1.4847 - val_loss: 5.2795 - val_root_mean_squared_error: 2.2977\n",
      "Epoch 37/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.3826 - root_mean_squared_error: 1.5436 - val_loss: 4.6974 - val_root_mean_squared_error: 2.1674\n",
      "Epoch 38/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.1368 - root_mean_squared_error: 1.4618 - val_loss: 4.5723 - val_root_mean_squared_error: 2.1383\n",
      "Epoch 39/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.2746 - root_mean_squared_error: 1.5082 - val_loss: 5.0271 - val_root_mean_squared_error: 2.2421\n",
      "Epoch 40/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.1044 - root_mean_squared_error: 1.4507 - val_loss: 4.6826 - val_root_mean_squared_error: 2.1639\n",
      "Epoch 41/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2629 - root_mean_squared_error: 1.5043 - val_loss: 4.9146 - val_root_mean_squared_error: 2.2169\n",
      "Epoch 42/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2848 - root_mean_squared_error: 1.5116 - val_loss: 5.0161 - val_root_mean_squared_error: 2.2397\n",
      "Epoch 43/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.0837 - root_mean_squared_error: 1.4435 - val_loss: 4.9706 - val_root_mean_squared_error: 2.2295\n",
      "Epoch 44/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 1.9202 - root_mean_squared_error: 1.3857 - val_loss: 5.4212 - val_root_mean_squared_error: 2.3283\n",
      "Epoch 45/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.9107 - root_mean_squared_error: 1.3823 - val_loss: 5.4569 - val_root_mean_squared_error: 2.3360\n",
      "Epoch 46/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.0895 - root_mean_squared_error: 1.4455 - val_loss: 5.4816 - val_root_mean_squared_error: 2.3413\n",
      "Epoch 47/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7631 - root_mean_squared_error: 1.3278 - val_loss: 5.4578 - val_root_mean_squared_error: 2.3362\n",
      "Epoch 48/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.2333 - root_mean_squared_error: 1.4944 - val_loss: 5.9798 - val_root_mean_squared_error: 2.4454\n",
      "Epoch 49/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7907 - root_mean_squared_error: 1.3382 - val_loss: 5.4091 - val_root_mean_squared_error: 2.3258\n",
      "Epoch 50/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.0035 - root_mean_squared_error: 1.4155 - val_loss: 5.8694 - val_root_mean_squared_error: 2.4227\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.18676808339106665\n",
      "RMSE for dataset is:2.422682104677018& mean of this fold is 17.768\n",
      "this is 13.635086405502289% of the mean pheno data\n",
      "Model: \"sequential_9\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_54 (Dense)             (None, 200)               124188200 \n",
      "_________________________________________________________________\n",
      "activation_45 (Activation)   (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dropout_27 (Dropout)         (None, 200)               0         \n",
      "_________________________________________________________________\n",
      "dense_55 (Dense)             (None, 100)               20100     \n",
      "_________________________________________________________________\n",
      "activation_46 (Activation)   (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dropout_28 (Dropout)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_56 (Dense)             (None, 64)                6464      \n",
      "_________________________________________________________________\n",
      "activation_47 (Activation)   (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dropout_29 (Dropout)         (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_57 (Dense)             (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "activation_48 (Activation)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dense_58 (Dense)             (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "activation_49 (Activation)   (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_59 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 124,217,517\n",
      "Trainable params: 124,217,453\n",
      "Non-trainable params: 64\n",
      "_________________________________________________________________\n",
      "None\n",
      "Fold:10\n",
      "Train on 678 samples, validate on 75 samples\n",
      "Epoch 1/50\n",
      "678/678 [==============================] - 18s 27ms/sample - loss: 209.3504 - root_mean_squared_error: 14.4689 - val_loss: 642.5338 - val_root_mean_squared_error: 25.3483\n",
      "Epoch 2/50\n",
      "678/678 [==============================] - 5s 8ms/sample - loss: 68.7108 - root_mean_squared_error: 8.2892 - val_loss: 412.6491 - val_root_mean_squared_error: 20.3138\n",
      "Epoch 3/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 26.8565 - root_mean_squared_error: 5.1823 - val_loss: 68.4870 - val_root_mean_squared_error: 8.2757\n",
      "Epoch 4/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 13.7912 - root_mean_squared_error: 3.7136 - val_loss: 21.4812 - val_root_mean_squared_error: 4.6348\n",
      "Epoch 5/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 12.6556 - root_mean_squared_error: 3.5575 - val_loss: 41.0009 - val_root_mean_squared_error: 6.4032\n",
      "Epoch 6/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 10.1387 - root_mean_squared_error: 3.1841 - val_loss: 14.7728 - val_root_mean_squared_error: 3.8435\n",
      "Epoch 7/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 9.0856 - root_mean_squared_error: 3.0142 - val_loss: 6.4267 - val_root_mean_squared_error: 2.5351\n",
      "Epoch 8/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 8.0555 - root_mean_squared_error: 2.8382 - val_loss: 5.7024 - val_root_mean_squared_error: 2.3880\n",
      "Epoch 9/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.8616 - root_mean_squared_error: 2.6195 - val_loss: 4.6302 - val_root_mean_squared_error: 2.1518\n",
      "Epoch 10/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.4554 - root_mean_squared_error: 2.5408 - val_loss: 4.6836 - val_root_mean_squared_error: 2.1642\n",
      "Epoch 11/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 6.1914 - root_mean_squared_error: 2.4882 - val_loss: 8.2743 - val_root_mean_squared_error: 2.8765\n",
      "Epoch 12/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 5.8517 - root_mean_squared_error: 2.4190 - val_loss: 4.0328 - val_root_mean_squared_error: 2.0082\n",
      "Epoch 13/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 5.2268 - root_mean_squared_error: 2.2862 - val_loss: 5.0405 - val_root_mean_squared_error: 2.2451\n",
      "Epoch 14/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.9350 - root_mean_squared_error: 1.9837 - val_loss: 4.6528 - val_root_mean_squared_error: 2.1570\n",
      "Epoch 15/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.3105 - root_mean_squared_error: 2.0762 - val_loss: 6.6639 - val_root_mean_squared_error: 2.5815\n",
      "Epoch 16/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.0347 - root_mean_squared_error: 2.0087 - val_loss: 4.6673 - val_root_mean_squared_error: 2.1604\n",
      "Epoch 17/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.6774 - root_mean_squared_error: 1.9176 - val_loss: 4.5142 - val_root_mean_squared_error: 2.1247\n",
      "Epoch 18/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.5465 - root_mean_squared_error: 1.8832 - val_loss: 4.5878 - val_root_mean_squared_error: 2.1419\n",
      "Epoch 19/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.7548 - root_mean_squared_error: 1.9377 - val_loss: 4.5004 - val_root_mean_squared_error: 2.1214\n",
      "Epoch 20/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.2774 - root_mean_squared_error: 1.8104 - val_loss: 3.7437 - val_root_mean_squared_error: 1.9349\n",
      "Epoch 21/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 3.4501 - root_mean_squared_error: 1.8574 - val_loss: 5.1367 - val_root_mean_squared_error: 2.2664\n",
      "Epoch 22/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 4.0325 - root_mean_squared_error: 2.0081 - val_loss: 4.0786 - val_root_mean_squared_error: 2.0195\n",
      "Epoch 23/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 3.6945 - root_mean_squared_error: 1.9221 - val_loss: 5.8181 - val_root_mean_squared_error: 2.4121\n",
      "Epoch 24/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.0485 - root_mean_squared_error: 1.7460 - val_loss: 3.4695 - val_root_mean_squared_error: 1.8627\n",
      "Epoch 25/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5314 - root_mean_squared_error: 1.5910 - val_loss: 4.2404 - val_root_mean_squared_error: 2.0592\n",
      "Epoch 26/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.5215 - root_mean_squared_error: 1.5879 - val_loss: 3.6322 - val_root_mean_squared_error: 1.9058\n",
      "Epoch 27/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.9259 - root_mean_squared_error: 1.7105 - val_loss: 3.1231 - val_root_mean_squared_error: 1.7672\n",
      "Epoch 28/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8327 - root_mean_squared_error: 1.6831 - val_loss: 2.9716 - val_root_mean_squared_error: 1.7238\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 29/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.4791 - root_mean_squared_error: 1.5745 - val_loss: 3.3698 - val_root_mean_squared_error: 1.8357\n",
      "Epoch 30/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.6215 - root_mean_squared_error: 1.6191 - val_loss: 3.0581 - val_root_mean_squared_error: 1.7488\n",
      "Epoch 31/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.6986 - root_mean_squared_error: 1.6428 - val_loss: 6.7460 - val_root_mean_squared_error: 2.5973\n",
      "Epoch 32/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.1081 - root_mean_squared_error: 1.7630 - val_loss: 10.3125 - val_root_mean_squared_error: 3.2113\n",
      "Epoch 33/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.3756 - root_mean_squared_error: 1.8373 - val_loss: 7.8648 - val_root_mean_squared_error: 2.8044\n",
      "Epoch 34/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 3.1911 - root_mean_squared_error: 1.7864 - val_loss: 4.0894 - val_root_mean_squared_error: 2.0222\n",
      "Epoch 35/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.4026 - root_mean_squared_error: 1.5500 - val_loss: 3.6056 - val_root_mean_squared_error: 1.8988\n",
      "Epoch 36/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.8296 - root_mean_squared_error: 1.6821 - val_loss: 4.0813 - val_root_mean_squared_error: 2.0202\n",
      "Epoch 37/50\n",
      "678/678 [==============================] - 3s 4ms/sample - loss: 2.4560 - root_mean_squared_error: 1.5672 - val_loss: 9.3037 - val_root_mean_squared_error: 3.0502\n",
      "Epoch 38/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.7177 - root_mean_squared_error: 1.6485 - val_loss: 4.4746 - val_root_mean_squared_error: 2.1153\n",
      "Epoch 39/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.4290 - root_mean_squared_error: 1.5585 - val_loss: 3.1992 - val_root_mean_squared_error: 1.7886\n",
      "Epoch 40/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.4624 - root_mean_squared_error: 1.5692 - val_loss: 3.2577 - val_root_mean_squared_error: 1.8049\n",
      "Epoch 41/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.0292 - root_mean_squared_error: 1.4245 - val_loss: 3.9430 - val_root_mean_squared_error: 1.9857\n",
      "Epoch 42/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 2.0564 - root_mean_squared_error: 1.4340 - val_loss: 3.4070 - val_root_mean_squared_error: 1.8458\n",
      "Epoch 43/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.9154 - root_mean_squared_error: 1.3840 - val_loss: 3.4208 - val_root_mean_squared_error: 1.8495\n",
      "Epoch 44/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8834 - root_mean_squared_error: 1.3724 - val_loss: 3.2297 - val_root_mean_squared_error: 1.7971\n",
      "Epoch 45/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.8963 - root_mean_squared_error: 1.3771 - val_loss: 3.4109 - val_root_mean_squared_error: 1.8469\n",
      "Epoch 46/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7525 - root_mean_squared_error: 1.3238 - val_loss: 3.3872 - val_root_mean_squared_error: 1.8404\n",
      "Epoch 47/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7480 - root_mean_squared_error: 1.3221 - val_loss: 3.3814 - val_root_mean_squared_error: 1.8388\n",
      "Epoch 48/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.5985 - root_mean_squared_error: 1.2643 - val_loss: 3.2038 - val_root_mean_squared_error: 1.7899\n",
      "Epoch 49/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.6834 - root_mean_squared_error: 1.2974 - val_loss: 3.1643 - val_root_mean_squared_error: 1.7788\n",
      "Epoch 50/50\n",
      "678/678 [==============================] - 2s 4ms/sample - loss: 1.7326 - root_mean_squared_error: 1.3163 - val_loss: 3.3108 - val_root_mean_squared_error: 1.8196\n",
      "fitting done. Processing fold accuracy + checking best model\n",
      "R^2 Value is: 0.42153556099212375\n",
      "RMSE for dataset is:1.819559490006001& mean of this fold is 17.904\n",
      "this is 10.162866165315766% of the mean pheno data\n",
      "Training Testing Accuracy: 10.90% (1.70%)\n"
     ]
    }
   ],
   "source": [
    "#my_model = build_CNN_model()\n",
    "best_DNN = eval_DNN(tt_vcf, tt_pheno, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 Value of Holdout: 0.63\n",
      "RMSE of Holdout: 1.84\n",
      "Mean of Holdout: 17.17\n",
      "this is 10.738319474063003% of the mean pheno data\n"
     ]
    }
   ],
   "source": [
    "#ho_vcf = ho_vcf.reshape(ho_vcf.shape[0], ho_vcf.shape[1], 1)\n",
    "all_preds = [x for x in best_DNN.predict(ho_vcf)]\n",
    "ss = sqrt(mean_squared_error(all_preds, ho_pheno))\n",
    "rr = r2_score(all_preds, ho_pheno)\n",
    "mm = np.mean(ho_pheno)\n",
    "error_mean = ((ss/mm)*100)\n",
    "print(\"R^2 Value of Holdout: %.2f\" % rr)\n",
    "print(\"RMSE of Holdout: %.2f\" % ss)\n",
    "print(\"Mean of Holdout: %.2f\" % mm)\n",
    "print(\"this is \"+ str((ss/mm)*100) + \"% of the mean pheno data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 17.6 [16.134573]\n",
      "1 23.6 [19.051014]\n",
      "2 15.1 [15.280489]\n",
      "3 19.2 [18.973824]\n",
      "4 14.5 [16.545984]\n",
      "5 14.9 [14.623625]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-c4a2e0c37ede>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mthisplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'Oil'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mplot_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'preds'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mplot_y\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m#sns.jointplot(x=\"Oil\", y=\"preds\", data=thisplot, kind='reg' , joint_kws={'scatter_kws': {'alpha': 0.2}})\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Oil\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"preds\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mthisplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Real Seed Oil %'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Predicted Seed Oil %'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mregplot\u001b[0;34m(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)\u001b[0m\n\u001b[1;32m    833\u001b[0m     \u001b[0mscatter_kws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"marker\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    834\u001b[0m     \u001b[0mline_kws\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mline_kws\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 835\u001b[0;31m     \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscatter_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    836\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    837\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, scatter_kws, line_kws)\u001b[0m\n\u001b[1;32m    368\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_reg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 370\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    372\u001b[0m         \u001b[0;31m# Label the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mlineplot\u001b[0;34m(self, ax, kws)\u001b[0m\n\u001b[1;32m    423\u001b[0m         \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msticky_edges\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0medges\u001b[0m  \u001b[0;31m# Prevent mpl from adding margin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merr_bands\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 425\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0merr_bands\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_color\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1445\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1446\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1448\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1449\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mfill_between\u001b[0;34m(self, x, y1, y2, where, interpolate, step, **kwargs)\u001b[0m\n\u001b[1;32m   5301\u001b[0m         return self._fill_between_x_or_y(\n\u001b[1;32m   5302\u001b[0m             \u001b[0;34m\"x\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5303\u001b[0;31m             where=where, interpolate=interpolate, step=step, **kwargs)\n\u001b[0m\u001b[1;32m   5304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5305\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_fill_between_x_or_y\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36m_fill_between_x_or_y\u001b[0;34m(self, ind_dir, ind, dep1, dep2, where, interpolate, step, **kwargs)\u001b[0m\n\u001b[1;32m   5203\u001b[0m         \u001b[0mind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmasked_invalid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"convert_{ind_dir}units\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5204\u001b[0m         dep1 = ma.masked_invalid(\n\u001b[0;32m-> 5205\u001b[0;31m             getattr(self, f\"convert_{dep_dir}units\")(dep1))\n\u001b[0m\u001b[1;32m   5206\u001b[0m         dep2 = ma.masked_invalid(\n\u001b[1;32m   5207\u001b[0m             getattr(self, f\"convert_{dep_dir}units\")(dep2))\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36mmasked_invalid\u001b[0;34m(a, copy)\u001b[0m\n\u001b[1;32m   2364\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2365\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2366\u001b[0;31m         \u001b[0mcondition\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m~\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2367\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMaskedArray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2368\u001b[0m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_x, plot_y = list(), list()\n",
    "\n",
    "ho_pheno = ho_pheno.ravel()\n",
    "\n",
    "for counter, i in enumerate(ho_pheno):\n",
    "    if counter <= 5:\n",
    "        print(counter, i, all_preds[counter])\n",
    "    #zoom in a bit closer\n",
    "    if(all_preds[counter] > 9):\n",
    "        plot_x.append(i)\n",
    "        plot_y.append(all_preds[counter])\n",
    "    \n",
    "#plt.plot(plot_x, plot_y, '.')\n",
    "thisplot = pd.DataFrame({'Oil':plot_x, 'preds':plot_y})\n",
    "#sns.jointplot(x=\"Oil\", y=\"preds\", data=thisplot, kind='reg' , joint_kws={'scatter_kws': {'alpha': 0.2}})\n",
    "sns.regplot(x=\"Oil\", y=\"preds\", data=thisplot)\n",
    "plt.xlabel('Real Seed Oil %')\n",
    "plt.ylabel('Predicted Seed Oil %')\n",
    "plt.title(\"DNN Seed Oil Prediction\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import figure\n",
    "figure(num=None, figsize=(12, 10), dpi=100, facecolor='w', edgecolor='k')\n",
    "x_ax = range(len(ho_pheno))\n",
    "print(x_ax)\n",
    "plt.plot(x_ax, ho_pheno, label=\"Actual %\")\n",
    "plt.plot(x_ax, all_preds, label=\"Predicted %\") \n",
    "\n",
    "plt.title(\"Seed Oil % Predicted vs Actual\")\n",
    "plt.xlabel('Validation Sample')\n",
    "plt.ylabel('Seed Oil Percentage')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(best_DNN, open(\"Oil_kfold_10_DNN.pickle.dat\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_DNN = pickle.load(open(\"Oil_kfold_10_DNN.pickle.dat\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history['root_mean_squared_error'])\n",
    "plt.plot(history.history['val_root_mean_squared_error'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('RMSE')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'validation'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "range(0, 189)\n"
     ]
    },
    {
     "data": {
      "image/png": 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73d3dfPKTn2TJkiVs2bKFG264gWq1iuu6vPGNb+T3fu/3jvXuOH4JPVbmMuwo9OcjvKBD6Asddzy4ix89sOuotxOGIaZpNrz2qxefwGUXnjDt54aHh/ngBz/Il770JU477TS+9rWvce2113Lttdfy9NNPc+ONN7JhwwYAbr/99uRzn/70p+np6eG2225jbGyM17/+9bziFa9o+h2PPfYYX/3qVzn11FO5/vrr+dKXvsQ111zDqlWr+OIXv4jjOJTLZd7whjfw0pe+lFNPPfUo98bR4bj10OsK/dhd8H4g8cMO4cwH/CAEIOjs3w5a4JFHHuGMM85I6l5+4zd+gyeffJJyucyJJ56YkPlk3H///ckA7b6+Pq644oqW33HBBRckg7nPO+88du3SN7BarcaHP/xhXvOa1/CmN72JQ4cO8dRTT83lzzsiHLcKPbZajpXlEkpFECqCjoKcF3gdy2XB47ILZ1bR7WA+Cnfy+fycbCdd9GiaJmGohcYnPvEJlixZwl/+5V9iWRZve9vbcF13Tr7zaHDcKvRYmB+roGisIOP/72Bu4SX7t0PoHTTH+eefz1NPPcUzzzwDwH/+539y1llnzXhzuPjii/n2t78NwMTEBD/+8Y9n/d3FYpHly5djWRZbt27lwQcfnP0PmAd0FPoRIiYa/xh6+M9n1IOinRtmB80xMDDArbfeyrXXXksQBAwMDPCxj32MAwcOTPu5d7/73Vx33XW88pWvZMmSJaxfv56urtnVsvx//9//xwc/+EG+/vWvc/LJJ3PRRRcdzU+ZMxy/hK4WBqEHHcKZF3iJh965YXbQGpdccgmXXHJJw2snnngi3/zmNxte+8u//Mvkv3O5HJ/4xCfIZDKUSiXe9KY38Vu/9VsA/J//83+S973+9a/n9a9/PeVyueHfAGeddRbf/e53m64p/V3PNY5bQpfHOCjq+R1LYD6RPAF1bpgdzDEmJiZ4xzveQRiGuK7Lq1/9al70ohcd62XNCY5bQj/WhUV+J61uXtG5YS48PLTlEJnnQeXuokWLpij45wuO26BoeMyDoh1Cn0909u/CQs0LuOHzP6fmza5ZVAfPLY5jQj+2Hnrs8YZSdfqNzAM6Cn1hwfVCpILOqb6wcdwSujzWWS5+nWg6xUVzj06l6MJCXBcwiwFnHRwDHLeEnlSKHqMsiDTRdEhn7uF38tAXFOLj0eHzhY3jmNBjhX6MslxS2RedTIwjx4HhMsWKN+X1pFI07OzbhQDXjwm9w+gLGcctoR/zStGU5RIEnZP8SKCU4rpP/4x/u31qD4ykl0tn3y4IxE9K/1P4/C1veQt33nknADfeeOOMlaD3338/P/vZz47ou9K9ZY4WM6Ytjo6O8sEPfpBdu3bhOA4nnngiN954I+Pj43zkIx/h8OHDWJbFOeecw5/+6Z+SzWbnZGEzYaEERaGjIo8UY0WXofEa5ao/5W9up1J0QSFR6ByfjB4EAZZ1ZFnaH/nIR2ZsJ/DAAw9QqVR4yUteckTfMVeY8RcahsHb3/52Nm3aBMAtt9zCxz/+cd71rndx3XXXcdZZZyGl5AMf+ABf+MIXePe73z3vi4Z6peixTluc/N8dtI/t+yeA5tWgfpzl0gk4Lwj4/lSFXnz0LoqP3HHU2w7DkPFJ7XO7z7uM7nN/ZcbPrlu3jne/+938+Mc/plar8YEPfCBphbtu3Tre8573cNddd/HSl76Ut7/97fzFX/wFW7ZswXVdNm3axHXXXYdpmjz99NNcd911VCoV1q5d29Bo6x3veAfveMc7eNnLXkaxWOTmm29m8+bNGIbBhRdeyG/91m/x1a9+FSkl9957L6961at45zvfyd13380//MM/4Hketm1z3XXXcf755wPw13/913z/+9+np6eHiy+++Kj3YYwZCb2vry8hc9ANcb7yla+wevXq5DUhBOeee27SJOe5QL1SdAEo9A6hHxF27IsJvXH/KaU6WS4LDHUP/RgvpAmEEHz729/m2Wef5U1vehMXXnghixYtAnS3xG984xsA/PEf/zEXXXQRf/7nf46UkmuvvZZvfOMbvPGNb+SDH/wgb3nLW3jd617Hww8/3HJy0c0330w+n+fb3/42QghGRkYYGBjg6quvTgZjAOzatYu///u/5wtf+AJdXV1s27aNd7zjHdx1113ccccd3HHHHXzrW98im83OqQie1TOIlJKvfOUrXHbZZQ2v12o1vvGNb/CBD3xg1gvYvHnzrD8DdSI/cPAQg4ODR7SNo8H2HcXkvzdvfoLxg5kp7zkW65otjuUaBzePADA8MtqwjrRiP3Dg2Bzf2SC9vooryToGYgENUp6L/bdlRwUApWTS20ScehG9p85fU6r4e2bCq171KsrlMsuWLWPdunXcf//9XHrppQC84hWvSLbz4x//mIcffpgvfOELgOatgYEBDh48yNatW7niiisol8ucfvrpnHbaadRqteSz8X/fcccdfPnLX6ZarQL6hlEul/E8D9/3G75r586dDTcG3/fZtWsXP/vZz5Ie7LVajVe/+tX84z/+Y9Pf63nerI7frAj9z/7sz8jn87z5zW9OXguCgGuuuYYXvOAFXH755bPZHADr169v6DncLv79pz8EYNGixWzceP6sP3+02DayBRgH4NTT1nLOaYsb/j44OMjGjRuf83XNBsd6jf98lw46dXX3NKyjXPXh3/cC0Ns3sKD3Y3oflqs+v3fj7bz3jRt46YZVx3hlGnN1jEfDncAICmPOe5cfbT/0fD6ffN40TTKZTPLvxYsXN2z7M5/5DGvWrGn4fKmkB80XCgWE0HkiQgiy2Wzy2fi/DcNo+L4YjuMQBEHyuuM4XHLJJdx6661T1mvbNo7jNGxbCNF0HziOw3nnndfwmuu6LYVw21kut9xyCzt37uRv/uZvkh8dhiHXXnstvb29XH/99e1uak5wzNMW/Y7lcjQIQsnug/opJ5xkuRyvdtbQWJWaF3JgpD1leTzBC+qFRQstdTG2VHbs2METTzyR+NSTcdlll/G5z30uGVIxMjLC7t276erqYu3atXznO98B4NFHH2Xr1q1Nt/Gyl72ML3zhC8k+GBnRT5ldXV0Ui/Wn9he/+MX89Kc/Zdu2bclrjz76KAAveMEL+MEPfkClUiEMw2T9c4G2FPonPvEJNm/ezOc+9zkcxwG0/fJHf/RHmKbJn//5n2M8x4+Yx7xStCEo2snEmC32Hiol1srkoGhDFe4MhP7o04ep1AJesH7F3C9ylhgr6kBa1X3+9TtJCxilYAE5SoRhyGtf+1qq1So33nhj4p9Pxoc//GE+9rGPcdVVV2EYBrZt8+EPf5g1a9Zw6623ct111/H5z3+etWvXcs455zTdxnXXXcfNN9/Mq1/9akzT5OKLL+b666/niiuu4Fvf+hZXXXVVEhT92Mc+xh//8R9Tq9XwfZ8LLriAc889l5e97GU8/PDDXHXVVUlQ9ODBg3OyL2Yk9G3btvHZz36Wk046iauvvhqA1atX84Y3vIH/+q//Yu3atUkO5QUXXMCf/umfzsnCZkLSnGshVIp2MjFmje37tF010JOZVqHPNFP0a/+9jZFibUEQ+mhJE3ql9nwk9PpxkEohWDiM/ra3va2hj3mMLVu2NPy7q6uLG264oek24iHTzfD5z38+sUN6enqa9jtfs2ZNMgUpxkte8pKWaYzXXHMN11xzTdO/HQ1mJPTTTz99yo6J0er15wLHPA89pVg6c0Vnjz2HSwgDTljWQ6nWmIc+m5TQibJHqUml6bFArNArtal59cc7GhX6wrJcOqjj+K8UPUYnlx9ITGEk/93B7FAsexRyNhnHnKrQG+IT09tZ42WXYsVfECQzVqwBz1OFnjrHj1HYqim2bNky50Ha4xnHLaEnI+iOkeXiBSH5rH7A6Vgus0ep6tOVczBNY4qHHpOHbRrT7lulFBNlDz+QSZ70scRY6X+Ah66OXSLC/zQciUg5fgn9GI+g8wNJPmsn/91Bczz45EGGx6tTXi9VfQp5G0uIKQo9DopmHGPafVvzwuTvpcqxtzliy6W8QCwX79BOclvumJOnl5jQa77C9xfG73u+w/f9WbcrOG4J/ZhnufiSQofQZ8TNX3yAb//k2Smvlys+XTlbK3Q5WaFr8sjaYtp9O1Gue+fNOjY+1xhbYEHR8pb7yW6/D+lWjnpbcVB0cNsEhw4dQnZU+rxCSsnBgwfp7e2d1ec6M0WPEF4Qkostlw6hN4WUCj+QHBqZSiilqseS/hyWOY1CtwXutIRe77exkBR6dYEQekzkyqtC9uh8Zi8IsS3BA1uLXHFhdU4TIjzPS9KhFyKO1foKhQKLFy+e+Y0pHMeEHv//3BF6uerzJ5+9l/ddvYETl/dM+14/kPQUHIQwWgbuvnTbk6Dgzb925pyt8XhCbIcNjTW3XLry+iKZHAepK3SDsnd8KHSlFOOlhZXlIt1q9P9Hr9Dj8314vMZQNcvFG04+6m3GGBwcnFINuZCw0NeXxnFvucylQt8/XGbb7jEee3poxvf6gcS2BLbVaAsM3/Gv1HY/CcCdD+5mcMuhOVvf8YaYqA9PInSlFKXIcrGEMSXXPA6KZpzpLZfxUprQjy2Jlqo+Qajozjt4gXxOntq27xvn9vt2tvx7TOTSq824LSkVX/nhluQpYzI8P6S3oFt01J6HQd/nC45bQp+PoKjraWV4sIlFMBmeH7Ku+ijn2LuTPHTpu4z//FuUHv8ZNU9yaLSK+z9wSvqT20dQSiXe+Gix1kBwNS8klIqunM0Fu7/MC8WjDZ+PA3Cz8dCPdS56TITrB6oI5HOS6fLD+3by+W8/1vLvCaG3odD3Hi7xb7c/xf2P72/6d88P6S7omFHVbZ1RJKXiqZ0jM35fB/OD45jQ5z4oWovItx1C90PJGcV7ucR+LEmtC0ujAAQTQxwc86NtHvt0uucST2wf5oOf+imbnx1OvHGlYGSirhJjv7tPlFlUepo1ovEpJib0jG0QTJOHPlF2EcLAMo1jbrmMFV1yhscbK1/hxZktz4nt4vohrhe2zGKpWy5TLa/JiPdfq1iE50sytkXWMZPrpBke2nKI//t3P2XfUGnG71yIWAj1DEeD45/Q5zAPPSbfQ6NtELofkg3LLDNGk2EMdUIfrhP6NGrm+Yin94wBUKn6DTfbtI9eqmry6C9vByBP42N+rMoztkCqqc27YkyUPXoKDl15h1KTqUfPJcaKLl1GDYHkZOvwc6LQ4ydKr8VTjPRSQdEZEBN5q/3oBSG2LchmrGlFSjx9qlILcP2Q373hNh58cm76lMwn/EDymW8+yu/ccPsxFwdHg+OW0GOumMtK0dgeaZaVMRkiqCJUSNbwcNwxAILiSPT/dUL/n2a5JEMrpGrwxg+nbpIxaRRGnwYgZ7gN5O/5OqPCMqevxI0JvTtvH/OLcLRUI2Po33WCNfycpC7GxVRuC4KVtfYtl0ShtyJ0PyRjm+Qca1oPPbZApVSUqz4jEy67DhRbvv/z336MnYea+/bzha/fsY2fPbK34bWb/ul+vnfPdsaK0693oeO4JPRQqmRyylwq9PjCKFb8GR+Zs2G9RWqhphVIrNBlZYKhUa2KvEC2tIWqux6n+NjdR73uhYR4rFwYyoZjkw6MajWosA7r1LeC4TaocD+QOJbAis7OVtWiiULPOcc8bXGs6JI39RqWmEWqE+Pz/p0zEnqkzNsJisZB5VaxCC9KAshmzGmfPuKq3zBUyfFvJWqUUvzXT55l276Z1zeX+MG927nzwT3Jv4NQ8tBTh7jorGVAe0/oCxXHJ6GnLvC5zHJJP0pO56Mrpcir+t97XE3oQUToAO7EOCLq9dLqhJ4YvJ3Rn/5Hw2uVmn/clo6HUrErNSc0rdDTlku56rFEFDEqowRmlrzwGt7rBRLbNjFnVOhupNCdY67Qx4oui3L1czE4tH3evzOONbj+1PNFKYmaRdpiqU2FnnWsaT30+NoMpUzUerXVDSfaXc1mys4nqm7YUMMQX/dnnjQAtBdDW6g4Lgk9ffHPZZZLmtCns138QNIt9MUSIuj1dVAvLNaj+12qzMkre6ZsNw0V+FM6Hd3yLw/yt1/95ZH9gGOM/UOlxM8NQ9lws21Q6FWftbbOpiguOpuMERB49QvM88NIoWtCb9XNcqLscTJ7OVVtP+Zpi+Mlj/5s/fcawzvm/TunU+jKqwFRam8bHvrMlotkWfUZem1/2rhQ/DQllUqOf6sbwLGq9q55QUOGVGwh9RQyDPRk2rJcFyqOU0KvnwBzeTKklfTBaR67vEDSY+jHxMPWCvqCw3otpVFErhuAPlHhjBP1Hb/VCa3CAKUayWq0WOPJHcdn2teOSJ1Do4cujElB0YrPCnMMI5On1nsCAH657lvqHP+6Qh8t1njfJ+5i14H69qVUWNURNu77D84ZvfOYpy1W3YAuSx/nsnSwxxrzw7/2463c8i+/mNPvrCv0qQSbzmwJa+0odE3k5SY3xlAqbFnl7Ge/xLneQ1SnUeiiPMI7u35M6FaTa7OV5x7Hvya3fphPBKGuEUgTevxEnMuYLO3PdxT6c42wQaHPrYfeFbV0PTTSWtX4QUi3qKIMwUFnDb1yDOm7BKVRMitOA3RK3imrepPtNoUMQDb+LQglIxO1pOrweEIcEIXIQ4+OzeK+3KQsF58ldgW7dynK6QIgqNQJ3fNDHFtgRmfns3vHeXbvOE/uGE1tw+O38j/HlB7ZYJyaFyTWTG3vVvyRffP2O5uh6vrkhSaGp4Pl5Ip1j/aZPWN86baneGTbzAVrs0F8XjV7AkzbLH51NkHRqTdGPwhZJEoYKJYEB6YNimbHt3O2sxcmDtUJvZXlkmSqzbi8OUO8llLVT3gkFlzZjMWygcLzm9BHR0d5xzvewSte8Qpe85rX8J73vCeZo/fwww9z5ZVX8opXvIK3ve1tDA8Pz/uCoa7QbUvMuYeedeK7dOu5kL6vLZfQ6WLCWYZA4R3eTVgcwR5Yjm/mGLAqLOrN6u22eERVYQCTFHpMSmm1e7xgx/4JBnr0b0576MsXFShW/IQIShWfAbOM1bsEMlMJXQdFzcRyGR7XT0NjJf3/Ox4Z5OCX/oR19n687pWY0idveAkZHf7upxm5+6vPwS+uo+qG5ISPYTkcMJaS8ceRvs7e+dTXH0FK1TCJaS4QN8xqJhjShB62k+USWS3lqj/lmvJ8yWKhj0+/u3/6GE+ot6OC+nZaKnT53Cv09FomoptYfH3mHIulA1p8tEqVXeiYkdANw+Dtb387t99+O9/5zndYs2YNH//4x5FS8n//7//lIx/5CLfffjsXXnghH//4x5+LNSdE4djmnBcWZRyLZQP5aRW6F4T0GDVkpofx3EoAqtsfQboVzK4BSkYXi60qWcdKttsMKvBRkzz04Dgm9H1DJU6K4gZphb66Fy7LbubQqL5JlioufRSx+pZgRE2jwmpKoUc5z7HlkhB60dU90L/zCUqH9vPv5Rfgn/XrgH4iim0DWS0hq89t6lnVDcgZPiKTw7B0RaUKA57ZM8bTu8dYOpCftgjoSBAHQ5tbLprEy9JpKygaWy1STe3n7gchi0xdKGTLGl3B6JTPJwj0k6UM/SS+ldR3fOfTjN3/X/U1zvFMA+/QToqP3jnte9K/LbZdqolCN1k2kCeUiuGJ5zbzZq4wI6H39fWxadOm5N/nn38++/btY/PmzWQyGS688EIArr76am677bb5W2kKMaFnbHNO23jWvJBsxmRpf27a1KU4KKqyPfjZfobppRSlH1pdfYyrAv1mmaxjJttthmYKPX762LFvguKjd7L/K382Fz/tOcGLvHs4N9wMQCBlonLWspOr8g8xukNPQPerJRx8rN4lGFmt0NOE7vuNCn0o6qc+VnQpTZToMmr81D2De921FJboVLN+UUlsA+nX2qqOnEvUvIAMHiKTx4wInbBuA61cpG9cfiDZ/MwQV//x947K91dK4bah0MdkAem2k7boJefr5MCo64csEkVUNEd0JYdaz3oNomMQBCnLRRNmZesDuHvqXRrbVeg790/wm9d9d0YrZOKhHzJ02+enfU9aXMWEHqv2bCTm4PjNdJmVhy6l5Ctf+QqXXXYZ+/fvZ+XKlcnfBgYGkFIyNjY212ucgvhEydjmnOehZx2LQs6m4gYt1VSa0G1T8KxchT+sCxXM7gGGvCzdlMlmrGi7LRS6DKYo9Lhz447941R3PpE0+joecIZ6llW1ZwCtuuKbU39G/6byQe0rWxVt2dm9SxERoatq3eLygshD1/ySKPTRosvIwQMAbLxgHTe884WccMpJQF2hKyVRXg3ptrbM5hpKKWquJnTDyWPZdYUek1Y2o3+M64fs2T/MWrkt6Z9+JAjCehZJs7TF+IY2JvPgT39zC6WiXPNZHt10Jt9ofF+yyCwR9K5GCpsTrOGWNooR6s+qwE+uzZobIr0aslbSmV3xGid56D+4dzs/uHdquufOAxO4XthQnJZG1Q24+YsPUJqYQPkuSra2ttL2Z6LQY8slY7E0IvTjNdNlVu1z/+zP/ox8Ps+b3/xmfvSjH83JAjZv3jzrz+wb0QciDFyCUDI4ODgnaxkZGyfrCIaHtP/3wC8Gk2rFNHYcrHGuUeNQLWSsPMyYt5SLsk8A8MSO/Rxws1xo1pj4ynW8NtfHlm19dDO162KhWMIM/Ib1x4/PO/aPM5LfiR0Ec/b7muFot/3YjgqnrsiSzwgsAkKviGHAnr37UDUdBHTHNQkP79zK4OAgZnUIMrBt3xDPFn0GlGB0705Go7VMlCoUbB9L6IyhQyP6cf/A0DiPP7SPM4Cy75Et7eaRLZI+Q9Avyjz2xFbM0nb6gVpxgn/4yt1sOLWA3eQYziXue+BBpALlFilbDl6gye6xRx7m6YksS8U4lZImigcHH6byxEP8XtdPefIXZ3Bw+ZIj+s5aqq3ws9t3MZgfa/h7ZvtW8miFrrwD0x7niitRCrKmxyJRZM+P/oXxMzagHL3mvcMey0SJcVbiZBZzgjvEnfcMsnJgao/walEXVB3cv4/d8ikAiuUqj/z8bnqB8dER9sbHuaLP9UAqBgcH+c7dhzAMWJoZwRrZhX3gSapnvpzHt+mb85NPbcEdm9pdct+Ix88fO8SLlu7jBOChX9wPVqbpb92yt35ze+yJbWT9/Wx7Rj8dPvXk5uRcefjxZ+gTh0EGGF51Xq/BuUTbhH7LLbewc+dOPvOZzyCEYMWKFezbV88kGBkZQQhBX1/frBawfv16MpnmO78VCjtH4LZD9PZ0cXhijI0bN87q82lIt0pl+8N0nfFC/t8dd7B0cRennLSIOx7ZzNnrz016dqdhPvos5i8Vi1efwkpvOT/dNQ4YgGLVug0c/tFWADLFfZxm1xhdsYqNG0+bsp3NPwoRUjWsX351b5IVkkGiUEf1+6bD4ODgUW17ouzx0X/7Ab//unN48YaTefL7AZYpsU3BkiXLOPnEfvjJMMv6u2A/9KgKF1xwAXf/pxYD57zgUuSuKuVHMwwUMpwercX8wQ9ZtnQxpqlv3K4fKT3foL+gg65nn38Bq9adAcCOnw/QV6vgLFnJeWcvYtd/A4HL9x8c46x1p/KCC1Yf8W+cCYODg5x51rnAPvIW9C5eRjc9MAJnn3Um4pmDbOz7Ng91vZFHybLujLPYsuNxAE5es5LTzj33iL53ZKIGX9fX3+Kly9m4sbHn/nBxG+NbYELmMFXIBeefh2E2v9x1I619nHnaak4fvoc1+x7HGLqfZa/7Q/KnXUD26cM4D5QIVpxM3pJkH/sxe7qXs3Hj1J7od973I6jCkkUD5E47HX48RKgM1q1ZzgGgu5BnXXScD41W4Fv7CUN9jv/L3XeRsU02btzIyJ1PMrZrkFN/9Wq2jnQDY5x66ulccMbSKd+ZfXYYOIRbc8GC8846A6t7oOlvLYs9gE7e6F+0nI0b1/H0yBZgnBdcvBHLFCy+bRiR6WXjxgsY+/m3GLrn65z6f/8Vw5hfYdAuXNdtKYTbslw+8YlPsHnzZj796U8nkzvWr19PrVbjwQcfBOCrX/0qr3zlK+doydMjDhw6tolSR1ctOvHQ7Rz6xscJSqNJlkvsJbYaPByWxwAwC73YlmAisMmsOAVMi6GqySPeCTx5+pvIn/VicobX0kM3VIhBY079RutpLluiK0/diRFQcsF2gEu80VqgbRIjwJI1TFNEHrpet+FryyRTG8L1QvqMEqFwELkuTNOgIh2UW+/O503KcolRrvpURrXq71++LHnd7llMv1lmvORGBTVgSh+BZOvuaQJ4s/mte57CPdA4Su/w9/4B++CWJNBmhTVEJo8dT7eRAUS/64SSbhHs+iEqskDCNkryW8FLnZvNPHS3UqKmbKpKr2W64qLys49xmnWAFYvydIkaoZXHsBxKj/9Uf9fEEKahED1L6DvxdBwj5OCuXU23lVguYdCQthhMDCWvx5jsoftBmARSVeTDlJ+6P4mNtCoijG1Kh8i/n2a/VptaLkHUO0jTYTrNNigOI/wqKlwYQ0tmwoyEvm3bNj772c9y6NAhrr76aq666ire/e53I4Tg1ltv5YYbbuDlL385v/jFL/jDP/zD52LNCVFkIuI9mkyX+CKVru5dnnUsMvb0wcywMgaA1dWPZQqCUNG14eV0nX0Jh8dqKATWktVY+V7yhtfab1QhIhUUDULJr2Sf4Dz3l2w6axlGTHJqYaZQJXnfXkCt6mIaCiuoYZmG9tCjC9CICKwrGKVY8RkQJYLcAIZhYJmCssokxAdxcy6ww6kXZnlkmFAZ5Hrqsxat3sUMmBXGS14DcZ2+PMu2XWNz8luHf/j/GP7h/0v+HVZLFB/+b+zDzyQ3NhERupPRJKrCICGmgeI28oarRUJ0gwvbCFa2QprEmwkPr1yiKm1qSqvy6QhdPfh1XpN/iOWLChQMF8/pwe5dkgSq1bi2C82+ZWQWrQCgeGA3w+NV3v2xOxoysoSM/Pew3m1TKXDHdPFdmhgne+gv8u9lU+0e/T4ZFWltuY9iRLytWgTE6Zu5qA5ATUPo8bHqytn1oGgUO4uRdazkhhlvS3nHR13IjJbL6aef3nJ+4AUXXMB3vvOdOV/UTIiJIibeUErsI6yR8g7oIIzyatS8kIxjJjeKVgVBqqxPYKu7Dzsq6sytfxm9G65g6L+13dKTNxEiT9bwcVsQulAhhqGDaoZhEASSjBHg+OO881WnU/pCdFLJEEOYR/T75hPJJHgvxK1owhDSwzb1zSnJ5Y3auOapsWvXAQZECatX2yCmaVBRGUgFMT1fsqK8ldVPfoMMv4GPxesHnuT7o6cRFEeoiAKGUT/eVs9ieowy46Ua0qvbd+ef1MV/PjhGEMpEfR0ppFclHD2YHCvv0A4AjMCNOisqDL+GyOSwo6BoGASJIhUq5Hxnp95nfpTa14R4br9vJ2uWdXHWyYumXY87g0L3q2VqyqYWKXQ1XdZPdYwuI2BxX45h4eGKLkSuC1nVN1lV1GRs9y3F6tdWhjdygJ89so9dB4o8/uwwJ63Q6apCRnnoMmhIWPDGpir0cJJCP1HuhTCipIjl/aE9IHUMptWTeJzf32WFIKe/ecXiaml/vkGh5zL168uyBH41amEQHSPpVzHpbrnd2cA7tIvS4z+l/1d+e85tnOOyUjS2XOyoHd+RWi7Sq+KP7E/+200Iffr8cWqa0DPd/cka4jSuobEq3XkHxxKIbAFhKIIWpdcm0YUYKXA/kDhGgO1O0GelTsppovbHEolCdwPcWn29XcLT3fbi4+JXUdGptvXxp+gXZbqXaqVnCUFFZTA8TegyahlQCCYQ0qdb1FhpjvJSBtng7MAJSrhW48Bjq3sRFhKvOEaQWsepS/U4uJ1zkNMvfQ9ZKxGWxgDwDu4AtJ1U8wIcAgwUIlPPcvFdLyEwKSw2OttxvRAjztX2orz6wdsJa/r3//P3HudH9ze3M9KYidDDWoUaNi7TK3SlFKZbpEvUdKMz06VqZBHZLmRNE7pROkyoDJy+JZiFPkLh0BWOcdvP9T5IZ4QkhB6GDdelH1kupBV6nIcevc/GQ0TXhAoDDEvfjJYXdaZXq1TJWKHbKrJm3Cr+2CEO/PvNU3531Q2wTMFAb5bxqEFXzQuSjDSgYaykTBT63OWll7c+wNi935yX6/r4JPRU2iIcOaF7B3cSNzByo/LotOXSUqFHJ4mdLySEHp8Ah8eqLOnLASAymniapdAppTCJTtDoiSMItUI3kHiH6tH8yamNCwVeqjmUV62f8HnTJ5Cy/ojsVQl6NIEPPfMUBeGRHdAeuGkalKWDiFR8fNFaRpQbbPhkoz7jK8wxekSVwGkc4G31RJPRKyOUxuvkfcIiTazbdo81XX/VDRidqCXkqGTI+C9/zP5/u5HDP/hsw3tVpKq9w5ps3YP6yc4IXGpumKxRZPIQBR9lGCQE5vWdxApzFM+XiCBSfV6NYOwgQ7d9jsrWBwhDGc0mnfl4N3joLQqLatLBI/LQWyh0WSthqJCsEZC3JAXDpaIymLnu5CYjKsOMyQKZjKMVZfdSFosiew5pwk/nbJsJoQcNnreMGtc19dCj88TBTyxIJUPMQi/O0hNY6ut011bWqufrWJQVEbpfq1Db/QSVpwfxhhr7nte8kFzGpKfgpPLQQ3JOI6HHolH59WM1V0hsp3l46j4+CT2YbLkcGaGng1xetYJAcuLu75OV+gRt3SXRI1ACx7GnEPrQWJUl/ZrQzagKUjWr1JP1EzvOm/WDEIdoMMb+p1PvXZgK3Us89BAvpYwLwicI6paL8iqYS05EKvgVHgAgu0ZnqFhmpNClr/vhRJ8xVTRX1PDJGvrCW2GO0iuqGPm6fw51QrdrY5SKdS++zwnpzjts3TU1MFqqeLz5T2/jd264nXf8+Y8IpWL7L37O8Pf/nuquxyk9eldDPrOKCmZiQvcO7AC0Qq+6PrlojSKTw4gS6GUYJF4wuR5tv3k+QkYK3Xcpjek0v117RyhWfJRq3f89jVhs5DJWc+HhV6kpm0xOZwW1qhaNnzgAqBXJ4lIKM4hsAVktoZREuEXGZQ4nut5yi1ew2NT++kBPtqGRnakispJBw3WpyhGhN8tDlwqlFBn8ukKXAYZp4Sw/lWXqMKBaluN7QRg9IUX/rlSS4R6TpzVVXa3G04SuX6uTq22JetfIWKH7R+ahS7fK2H3/1dCET4UBhmnPS9bMcUno8Z3/aIOi7oHtEHmxXrXKCnOURXt+gnVQ55S3ynJRfg1PWWQcq4HQlVIcHq2wOFboMaF7Uy+mtFJJLBe3RpzY4e5/JvXehUnofuKhB/i1lEIXOiCmlZdCuRVyvYsYkwUKwmNkzSVkV2tCN01DB0XRJfvxsTTR+6dg+uQi9bvaGqVg1LC6+hvWYfVovzkXliiO1ytOlVdj7Ql9TRX6+HiRa3L/yUVLKowWXQ6NVDj4rK5k3bP65ajAwx/V2UZKqeSC9g/vRoU+3pBWjUbgUp2s0EWk0H2/7qHnejANhVerYcaBw8BlInqiGB4tJT262+kjEiv0noLTtLDI8Gt4hoMZpQS3slzCVA9/f2Q/AsV4qDOQiHqqC69ESWVxonM9t2Qli0WJpf1ZLjprWYPlEit0wrqHnsFLAuMqJWSSboth1G/dCOpJAmEIwsReejJdRo1eo9KyotTzZTItCrRCl9HTxWRlXYsSH3oKDq4X6oB+9FoM2zITgRZbLa0Uurv/GQ58/daWxUyVZx9m5Mf/nFh08b6hRQrp0eK4JPTkES1W6KHi54/tm/WkEe/gdqwlun1rUKskF6UVXXAtLRffw8PCFAZ2pMb8IGRorEa5FrB6qa5+jAnd8JsQepBW6JHlknosdg/UK+bCBa/QA/zU2vOGHlihg9UhyJBcdze7wsXsDBbRd+lvJ++1hKAiNemE1eIUhZ4362SZNXyEAZm+xoChyOn9XRA1xsfqk4KkW2bpQL5hQHXyt/IYK60xNi7Wx2bPoSLB8F6KMsvD41Hg77C2vWJ1rl/bhXd4D8hAty4IalrhpQg9zveWqW6aZkE/VQTVcnJ+Kd9NGmfVam6iGNsZ+BCLje6ImCbDDGsoOwd2tG9rFX5w7/Ypw0KCKAUXwB/WN6lx38aMWzLUSlhekaLMJuLF7l+OZYS84/LlnFd9AL9STFI3TaX/30gp9D6hf6PZ1d9w3kupWCSK2ARUy2WEoRDEaYsBhrAI+/X1ucYaQba40fl+/RwBfS37Ff2kNvlGVnNjy0Xvl4mypy0zx0IFPtUdj83KQ69sG6Sy5X7CcvM4jYrjJaknJK3QO4SeIL7o4xMslJJb/3UwCdK0AyVDvMO7OWzr9gVjoxMpQtcHoeU80MAliIJNllWfqhPnPK89QSvImNDNYKo6ClOPnrFCD6r196VtGhm0WMcxRpz/W/NCglQKXs6oB0WTfZor8APnFXy68ipOWl0nZNM0qCntdUu3kqi6mBjyZtBwsQIU+hsJ3RAm0s5TMFxKKYUu3SoZ22zwm2OE0T5dHMVX9xwqYZUPcjDs5b49BmDgH9oN6Bs46GEm3tBuvMg/z554NoYMcWvVZPycyOSTjCSVynKxI0KXtXISvCPwCKMnG69WqxN6i4EeabgNCr3x9ykZYikfnCwiCiweODjC33/jUR57pt7CNwwlP7+/3loifuoYca2kr39YKWIHFapGLrEIrH4d/zj5wI9Ztft2LnaeSVS6lbJc4j5L/aZWy3b/8kkeOlzb810uzT5JuaiPWxxXkmFAiEElvwKpDNZYw4lCHx6vTplwlbfq+yB0qxw8qH/n0OFGu63q1hU6aEKvetpyKT91H/u//FEKYbFO6LGH7jcndH88SscMmlsy8Q0sHcPoEPokBKFEIFm9/w4cfPxAEoSypUXSDCoMQAZUzB6kguJ4qa6ywojQW20v9AgMTUK2FSn0ULJt1yiWaSSTiuKgaDNC99z6CSAjSyVoEbgKFyihx82hal7YkFOdixR6EEryIvaWC6xe1su6kxY3pBBapiCMT0NV79AoImLIi8hDT/mNvUvqRUUxjGyXDuiVSrqYxhBIt4ITEfrk4iwZKeec8Ontcth9YIJuf5hRo4+iZ6C6l0xR6IdYhPJqjP7k3xGZPJnlpwIQVMr02FHOfSZXV+hhkKTfWV0RobsVnJj0ApcwOuZezUsIfTYeek8ThR6rSuHkcRwLT1nUSpow47S98ZLLRz73c3Zu35M03Yr7EQ3XTIjK/oPR/RgoqkY+2b7drwPc5Sd0zvgZ9v7ER7ciqwwZTlHoVv8ykPUeSTIMyQufXlGhWowCsJGHfni4yLP7y4zX4GDYw2pzhDBU+IHk/7vlx/z3A/VMIM8Pk+EioPP7VWS5pLOeQD9N5jIWvV1Osh9qrn4tzrvPUiMI9Dkzk+USTsSE3rzRWpLlNEWh203ff7Q4Pgk9UJxgDbFs54843T5IzQt4UWYrTvlg+xuJA5ESPCwqpTqhG772slsFRUXoEYqI0M26h7511xgnr+xNSF5ktJduNSmQ8Wp1Qg+jiz6uHFRG42GRC9BDLz35c7p26Qva9QLCVOFF1vAIpR4SXEgp1w/89gV86HcuatiOaQpkRChKhol/bEZea84M6LZDRK6bmqVvlD2Lp/Y/EbluCsJFejV8w0Fk8xGhC6SaamPI6OlCBDVWL+1mz+4D5A2X3tW6nH3MWoJ3SJOGjPzz3aH+XhUGLHvDhzAjFRvUygmhiEyhrtDDMAl+m3m9duVVsKkTemwJeF6d0Nvz0PV7uvNTFXqcVWVmCzi2QU1ZeBVNKPF7P/wP9/DUjhE2nZLD7l0MpoUfZYSUZIZxX9+U4iwR16ynilo9i5I4gehZwqn2QQ4PTegngzgVt8FyKaMwsHqjsv2Y5KL/zxse1ZK2SGKFHgYBnjR4Yscwu8NFrLGGCaXE80OqbtgwAMYLJIWUQpduNWlINsVyiYqIFvXqa3NorJq8Fme0ZIxAnzNuLXl6bmW5JBWwfitC9+trijA8WmKoOLXv/FzguCT0UEoykRKwCajVAn4zfz8rR9ufxRn71n4IrrKpVSpJYEV6VTKO2dJDF6FHGCl0K7J9XC/k6T2jid0C2goIRLZpxaPbRKEnbU57tAKNc7fDBUjo4w98l8W77wIihZ5kARhkcCMPXZE3I6LL5unOO8mjbgxLGAmhI2XyKG1ElkvO8OmyQkQmT37FSQDY3Y1BUQCn0EPBcMkYPtJ0EI4m9DgTarLtEtsBZlhj9dIu3MhuWHziyZy0oocdtW780QNI300Cok96y1ny6nez6n9/nNyJ6xFZrVpVrUIhJnQni2HFCt2fQuhGrYhlRL8xrFe2Bp6X5EW3k7boRnNXs03O07iIyM7lydg6iyjOMnG9kCCU7DpQ5PUvO50lGQ+zqx8z35O0tCjJDAfLkVAZjgm9rtANYWL3LcXsHmDxFb9Lxgio7dnSqFJThUW9RhXfLiAcnXGjJhF6zvCoVaKbEPHxD5EIHt02xJ5ggF5RRdTGk32TToTw/JBCdJ55ykT6NYwoEWGqh67tlUW9Wd1ELkq9zGVMZCRK4kwzL2WBNrNclJIE40PRz23RCjke+JFaR7FYoeor5qM1zHFJ6EGoEvK1jJCqq8vORRPibIlIoXuhgacsglo1UejKq5KxW083F1KTBsDKJQUyjsm/fP8Jqm7I2hP6Gt4bWlkcvCmZOL5XPwHqCj066L36kTaIxrOFczzpZi4QjB3E9sYxULhemDxdiHwPWeXqARehpCBS2R9NYJoCqaLTUKYsF1nPQy+YAcLJ0X/mhWRWnJYMkEjDLmiFniFAWVlEJo90q0ngfDKhx6RiBC6rl3azzNTB1KUnncIpq3p5ptwNSuIP7U380UpgkF//K1jRDWXveD0ts2D6GHYWQ5iJ5aICHRQNlZGksFq1uqdrBF5yoQeBz3hx5qDo6M++zviDt+mskKiqOZSKyqG97PuX6wlrZfyoFbFT6CJjGTzpr2RReTu5qPVAfAMo5CyC8hhmoQ8zTgUVJi42+yei/irDugFYYDdWSS56xf9m6WuvIX/K+YQIMoefalCphqz3ZekVFVyrO7EZEtWaInSvElsusv55JXhqxwgHpV6bVR1KCL3BQ/dDckL/pgmZA6+W2JyTlXVsuVimoL87y+6Dkc2SqSv0mND9VEFgM4UelieS3zIby0UGPkpYnbTFGEEoyUR9G2wjxKtEZBK2PzAgVuheqHCVhaW8hNClGyn0Fh66qXxUROj93Vl+++VnsD2ap3n6mkb1KK0cOcObEmD13amEHp80ol8HasNM5LsGjUHBYw3pu3ogtgrpNvSFE6ctWt0DkUJXkYc+PaFbZl2hK1Vv6CUihd7jSLqdEJHN03vRq1j1tluabsfM91AwPH2jtzOITA7pVXCs5o3WYltDBFXWLOtimTmOp0yWn3gC3XmHHVV9M/UO7ySIlJunrCSbw/ND/vobuj0sXoWcCJLfaER2nPbQAwLMejylVs/CMUIPom2bKmTvYU0ukzNR0ig9dhflJ+/F9UIc2yRj65tHecdmarufxB/eS3kiqmQudOHYggfdUzDRrQdcL0z2RcaxCEtjWF39mAX9BGHmunFsi32jHobl1Gez5hqLufKnnE/uhLMQmRxD9koWl59JrCnQWS6xpdBvVqmZXfUbXUxykVDJGV4y99QyomZ0KiRA4AWSgOgGHtRVf7qtgB/I5ElwQuYgqGGGEaGnlLWUqqFvy5K+XJ3QHSvxyeOgddp/b6bQY7tFf08LQg/qT/3JtgJ/XoqK4Dgl9DCUZGNCp17UYoWzSP6P0uL8UF+oGaOeTTGT5WKlCB3gqktO4ZSVvRRyNquWdDV+jZPTDbombctLKXQVNvp01oAm9CDbm/zehYRgrN7bvV9EwSe3ppVovgdHuYSR2s7NQOiGYdT7ssiw3tArUj6nLcuwvFsgnOafjyFy3WQMn7zwMJ2cVui11pZLnNtv+NpDXyYmGDX6cWyL7oLNXlcTcDB+mCDORFFW1LcFtuwcZcKzom1UkvFzQFJYRBgrdIFwMoQIHL9O6EJ6qKhq1DIku6PH/1ZdBQHC8jhhtagVul3vO+SN65awslbGjfzoTL5AxjbYEw5wMOzhQmc7nh8mT54ZUyGrRcyuukIX+W5WLi5wYLiMyHWhAg+JgZ1v3cek3LWGATncQJ6Gqpf+94oKZSNN6PVqUoCc8BrbY8gQQ4VJsNyJiqMI/eT8CFL7yPVDssIHw6CocuDX6lyQusno4DhJ35bF/TkOjWruyGXMxFqLYxx+OuusiUIPogyX9G+ajLpCTxF6GIDoBEUTaMtFX5C2EeK6ccvU9gk9LgTwAoWLhTOJ0Jt5kzEsFYBVJ3TTFHzk7Zu48Z0vRExq+YqTj1ro6gP7xe8+zk9/uZegQaFHBz0+oVasxepfjtd3kn49bG79HCv4Y/Xgc0zooefiGzYiW8CRWqGHYUzoBkZEdk0REWBaoRuyfiEor5KQZctNRAHKAVHCyuY1oXtVHDsqHPMbSTI+/kagWzUssyaoZnXFaXfeQSLAyuh+PxGh+8qkEin0R58eohqlW4qgRl5VEp9cxMQldVA0RGCaAs/IkA+1IizJjH6ijEjQRCbnW6u0RRX4SLeCrExoD902kxtWUEwRemRfZAvdZCwBGDzonsJp9kEoD9erTFWUH17oS9Zu5rpZsbjAvqEyZpTfXyFLoclcgATZbixDEpZTTx9RlotthBSMGiUjn7Jc9D6Mn0xzhk+QHmodBAgVIpW+ljLZmNC9ZN/ISQo9ZwQYTk4/bXvFeltqv4YMPPb847VMPPuYXm6mrtCTn+BYiQqPi6PSWWdyUrfFfYdLfO+HDyb/blVJ2sxyIQySOMtc4zgldEnWiC2XAD/KGIkLNtpCdEG7AXjKJkOQtN/U+ctWS8vFxp8yEWVRb64hIJogIvT4Ivr+vTv4+eb9BCmFHj96xhe307+ME971afw+XVSx0CpFgzShRznG0te5+SJbwFE1wlASSEnO8HQqn9H6VBMipdDjoGjcE8SrIN1qS4WfbCNSkI4R4uQjQo/SFmGq5SJTCl0Ig36rxqoT1wCa0AGws0i3mlzYPhaVml7XY88M4WERKgPDr9AVjiX52cLUmTVxamyIQBjgGxm6lFbPRZlDSD/pFW8a6TbKzT30sKKtlLBawvWChs6gccWnrJXxoy6Jma4uMrYmxYe8kwDoH3m8TuhhlFnS1Y+IFXqumxWLtEKP570WZZZCrrWilI7e935KsRpKZ7n0mfranFCFpDoytiHiqtGMEUCt3u9IBj6GkolCzyYKvZ45E0wKimYMH+HkcJWNFdTJ0whqhBNDeAe3U4uqr2PLZXGK0HMZKyHluCdMfNzL0mmwTB5/dpgP/M3dlIfq10FrD32q5YIMkpv+XOO4JfRMynLxI4VuzUqhRwN2Iw/dMYLEHlCR5VLzdM6snugSfS4MdADWnkaxpCCyXVqhuyE1N6DqBpSrPr7fxEMPXDxlYkcnnBBmw98XCvzRgxh2Ft9wEoWufJfQ0IRuy1qi0LOGPyMZJ2l+qaCokaR7VZBuZcZtxAodIJPPY2RyDYQ+NculfhOVvouQPstWakLuzkceuJVFuhUCN/bQTSq1ANcP2bJzlIGeHFXlkJMVskERuy8idCGQCFSorYNACYRh4JsZCkZMcFlM6WFEgfwk3Y/WeehhJVLAMkD5NW25xA3qyhGhu2X8agWpDPKFQvKEMiR72Bf0saT4VELomaBO6JMVuh9IQlMT3kSYoZBtTegqIn5vVBO6qyxtmUiVdA0dC+v5+XHaYrpq1Hbr6j4IAj38JXp/Lh9nx9Qbl4WTgqJ6QHcO36ivM1ACI3AJK/qpKLbOYstluVUvQstmrESFW2HctVHfGIoq15CH/p2fPotlCRab5SSVtlWWS2IvRdtSSmHIANPuWC4JglC3mQVtucSE7qgjU+jCyZJJdfXTCl3geiGPbhvi9//ix+wfaixUMKIUrJlgZgvkhE/N9Rgp6nWWqz6BV/fcksd/38VTVlJ4Uy9QWViEHowdxO5fStnsSQhdSJ/QsBGZAqYKMaQuLsrhYWZnIvS6Qk8u1Ei9hZUiyHBGy0WkCD3f1aWDkGFARkTB71YeugwIJrRdEav8WKGHZkYrdL/uoVdrAU/tGCEIJVdcfAJV5bDS1GRqJYRuECD0xSy1FyyEQSjq50xR5rCUj4gyaOJUxnzWapm22GBpeGVtuUQKnWjoSlgrI2u6F3o+5yQKHeAxfw0Dtd24xTFA4ey8D0wLu29Z0prAzPewIiqfraKfQkszKHSyer95UWylqhxElOXSb2oiG00RemxDpPufZFKxhdDzMJTEsm2WDeQZ6NfbN8KAwAt5f/cPGChuS97vBVJ3anRySFEXWuMyjxG6ScFQrLizjoV3aBdL7vwzTrIOR6+ZSQwgbp4WF8tNyEZCr7oBywcKLLIqFG3dJqJ1Hnqjh17zQgQSs0mm1lzguCT0MFT6MQ2tbMLIvkgKNtqASghdkc3nGzx0lCRv6Yh4rM7jxknVqEDDtNubg2rm9MXhlsuMTuhtlKo+YROFTujiqnrDrzi4ttAsF3/sEFbfMkqiOyF0m4BQ2Al5W6GLlIqM4SUZHq1gpDz02G6IR5nFhR3GDEHRtEK3c4XkBmCrSF1P8tBlikyCUd0TPw4MxoTuGxmUVyWMFLqPSbnm8/izwwgDLr9oDTVlJ4Ru99cJPVRCDyaRAaESGIZBaNVvSkWZw0BhRvZANuLlRb3ZlkHwRKEDVlAmY+txiQKJUdOkJWv6iaambPJZi4xdv8R3OachUIh9j7LR2Y6x6yEGLn0TZr677v9HlgtAKdT7oahydE1D6HEvnTjroyId7YGHil6hiWzYyybpppODogD5sN4LRUYeujJMPv7eS3jDr54FaBsu8MqcbB+mt7Y/eb/nh9jooHRo1q/LMZlHhC4ytqqi45jLWMm+HBCl5LU4hhU3T4ureIsy25DlEndn7DVKjBu9YIjWlktQf9IEKJY9LCSm094T/mwxI6HfcsstXHbZZaxbt46tW7cmr99555289rWv5aqrruLKK6/khz/84bwssBnivuGgFXpcpWgTtOx6NgWx5eIrsoUCjhGSxU0CNwUrwPVCxopxsUc0HzEm9DYVulOI/MXKBKPFGuvtXRRqBxt6ucQeuhF4eMrGjAKrphn3qlk4QVGllFbofcso0sVApMAcI9QFPVG+tSNr+jjhzex/xylcUkYZHgrCxkwiMYPKF6ksDOFkk++04748UxR6nTTjrooxqXVHxU+eYev+Mp5LoAQKQaUWcGC4zKK+HCsWFajhJOo6VuiGYRAidIm3DHXaojCQVv2cKRHdcCJCz1hRil93liBUTefIphtAmX5Fe+i2SbdRTYKA0i3r6VsRoTtRryFTGIR9J1ASXfQ89R1+u3AvYtlp9G56jV57z2IwBFbfEhb15bBMwZinj8tMCl1EKY1xGXxVOYnl0iuqSASjvj1VoaeESkHVbc0w0PNglWHS150hV4huhNJPbJE4xgL6Zm0rD8PJIVOEPioLmKFHEO238aizZTZjJQTcHQ0iz2asetpwlCET/7uocg1ZLjUvIG/rCVzjqoBhOS0JPamKjTz0iYqHaYRYbVq2s8WMhH755Zfz5S9/mVWrViWvKaX44Ac/yK233sq3v/1tbr31Vj70oQ8l1XfzjcmWS1rttmrkPxlxf+JaUCdnmwAzmhaeN0NcL2C01Fi950apTGamPUIv9OiTvTw+wehYid/t+ikXq4cJ/XRf6EiFBjU86gUHIumrvXAUelgeR/kuVt8yJuiiYNSwCXCMACVsROSn2srVT1JqZkJv8NBDhUCBUsiUshfODJaL5WBET01GmtAjG86bVJyVvvH7o3rEmRndFLKOiWUauMpBulVk1C4ZoOL6jE64DHRnNXGbUb9x08Es9OntGFqhE4agQkIMhAHS1r8hVAaB1fh7HKEJeaBXb69ZYDSt0J2wGlkuVqKCQQdF8Su4ONiWiSkMHNukvydLLmvzhHU2KMU97lp6X3NNsu+tnkWs/v2/pbBuE6YwWNKfY9TVv7kos9MqdCuTJ1ACGWXaVJVW6KFU9BhlXLubmhdOyXJJt9K1UzEEGXnoKr7RCwsFiDBInsbThO4HIbZ0EU4OZTUqdANFeVjfsHfv0+vLOmbi3y/JhQgDHEskKtwI4m6YNXxsXGWB7yY32ZobUnD0NVoJBIbttLRcZLItFyXDRKHHc2fnGjMS+oUXXsiKFSumflAIilGHtGKxyNKlS+vZCvOMIFQ41IOi6YKGyQ3tWyK6oAPZSBZWTOjCxwsko1Hr1ZjQ454YdrY9Qs/36sf4seFRggPP6CwMfLxU2mJM2EL6DUGd+GKTC6hSNM5wsfuXMaY0efeLMjYBmA5m1Kv8NHMfgZRkcGdW6HHetgrrLXcBlbJZZtoG1G0XEeWhAxgTmqyneOgNlot+T5zpYRgG3XmHqrKRXiVql6zXWK0FjBZr9PdExBHdRGRhcf1GLCKFLoPIcjH13yJCd5WdlMHHsCOvf1FPTOhTxVFYHk/GsjlhJQmK9kbNr0S+B1krIwIX36gTRj5rsag3i2ML7jEu4tELr+eblYvJ9S9u2L6zaGWSjTTQk2U0UuhFNb1CdxyTksokqaZV5SDQx7LHqOA7vdTcgJFSrMzjLJfm53UY+LoFQLQWwzAIsDCkn7SYiCuJlVJ4fogpPYSTnULoAEE05DrmjFzGStbQnwmTNMZ4ELQRK3Rfp+K6ygZUosKrXkDO0Wur+QrDcloW/41PpCpE3SrFiodlSOx5slyOKHfGMAz+5m/+hne9613k83nK5TKf+9zn5nptLRGEEjuVtph+3JluQGwa8ckkEYiU2jajYQmxn35oJC7Njgi9pkNF1gxBuhixf1waG6fH0l6rY/jUUiPbEkIPXXzqJ6SIclWVWjiEHqfHmd2LGIkumD5RxjFCPMshs+wkRvrP5uXqUR4s58moWqJcW8EwTfC1Qg9QWFGNgXRSCr2N/S1y3TAxpIPcK07FXrya4o8+xzrrElz/jIb3pp96/LGDIKyGm0ZX3qEcWJFCd/FjhV4LGJlwkyHOwslCAHTVyTEmdGTQkH4XdzB0laVjMClR5wiFYwn6uvW52MxHD8vj2AMr8A7txFE1HFuQceqE7ixeQ1geRQQ1ArOeQttbcFg2kMfASIY6QH3iVzP0d2fYtbcLZZgcCnunJXTbMinLLH2iSqh0K426Qq8gs2uQCv74cw9wfV9zywWgpiyyRpCQthJ1egoxMWSQKOF4dmkoFVIpTBkJB9uCKtSUrbtuAjLy9nOmxBSGnvIUEXqf7dOdjy2TKH4TxISeUuhEFoyd0f1got1R8xXCclq2z/VdL7mipVehWPZYRoiTbS8GN1scEaEHQcBnP/tZ/v7v/56NGzcyODjI+9//fr73ve9RKEwfAJuMzZs3z/r7x8Ymkn4LNqEuG47OzScefZiw7/A0n9awhrbTDUgMRsaLrIleHy77ZIGJof3AAHsPa99t67ansdx9DD27ndOB/QcPEQwOTvsdg4ODGG6ZPsAZe5ZuW59YGQKGU6PSdu7cwYSVwfSruKrAYLTd/bsOcBawf89eqjN815FicJbbtfdvpQt44qmt7K/YkIEBs4xtBIx5AYODg/widzEvUdt4oXcve43ljNsreHaa76lF6WR7du/i2aAnsdPSCv2Jbc8i904dJZdGV6CwgS3P7CAc9jDO/Q267v8yVxd+zh271jE4WN/nhw8fIur9hzeyH2XneOihh5K/G9JlpKIrPWtjQ/jKxBSwa98hihWPammEwcFBrGwOKjAW2sm+3HXYZYkSFMfHwa0Rov82Wq6xBk02bspSkcrAxuf3rljM3v26SdjgQw/TlWsk3O7D+1FODtPKkqfG8OGDbH60Qo+oIjEYlzZ2cRwzkLhqSbKeV2/Mk7Eldz02QalSZdfufdim0fB7J8OvTvDEWI7bz/l9Dg+X2LblcfY4zZ/AdxysUYimTvlYBAgMFTI0NEyXKhPkMrzuhf08/Ji+8ex45mk8t8ChgweS6w6gIjNkzYBnt25hJVDz/OQ32JgEboW9u3awGN2ZdHBwkJovsZAIJdl/eISSq5+wq9LGiwq/wvGDmMCSLsXvXryYxx59GGf3MxSARXaZ17+gm4cffIC++LdHaY6ViVGkNBO77dGHHiTI9VLzQiZGoniBL6n6AeWhw+xpco7LaoW4dnzzLwd5anuGdUhGR0c4MA/X9BER+pNPPsmhQ4fYuHEjABs3biSXy/HMM89w7rnnzmpb69evJ5OZ3d3q3+/9KfZE3UO3jPrFsfbkE8ifcv6M26g8IzjwoFboK9ecAPqpm1Wnn8XwjvtZs7QXntJBU4ATTjyZjRes5qFDe2EnrD3jTE45+4yW2x8cHEz2z/0P38FFI09iRb59xgggFfRatXIVZ27cyBM/ClFWJvncU5kt8AQsXbqEc6LX5hLpNbaL4qMTHH4E1p93HqU7H0QBS50qjhHS1dPHxo0b2TnxNF986lLO6R1j79JL+OMXvmjabX79ngkowuqVK1gt12ANRm1rUx76uRsvToKWrXBw192Uh3dw1nkbcBavBmCktgvjnm+xaNESNm48J3mvu+NpiOpCDBmS6R3gtNS++MEj9+MdzoPUM1InsOjvzlKOyv3Xn3EyGzeexENP3w8jcPo559Effb5r5wgH7hd05bN4XhGJycaNG6nu2wdD4Bs2+Z5+iO5PNSNDt23yml99IT+8fyf84mHOOvucZDZtjF33fo7sinVU/TKFskvfSWu46KLTeOa7X8O3u1m65iQm9j+Og0JkuxKx9crLXgDAtuHH2LJvN339i8nnvGmP/bNjW7l/65NkepZhGCVetOnCqVXQEQo7R9h8n36y8JRJoEwsQhZ158kUfVasPZNzXngJfzn0IxiGE1evomfjRiZ2702uO4CKchigzAkrlxM8DplcPlnjw7dbZC1B1+JFsEu3Ldi4cSNjRZfs13XB0OqTT6W3ZMJ+bfvUIiKOKz+7Miave6U+F8flAYYfh76sydmXvwB//BC77wDDyWFHDcIKtmDEcCLLBc5edxph7yr4yl5OXL0CDkIoDXJdPVi5HGc02Z8P/lBRUzZZw+fMU0/mwdEaYh+sWnNCcr7MFq7rthTCR2R6L1++nAMHDvDss3rI8jPPPMPw8DAnnHDCES1wtghCqT1booKMVB+FoNreGLrEclEGdiqDIg6KTs5pT7q8RalPmXx7lguAe/ZrsAgxkZTJkTEaPfS4yMmUfjI4A1Il5AsoKJp0KRQWNR9cq5sBs4JNkAQlLdNga7CCO/0NbZU4xx66kjpt0TZiD739oCikPfS6hRbP8gwnDUlOD+2Fun8eozvvMO7py0N4RTxl0ded4cCwTtPsj7xuO6fXlRlYnnw2znKJe5LIyAuO7TcfB1J5yDWyiacb1yBM9tCVUoSVCd1EK9tFXriJZbI04zLsZ1F2HhVqjRz79WlkbDNpzjWd3QLaQwfdXjafsVqSOYBjaQ8ddK5+gNC51lHHwzitMR9VfMbBUDXpN8azZUM/ngtQX2NoWAgVJEHGeEShF4RJxpvI5JL6kIqqE3GMdPO+OJ0wrOmnttg/Nwu9UT660mnE0sIlnhHrJgNCYgcqRKCEPW0eeikasSjdCuVSVMcyT5WiM271pptu4oc//CFDQ0O89a1vpa+vj+9973t89KMf5X3ve18SCLr55pvp6+ubl0VORhiqhNBtI9RR6bghW609Qo+DoiFGclECWN3aG9U57fXXkwq1KH0pk585SBdjyQkn83P3dC7KPMtw4RT6S88gUr64DHVFqqU8wlRhRJ3oFiChmxZeIPGcXhYHRUwUwtEnbpxu6flhkoI5HZKgqNQtd+OqSRlZLoZpN22ZO2U7EaEbdiomEvchd0sN7518k5ys/rsLDs/WBOTB8kr4qoe+7iyhjPKXY6+7ayki34Oz/OT6OoRBoCJClyFh5AdaUUqnL5yG4J0rshDlYdutCN2voQIPM9+Lcgp0GaMEESmv6Ql5/HCGh54tclr8gSY3QMc2CUJJpeaTcaa/9OMb1u6Dxen7uKBHQZalfr+vLN2MDIUZxpOT9Fri9MOYTOPisVjBxrNlk+BkykOXhoWQ9XiZGU19csdHeEPhPgCs3qWYjr7hVpWDS+M5Y6QJPU4njIqOkhz0Qh/B6AFt4wQeNZlLLBfpVakm8Qd9nCQGoWFjBmVU4DP831+k/6VvxCz0Uq76CBkwrvIspoT0qpQrUZbOsSL066+/nuuvv37K61deeSVXXnnlvCxqRqj6ZBTbCDFTKU/tEnqsiiUCJ0XoZkEXCkxuIxCnkcUVY7lZEPqKxQW+UbmY/66t5/eXH8IpBw2l3kpKVOBhAKE5ldDlAiR0hJ6M7mf6WVR5GqgXW8Uq0/XDhnFzrWBZJgod/A2kTCn0HGDMmIMeo3D6hYTlsYb3x6mIkwk9rkPAEKBkQ2ES6PL/UqD3v4HCoz62DEiyXMLe5Zx0zT81fNYUhm7uJaMmU9GDsJWP+tsLp6EXkCtyqFAPoDBNfQOcTOhxlaiZ70E6XeQNl5ptopTEqo3RvfhMfrZljNOih5pmTzSxKi9W/HqFaQvECv3QaIWTV/RO+17HTil0zCQIbEUtbEV0g83nI6UaxApd/39ZZTShR9uQvouAetoikUJPBUXjmbP+XZ/nVOsQE+f+FiefcBbWI9qKqConCWaCfhIXqcBlUvBTK6NkmGTHWV19uKB7w4QuVdnVEBStuVHbhCi/XxO6hQw83IPbmRi8jczqdXSvv4T9w2UsQ1IOY4VepVKJpqJ1ernU8c7XrEv+2ybEpv5YM5nQn94zxpdve2rqRqJHbqkMMvl0NoWerDK50Vei0H0XqWZnufR2OWQyDqOyi66eLjJGkGRygCbsuHAhrdBNK64UXUDtc6OLMFDRLNVsH3mi3Pzocdcy64OzY4KaDnUClMhQJTc7JezoMbq9fZ1ZeRpLfv0PGhqBxUUvwis3vDdptxAP8m5iudRSj+wBVpLpYRjQ19U67iPiPPS4DWxkHdjReRaamcSeAvBELiG3eALWZEI/uFebzUaulyoZCsJlcW8Ob/+zKLfCyrPOoyxTYqBJmmdM4hNld0bLpb87UsuK6cv+0TncJRV76FaSnWIHjW0yuiKFnszTja7BsqrbJJDqXJgidCksbbMEjYSuikM87q0mOO1SDMMg49hUpENZZpFm/UltXOaSzwKolFqX1VKDQgf0UJXQoyatJLgqfTfphx9nuUglCLBQvqdrACCpTN0/VMYkTPaN9CpUo9kNdAi9jnUr9YVhZPLYRoBjhNSUTaBEUq4b47af7+CrP9oy9RE2lbaYtk9EJovh5Ka04o3TFpXn4mO1pTxjGIaR9MfId2uVlktNspdhWJ8ubqYJ3W5Y60JATDxeNGVI5gaSv8XFVmZq35ht1CZY0VxRpSSBVGSi6TOYFkYm31YOeivECt30JxF6RCZmVAglJlsueYeaqh+L0LDJZ/Tx6C1kGn7jZMS9XBIPPbJcMpFCl2YGI6XQfTMHSuqZnLHlEjQWFj32+HYAhlyLicAhawQs789QefZhwKD/jI1Jmh6AlZuabRaT+HjJIzuDQu/OO4ldVshNTz62bVKOBJWvrGTeqCMjhR4RenfBIVAiIfTY9qoamuhrRuSxJ4SetlxMBAFERBw/lavAxcNKmpA5tuDzpZdxR+2sBtE1Kgsgw3rKZKoxWFgrJd8ZE3reChFSt+JoUOix5ZJS6D5m0toYSHrH7B/SCt2PmpxJt5qkK3cUegpJE51cN7ahH9F9ZVFT9pTg1479+m45pbd5ykPPRoRuWA6GqVWhCCZbLhGhB5rQZ4uY0AsRoRcMN5nUI6VMFLpKlS6LVAXlQoEKAzBEYkGpfD3f2co0KnSgLYVumUJPno+ac8UNtZSw9LE4KkLXRJ1uqQr1m2QcsIsn9sToLtgNCl0KXUoPdbulFUTyxBFGPUn0ZZbJOPxHeRPb8+dgWiZ+9JQTRPM6VRiQ23U/J5qHGwY4AJRGdJXjgbLJiKs/12W6VJ75JZkVp7J4+dKG9drTEHqp4s1ouQhhJCp9Vgqd+gg+W8YKXRNaV94hwKwPSI+OQZXIf4+ILybXZFAI+lwwVVAPHkcxNAJPd0uNflvGMXk2WMaEypPP5/SNlYjQU9tOD6SQ1WJipcZNyrpNH1OFuKqehy6bWi4CT+leLnWFniJ0JKajO5OGtXKSDBFXzc41jktCTx6PIt8zZ3j4mLjKbpwMIhU7Y0JvUSkoEeQyNoadSR4NhZNDRI+LXTlLX2Bx06jAw2f2B+OEZd3YlkgIPW+4hMSDEGRyQsmUcostFxZSL5fQTwKiAKpQV+h2rNBTqrydJxnTNJBKJFkujhkdK2FROP0i8qddcMTrNZwcIQI7bFTosYcen0NTPXRnEqFbKUKfvkrYMKLWrYmHro+jY5vc467DzS/FMgUeFj4WKg74hgHOo9/g0uyTU4ZcBOOHCJXBrgnBoUrU42fsIO7ereROPR/LFEnfIACnST1ITOJS1XuCT4f4d85E6DooWs9yiQl9ikLPaYWezNONEgNiZR5Y0Y07aGa52NqKi2wTS8UzYT08ZSWjBuORfAA9BQcP/dQSE7psQuhhpThFofda9bGDHnWFHlsuTiRUQgw8aTYQeljVnHNgqIgwFKZt4+Hglcv1AeHiGAVFFyJihR5fhHnDxVU2NeyG0v+DI5Vk9NtUhR4RErrXhXCyiZKIx5cBbCzs4/W523iwfBpwBoQeoTH73fbaS09l09nLsUpb9JqF7h9uKx8l65ZLWqGbVp3wFwpUGGhCj26QIsoKgno7hAaF3kaWS2y5ELVcdaK6AiUsBl72v45qvYZh4Ik8Tjipgji6ocdTeZp56CGmftRXIVI4ieUSZ7i0gojTFpVulRqnLcaEmnFMbEvo7AlDJNaCCgMIPLqE22ARhlKRrQ0zIrrYPVTFLep9Ov6L7+vOoKduAKCrtw+i+1amyci4tG8+k4cO9cBo1zS90CHax5G69mNCD3WDNqgTelfe5jACYkKPjkFN5HSWoJWHMK3Q69eZMrSHHmeqWIRRN0sfP9WhNP3k0ZW38UYs8pC0qUgUeuDr/S4DZLVY55SuPgB6TP1vFwuFQAob6deoiZjQFTUAQ+BKrdDjFMi4//rh4XGwQFg2rmfjVcuYxG5Ax3JJIJOcUf2YnDM8AkxcZTWU/u/YX29m5Pq658vIXV9BBX6i0C1b59gadjbJDDCcLPh6OyfaY/q7oiwJI2xMLWwX+azNqav7MKLUvrzhIUXskdctF+x0louI/r5wPHTCAEwrGWRs5XsIo9x5J6v3X6OH3kZQNBoUHY+giy2XuQoceWaOjJyahy4VSXfIyR56VzTkIm6+pUyb3Cwtl2RIh1FX6KDVsWWKxJ8VqVmbRuBRMGoNhH54tMIiY5zDYQ8790+wfVxvp/zkvZiFPjIrT9fr6uvS6ZJAtqtxti00kt1Mlov+ne0pdNDxnhF6GJGFJMU0ExF6HADuzjuEyiTwGwl9pziB+91TkxGAsQpPq1hl2lgEyT61CJOMFxcruUHF/28YUMjqPizSEFTFJIUe+FgReYe1UsQpRiISlxm66msiam8RCj21KFHokeXiOBa1MBpEUx7T31EtopSiXI5uEra27/xquZ4McazSFhcikjaX0c4vCI+D2IShAak2lzv21duNul5AbefTjN3zdXKnnJucTE40OUQ4mcSr1fMoa1imYKkY0xuICF6EHuFRDHiN1YpjhHimrRVJGNYb6KdarJpCECpjYSr0IN5/Fp7TS84dStI/0wq9rbRFU+j5kVLqoRgiznKZm9PTtwpkJ/f4kTqd0Mz1gGklwdMYGVur6EA42JRRplO3XGZQ6GaUhx63YY2zXNKkY5laoUulkiIjHVRTdIka5UDhHtxBWBplj7uCJWaRoewJbN83AfSw+6L3sens5dj9y5Mmbov781R3O+RxyRdyU9Y1W0IfaNNDBx0Y/WTtdYzVQl5hTYALGVUjMKxkfV15W3vaUadRI7oGJ8xe/q38YjYsz+onjCDOQ5/koRMmXRaFoZCufhzxlIUdBUUzyU3TxLG1DeuJfNKKOe2hi3wvFEeQlaJOG049pa9B91vfE2hLMTCzenAIIUIYSXW64zjUoptMWNQ3gbBSxAtk8vssO0NV2shaBSuqQj1meegLEXKS5ZI19ONTLTCSuZwA2/enCN0PE/WuwiAhScvRJ2vu1AuSSj7h5JC1Mkt6syxSOj84vjhN5eObU9VPu0gXvcSTv5WS9cfMVDqbGWd/LCCFrqQmdD8aGGHbQvvo7hD5gr4hpj306bJBkvck3QllMlgYaPBQjwaBnSenGvvAKClRGPRc+GvkTj63gTyg3nHRwyGHVoiLe3MIA9Ysm/74x5ZLrCZVXFhkGvR1ZVjcl0NKRVk5iLBeERx7sF2Gy3gYMvqTf6e26wkOnPNezjIC+ledALpxIAOnnkV2VWO3xCX9OSrKQSAZyE19inRmabn0xZZLG4TuWIKRin4yEVHAL6tqBKmnWcsUSMOst45WIVKBaZqAX28BEqcXTgqKWoTJPoW6teGplEJ36k9BtiWoSQtX5JKh7om1GQYYlo3IdRFWS6Akws4kgmuRGqUos4yryEoyc8hqiZoR6AyhKEvKcWwqQZSZFDWuC6sTlKteosYtx6YqTaRfS2bHzldQ9LgkdDUpKAqgTAdXBeDXL9wd+ydY2p/j0GgV16sTOmGYUuh6Fyy67C3J55zFq1FelVt+5zRGv6SzC4zURHDvKA5GuixdRSeZkjJJpzJT2zZT2RILBVqh23WFbgn6V6+mNLIVO9tMoc/CQ1ehrgI2pD7hjZk/2w6kXaCLWuOLSiIxomk9zXvydOfterWhlWHpQJ5/+sgrkuyPVognFsVDJ+ISdsMw+PQHLyOftfj+vdv5j/ILMIDfiI55GBG6ZUg9y3RiGFkrIXf+EoBlJ54MvxwDYOXiqUHPxX16xqmFJJ+ZemmnSbydoOji3tj7ntlitC0zsYlE9MSRpTbVnjTMVKVo9JQUnSPZKEYR++QNg5RNG8uQCJnOH68Tum01PgXFhP6j2jmcu6wbNWZE/nzUnzzwMSwbM9eNrBYxTEsrdDsDGIBiT9gf/beuFQirRapmoPddJAgzGYdqEAVII8uFMKBaLCVq3HYciqEF/kRC8p20xRTiu2za9zQsXeobt76sunqyzLoT9SOT69eLd5QME9XrNOlLnD3xbP09W+6uB8+irBdL+WDO3kOPka7gi3tby1Am/S1EKkpvCiOyXBYQoQdRlkuk0B3bjEavGVNK/2EWHnr0OwMpcUTYVql/u9CVlR5hKvcYKZMKzlboLjhUZUQy0bEa6Mkm7S5aIenlEn9VqidJT8HBMgWWKRiR3QzL7qRNsqylhpFXJwijgRHLhnVXvqUn6fYCjm02tX2W9OWoSIeKchK/P43ZWi4b1i3lfb+1gTNPGpjxvembRTwAOYvb0JsIdLGYjKuNoypaKzpHslldS5JYLmajhw5ghfUbcxAV8Oi6EKPhd2UzJrYp2OotY7t5UnL84uCnPo9tRK5be+h+DWFnMAwjyXbbG9R/tytyyGqRmheSy5jJNZnJ2JS9KIWxUncEqhPjCXnbmYzO/glcujKN84LnGscloSeNdHL1R1/D0oEHEbooJRkaq6IUnLhcq3jXC5OURhUGySOT7UwlDnvRKsxCH8VH7kxeiy0Xi6ChbHu2SA+XTopLVFhPo0tFv5N8ZrVwPHQZ+pQ9lWS52Jag54KXs+wNH0puVmnfvB3LJSksCnUeuk2YXIBzAZXpQhgKt1xMvSiTOoBW6M47VKQ+HsYsjnnSDz3+qibWUXofxYo2tlwAqI4n5f5LwkNIBAMrV1LI2axYlG/aLGtJX47vVTfwrepFTS2VRoU+M6FbpuCKi0+YtjFXDDs1u9SKCN0hIBST9ptp1ttHSN0rPnZWso4ZWVVTg6JEhG43EHrUh8V0kptsWqHHVbeVWpBYnem0xUShR2mL8Xvip+g9YYrQjaxW6G6gx9VFKZfZjE0pNdsiHvBSK47pIR2AndEpsKb06M52CH0KpF9DGaKh4ERYmSRvWHk1ylW9lxf1RlVoactF6tmjCsg1SckyDIPsiWcnisnHShoN2QQNmSizhWE5uogGENHTgQplqvQ7ReiGERXcLBxCn5iosvtwlc3PavXoWCZmvofC2ouS96RVudWOQhc6D12mui3OJaETVYPWJupZTyiJmkmh5x1Kvj4eYhbHXBjo0v8IaYUeo+GmFyv0ap3QndIBQCVTe7zcIoRpce5piznrlEU0Q29XhgMs5oC1uulThG2JxMVqx0OfDeI8cAAzta/CSU+zhmkngU1jkkLPZXRjr3hikJHaZkLoMkXo0azQ9OxZJxUUjW2Ycs1Pjl9ShRrVU5j5HoLiMNKtJk+YYpJCt02DKlmUV8Nz3QbLJZt1KKdqEO1+3XXTK9YVeiaTwVU6j74nE1UAdwi9DuW7KMtpeCwXTr1dpnSrlBJC1wcn7aGrMEweuVs9euZOXA/oDm4VI48pXYKoVWdDYHOWMAwjCdA4USGOUmEyNNpM/yZhEEb52QsFMvAJETy9ZwxoVGYxjlqhG3NruYisfkrzSnVC12Qyk0K3KcYNupyjUOhNCN1O7RcjIpswZblkSnv1a8vP1Nvs1QOoP/x7F/Ou3ziv5fcu6s2RbxHENAyjoaJyLtFMoQNTPHRhWfXzOfbQY8vFsRqCySIdq0qKlWpUIhssjC2O1M2/brnUc9PLVT95Mk7noRuWTe6kc5DVIu7B7cl1bdhZAsPmsIySLhxBNSp+wi2Ry6Q89KxDMdX2yR7Q4zr98kTioWey2aRjY68Vt5/uVIomkJ4Lpt2g4kynrtClW6EUtalMCL3BQ9cKXSqjZXAoGxG6vXg1vuFghS61Stw97ujGR8Xj63r7omIHKQmDgFAZWFbjhSYRU3p3H1PIgACRVOA61lRiMGdZWBTnocsoy8WaY8slTm/1U4SupExK8luhO+9QCWOF3v4xN6OgaPJdzRS6ldpHTSyXXGkfAIf79cCY/NJVtIMl/bmmAdEYaUtiLuFYqZt4ysaUZuN+E5ajR8kphaF00VXsf+cy2nKJW0uL1NOqEalwk5Bq3Dc9CoqS+o5GhV4ndMt2ACOpYYmD+/nTN+qbRRgkytws9DGWWZE8wWUdI+kEiVvRQ6ajm1I+5+Cp+vG1IoUeVuoB0EzWSXqqd1uRndQpLKpD+TXUJEK3nEzSrU265UShdxccbEvgekG9LUCU5SIxWnqJ9sAKMitOI3/KeQTCwVIetYq+4CYP+J0tkhYDsS8rJTLQ67GtxkOiEBgLyHIhDAiUmRQWOXOq0IP5IfQoeO6X0wpdJtZXK3TlnaThlTmLYy6M2XnoZpOgaL6qxyk9I1fxgHc6i8+/tK3vvvqKdfz2K1pP0kpXq84l0jf29ABkOclyMW0bE0nNCxPLJd4V2YyVFEZBYy+X9BNbfJ3HaYsqneor9DUUZ7mAtlszjoXhZJLZnyrwdNpiJk/+ZP3EE6cML3n1u3hk5ev1a4a+WcWEbvplPWQ6IfRs0nkUwO5dCoZA1Yp1hZ7LJmKzII5xP/SFCOW7KNNpJPRMhmrkzclqmVJVX6xdOUdPaknnoUdZLqFqbbkYhsGqt90CgH/XXWTCMl5xTP8tO7WsejaIg4fCdpBK56GHkZVhTSL0uIJywUAGhKlB1s0Khxo89Lba54pULxcZEfrcPZLaUcOlMJWF0I6H3lOwGfROoShzrJ/UGmA66Cn1Myj0NKFHlkus0D1l6Qn1wuLZERjqfgVXr1k3ZRvNcN7aJdP+fXK+9lwhbbmYdmuFbtkOBiHFipfYnvE5ElsuMRotl6mEHqctGpOenl64fgXnnLqY9AOYY5sIOzNFoQMUznwhlacHk6cwq3sAmekFRnBsE8skIXQrKJPN1G2jnu5sQ7M+kevSDd9qJRyhY3zZbDZpwZtjagbPXOK4VOgyUeipnG07g2tEj2JumVLUxN+2NGm7Xr2JvQoDwjBW6DPv2EBksJWXeLBxj+0jRXICmhYKXQkqQ32DsScRoIp6giwYyCCxExxLNA2+zV6hG0kBlZ5YFMwpoTu5LBVpIyeG6y+qmdMWY4X+iH9ig6UwE0QU5E2+qplCT20vLm6LPfTRqO+I1d3PnsMl1iw7OgGRhjOpRH7OtmvVi6fSRDxZoVuOzicvVXwMQlTKQ89lzIb9ls5DF2mFHk82qpVQNMadAP7vWy7kpRtWNcQpHFtg2FqhK6WStEWA/OkXYVhO0pgL6scnY5uYwqAU9Zq3wmpkuWiR1duVTbpmgm4lYeZ7MLwyeTv6Xfls0rExbljGPDXnmvEsveWWW7jssstYt24dW7duTV53XZc//dM/5eUvfzmvec1r+JM/+ZN5WWAzqKYeuoMf9d2Q1RKlqpdUuMUK3YvmjZZKVe1ZIxqq51ohNDM4yiOICN0sHK1Cj4IvppWkJcow0Gplioe+sLJcDBkSRJWPdot9lyar9jz0uCJWe+jmHFsujm2yP+yHsT31F5VEzZBP3pMqqLGbxAqmQ5hS5U0Veroj5SSFPh7NiRddAxwcqbB66ZFXJk9GXaHPLaHECt00RQP5qkkK3XYymJFCN2Q4yUO3Gp5szFRNRvp8iNWyqhXxsZPiwClrSh2zjG1i2BndYkOGgEpEg5nrYvU7/5qei3499dm4v7qJJQyK0dShPK4OiqpYoecaPHSRyWPmurU1Ey05m7Jc7KACwpqxluFIMeNRvfzyy/md3/kd/tf/aux697GPfYxMJsPtt9+OYRgMDQ3NywKbQphIJxc92uoJ46aTITCjMXE1rdATQo8UelCrYgITpSo9UVA008QDnozQzOLgEZTHcaDhTn5Ey08RepyWKMNAzzedpGiVIRrmjx5zRFYV0FK1Wg2WS3sKvRop9CBUc67QM7bJvrCfk8d36mCcYUQe+kwKvb6Gdm78aTQEXJt052wIiqYI3TBtylFDKM/uRilYs3TuFHq9NH6+FLpoCGYqa3JQ1MIyJLVAYijVNMsFdIvftEJvJPTov/0aPvmWxyYdj9KWSxble0nr3LTtEacbJv82Y4UuME1JyRVg2hQMnbaYKPTuXIPl8k+3b+fX7QJWMETOAgJtQcVP5cKvzFtAFNpQ6BdeeCErVqxoeK1cLvOtb32rYUj04sWLm318XrDsdddQPeNyQI8GA225mJZFYNjIWolS1U9KlmOFHvd5kUFAGCnidhW6TYCq6LYCTuEoLZeE0O3IIw9RgSbKyZ6zVvCq2WaOCQwVJBZCK4WeLkRpS6GnPPQwlJgqRMyxQt8X9iOCGsHEYf1iGwq9O6XQmwV/p0Nalc8UFI0tF+W7GE6GWuS9lgytzFfP0DtmNogthNlM3GoH8c3dMg1M0yRUet9OJXQbC4kfSAQh0jA5ZXmGX734BFYsLiRiIU308edi1KgHqH2spqmzMInQLRPDdqJh2xGhT3OOWQ0KXc8UNrJdFIRLLmMmHrpl23QVskkK7G0PHWbEtXDCCjk7mqFg2phRZhtuad78czjCoOju3bvp6+vjU5/6FPfffz+FQoH3ve99XHjhhbPe1ubNm49kCeDkGRwcxI4e//cdPIgMs9Rsh0N7d3F4ZBn9XSaDg4O4tQrlkkQoHZAYHjpMYJSRGOzZvZNBcXjarypGLTOL+7YTSIftzz5DbXz3jEscHBxs+npurEgW2LVnLwYG1UqFMeUjEezY/iyWuy95r1Tge7WW2zpazHa7hTBAGYLegokMvJafF0I7Rc88sw1Zmn5f7dhbpQuDaqVMpVrDyHocHh2DFbNfXzOMlwP2hbqC78l778Bfejqh7xGombdvmwZ+qNi9aweDcWesSWi2jUDVyahYrk55z+Hxennhs9u3c07qc6XQBhN2DOuMiIO7tzGy/8gf0dPfXS6NY5lzs1/TOHRIB5xlGLBr105WIzAJGR4vNXxXMDrGEkOxbdtWTvI9lFL0d1m8+DTJo4/8MlHoIYKdO7czqHS2z579B1gZbcNNtRNwpaA0Mdb09+wbqSeIHzywl4mKi3BLHPjlQ/QBu/buwxPN98PBAzrg6rs1zKxFuVqjmjPJGy779u5ir7+bHPDLhx8hY0kCLExDEmCyf8LjVFlNOrQ+/Ohj+PH5IEMCqebtej4iQg/DkN27d3PWWWfxoQ99iEceeYQ/+IM/4Ec/+hFdTfowT4f169fXu6zNAoODg2zcuJGHf2iBhFNPW0vhmcOEIs/SQhapTFavWMKZfQavF3fzHecyDFffMXu6uuhGP+afsfY0Nq5fMe137Xl4M1ShhyqjKst555zNiSumV+nx+pphePwJxncNctIpp7Lj8bvIZRy6uxyGhgRnnrmWc0+rZyrcfdv/w7HMlts6Gky3xlbYdpvEsBzOWLmEibLX8vP21/fjeiFnnnEGZ7eobIwhug6xa9Ag6zhYlo2NZNGKVeyAOfnd4yWXv/+2vqms6bbo37iRn/74ywhpzbj93u8NMTRe44x1p7PxrOVT/t5qH/7nt+oXbFdv/5T3HBguw/c0WZ29fj08pF/PFLoJ/R4IoeosYWl/jhdsmr1QarW+wd2PMVw+OOfn097yM/DwZnLZDKeccgrBkwLHCBlYtqrhu3YObyPcAyeuWYO9zdAzSKkf5+9/958BPfHp9NNOZeM5msb98Cl4JtqInSNUBqah8HBYvmwJGzeeP2VNiw9MwG36JnzaKSfRv3cJ3qEqJ591BrvvgpNOOZXuc5vvhwO17fDQowz092BRxRAmTvciCuOjrD5jLSuHDjC6DS648EJWDN5HOGol3SFlbjFmUbIkBxThgosupvvnP02GjziZ3FHtf9d1WwrhIyL0FStWYFkWr371qwE477zz6O/vZ/v27ZxzzjkzfHpuEQ9XsJwo31xltIde9enKOZSfvJsT3K1kxAXJZ3QAMkAqQbYNy0VFFWRmZYiS7Jq1nzoZIq40jT10JZGhRKqpj8IKgbFA0haV0gFLhMm7f/O8JBe9GSxh4NJe2mLGMXUBlQwIpEKoYE7bi2ZsExcbNzOAd2gnEOWhz1BYBLqOYWi8RmaWQVHZEBSdPlffti1CoR/jDTtLxdKEfigo0Nt1dEVsk/Gml6/jNS85ZU63CXX7zTRF1A5Zt8TFnmq5hEDo+VFhUeNxjjOPdBfGVMZLKhXSsBx8aWHi4ymzbQ/dsLNI303abEx3jqWDoqY08ANJaOUpiAMNhUUYgr7uDN6IiYiy7EY8vd0uSmAIDGGSzTn4ZUu3DplHy+WIjLSBgQE2bdrEPffcA8D27dsZHh7mxBNPnNPFtQMZBZzsbBbLErhkCKslal6oR1Ad1sqs2x+pfyYIdJogRlvkLKPsGdOvUFLZWfupk5H20GNCJwz17MvJhUXGwiH0pGRb2PT3ZFk60Hp4c3wxtpO2mLHNJMtFhQECOedZLgDl3LKE0GmjsAjqPnorn7YVGki8SYpaA6FbIvFVhZ3hgL2GH/RdzQ5/UVu9yGeD7ryTDCyfS9Q9dJH0twemtMmIc9Rl4CGQUzKA4nF94WQPPX1jsJwkVbAmrZbBeTtVmBTnoSvfRcUTkdogdB1zgCCQeGaeguHqgR9KarI2DE3oUlCJUhsPVfWxzIfF5Li+4fLTk+K0uQz4T8aMZ+lNN93EJZdcwoEDB3jrW9/Kq171KgBuuOEGPvvZz/Ka17yGD3zgA9x666309BxdsPBIEM/3tDNZbFNQjQgdoCtr4Q3pVLVeWe+TLsOo9B/RHjmnqgRLMnPUObwNaYuGDnpKqQl9ikI3Fk63xaRLXhuDJ2Jl3k7wLeOYSftcFUb9oueyl4vQN+4JZyn+yH6k7+qbZDsKPSL0Zi0OpkNDINSc+tl0aqdtmsmFb9gZLMtkn1hJqRq01Yt8ISC+aVqmkUxsAmASocfB7jAIMFQ45eklHqgdqkZCT3chNVKE7sr2FHqctqjSCn2acyw+bx1bNw/zQ4lrZMkbLvmsrhSNh6L0demJRGOeXuPeYtQyIJhIjuu5py3Byesb6XwNiIY2LJfrr7+e66+/fsrra9as4V//9V/nZVGzQV2hZ/SEEjLJaKpeo5wUE/WrseQzKgyQUXOmtuwTu97DvKSyLbM72kWztEVksPAtl+RCmPmETBR6G1kuGbtuPSHjx+G5Pem78zaj9HKSkoTFYYzU8ObpEKcuzlahx8QENFfo6dROy2gkdNPA82VkG86fmptLxCrZNLVV4kW/v7VC9xFNUkdjqypAkE3n6lu6LYBlSEwngx9t38Nqi9BtW+i0xcCrTwebhtDTlovnGyil0yUHDEXBDHClTIRNf3eG/yhvSvqyHyyb4IDjFTFSNSvp636+cFxWiqYRRheLk81hW0LnqPo1BJKeoF4ZuMiol32rMEyac7Wjto3UUIqSzM6qarDp9uxJhD6T5cLCIvR2qtziwhmzTQ9dj6ALU7bOXBO6QyWZfBZiKNWWQu8p1FNfZ4W0Qm+ah562XMyktF3YGSxT4IcRoeePD0KPRY4ljGRiE0wty09bLkYzyyXJQxcN6a9CGIkqF3amrtDVNJbLZIUedTeNm3q1Zbk4ZnLzHQ+ip7Wwqs/V6Bzv686yO1zMgbCPU1f3UYoKnwxkw3lsdAh9ZkhDTzlxomY81cjHyhkeuajBkTIES01d5akf7bXl0oxAmyFN6DUjd9RVXs7i1ZiFPqz+ZYml0mo9C8tDb189x0SerohshYxjJje2eLCu0cSmOBp05W0qXjQSLgwiMmlDoUflfu2cJ2nIiNAlBqLJTS395JL20A07i2UKyhUfKdVxp9Atq+6h+0o0WCVQL6IKAz/y0Jsr9BBjyijDuEJZ2E5doSurdcVyQ+m/qQeCA2FRx9OmO48bPPRo86ORpaJqJR3Ajgk9Fbg+86QBQszET29s8b0APPSFDiksfKWb2duWSTnqlZw3POzSQcxCL152ET1CFxVVlAOxQqc9hS7sDDKq7XHN1oHAdmEPrODE939Bd2ZLrIYQqaZ66BhCq8kFgKQgo40MFMtsX6E7ltCFKFJiztPMxe68Q9lNEXqbHvrFZy/j1150En1NRr5Njyi4p8ymAsAw6vaaZaaCok4GyxKMlfT5Wmgy7HkhIvHQhUg8dFfZDQPDIdUrPQgQKmwSFI0JXTQOGxciUeWWk0k6HOoB0c2PoxD1m4JjC8y8tj+CaLTftJZLxOJx6T/AeDWqRA88lJR1Dz01Y/askwcA6io93Y8mFobz6KEf94TuWQVKKpe0zSynFLqY2Ie9eDUyWw/WllUmmlgkkaq9SlHLtpLhGd4cEHoaWqEoXVLfxNNfiJaLaMtD1xfB5Au6GQzD0IpcSUzi6rq5Pem7cjYlN9qPMsBAtaXQVy/t5l2/cV5bsYA0VGSzTM7WSMO2tD1hCiMJlBl2BksIqq6+sR03lotVv4GbQhAicJU15ekkGbcX+gjUlCra+JiESjT21TeNRJXbmWySv+5hTdtnJ62046Z6QazQ2/HQLTNZx3g1sgPjEZbRzSdOLRXCYO0JuoCtJOuZbDE6lksb2DZwCZ93fw3QB6EURgpduMiRvTiL16Cy9danVeUkXm2z/uPNYJkiaa7jW3Ob8qUwdL9z1cICWkCWSzv5uzFiq6WdPHQAI7Ke4qEA8+GhF1MKXdCeQj9SxEQVIlrO5LTM+vGOyUXYmYY+L8eN5ZJkuQiE0EFNT9lTb2YRmakgaGq5KJrvN8sUiSq30kHRaTx0/bm60o4Vet1yaSdtsT4ib6yiz824/XZsudiWoCtns7gvR393Fss0KKmp5C3sDqHPDDtLzdIHyjbrhH6CM47yqtiL12Dk+wAdQAmUqX1aWc8jnQmWaSSEHthzTOiGAGTSG3ryBTBfCv3TX3+EXz5TnvmN6bVEhJ7ud90KiUJvs2eIME0MJTXRMg8KPW9TS4bNa4U+n4QeB0UDmlsuEBF6tH8me+jJuo8TQp+s0J/0VvGov2bK+ZzkkwcugjBRuTHSN8KGtEWhFbpUBk6mnrboqdZZLul1aQ89tlyimpTpCN2uB0Xjczlt2ZEKigL092RY1q+Hd/f3ZCnKqX65iPq5LLheLgsJr3rxyZwXlcpblqAY2JCBcx1dUJRdcwbGfu2ZuSrq5qZCUCFKtOeLWqaghk0VB8uZW08zSUuM/MQpF/88eej3bd7PCYtmGXiU7VsudX+4TYUuNKHHU160Pzl3v7sr7yS50bPx0I8UsTcsMWjl1pimqP/CVNpiuuPmcZOHnuq2aJoGd7tnAbBpkuWWZL2EXvSUNNlyqeehT+6rHygTH5OMY1FLCL11HjqkrRMBVh6EWVfo01guKxYVeOurz2bT+hV854DmkrhYSkV1LEbKLnrrq8/WbXWBRT1ZSoemqvF6dtv83aSPe0I/eWUvJ6/UloptCYrRlPY1xkFErhtn6QlYXU8C4CobYVoYyksUejuwTEFR2ZTnIGVxCoxUdkez9QgzUa1zCd8PCcJZVj8mHnobCj1iMdGGhw4gTAGBxDTSCt2f/kOzQHferl+QMkA8VwpdmS0tF9sUxPfv2EMXdqbhqeZ4UehxgZ62XNLpho3vSzI9AhehFGrSG9JB0cldO310YDTjmBQb8tBbH0fbEg2DWMxcN2F5TK9hGqVsGAavf9lp+jdF60jmxIaB5o8UoV+U6vOzqDdH+eBU8n4u8tCPe0JPwzYFNWkmPdJzJ56NYQisHh2oqCkb07EwZFWTaBsVj6CV/721tXSb3lH3cZkMZQgMqSDqkTIF82S5eIEkCGengENPl0y3Z7losmo3mChMCxEozPi3zjWh55yGC9JANm1rO2dIlbCLVpaLZWCEky2XTPJUIwwS1bfQEQcmzSjIG2NyUDxWqSJwEYaakuUSE/yUSlHT0ApdmWRss9FymSEomr5mRb6nTuhtpg/GlkuQEgRKycRDn4yTVvawf28v0Til5PU4KDqfvVyOj7OlTcTFGmXp0CuqZE/UjcKcnkV4aIVuWzaGqwfUTpEPrbYrBI/6J4IPm+aY0EFgEGoLoAWhz7VCV0rhB5JAzo7QfV8TutXGhWBFXmq7EKaJQGEZ8+ehpx+ZBXLepsaAfuIIEQRq+qCoYURZPVactphNzuNCzm752YUGyzQwjLiXS/24T16/cKJhy6Gu4J4sqhqzgxrTFisqQ1llyDhmkpPuqtb90GEqoZv5bi0ThKUD8e38tlgHpCw75FT/P8YbLjud0okVhr/2303TFjsKvU3EKqGqHHqpkjtpPQDZvoGI0C1ytoWohTrtqG2FXj8p516hG6BUVFXWZNtC6ADeHCIIZfT/s9tu4GnFLNpQ6JY5dVjHdDCjbATdi2/u+110T/LQteUyfwpdGAZ6hMM0Ct0UCFEfggCxh65f6zpOctBBWxR2lOI3WVk3vM/UN1Yr1Hn2U56S0s25JhUWfadyAY4R8Ae2maQt+sqatpbEtsyGv8fFRbOZGhSvY6qH3vyGYJqCXN9A9D0dy+WIEQdApJXDzBvYi1YBuse0qyxC08EwLQwldUZFu4Q+qTPenCKyVIRqZbmYiDlOW/T8IyT0WKHbMxNN3HWvXYiY0I04NXLuK0UnK/T59NCFMJCGOaNCV3HAu8FD1/u5cJzkoMd44xWns/6UxY35401+u4+dEPqUcz7aD6FqvDEIYVBUOVBRCqORUujTWS5mYwM+EaUuziYwmXjocX+eOEtuGv4Q+d4p35PusjpfeF4SeunElzKwfmnySJ11LB71TmAks4olZploJHHLO+xkpAl9rqelx1ksBrJpVz6EQMyxQvcCrYJn7aH7WqHHI9Omg2kabacsQr2bXqzQ59pnzGUsVKz6ZaifemZhCc0WQkQKXdUDn5Nx2po+lGwspNIeur6pHS8B0Ri/dcU6AEaLteS1Zrabb6QIfXIeeuyhYzacPw2iyhTsDJfzuLeKospNGxQ9dXUv46V66466Qm9/3yYeupqs0FtzgZncODqWyxEjbnW69ld+je5V9WIiyzT4t+pLOWWgl/PEfZjoAbVtB0Un9YSYU0QK3aRFkNYwdfBIybY9v5ngJwp9dp9LFHobvcq78w7ds1CYieUyT6X/hmGQz2mFlFgu80nohh7yEExTWPTO19aHwRgNaYsV4Pgj9Bhpi6mZQg8MG1u2UOgtKmwn2zj7jaV8rnQ5wLQK/fdefXbDv82jUuiTPPRpzh9DmOTXbSK7+ozkNdEJis4OF5+1jE9d+7Ip4+EMQ/ds6crZCEunARpKtf1Yn/aCj3a4xWQow8BUEowWvnF80kgJczTYN1bo/qwtFx8BWJmZL4arf3V2k3EShR5bLvPQ7yKby0D43HrogRKYbQRfk6CoXQ+KHi856JPRMGmoFaFH830nVwQnhUXKaLgxGIZukyClarDzrEme/UwQUXHRrBR6fAlGA1FU6KOURIjpt7H8Nz/Y+N3ZLsBICozmA88rQjdN0XLWZ8aJCF3qYa4Clfi2MyHd6nQ+FLpJa984VuVKxafT0cMPJL+We5hDxqJZfU5GhG7bM49FK+RsPdmlTZjWpKDoPKiYrnwGiiB9D2G0b7kdCYTQ3QEDZWK0QTj1oKiTPBEerwp9uqAoQGg4ZMMx/Y/JT51Jl8qpTzaWMPAiQq8PoJjd9WjmI8tlFgo9/g1CCK2uk0rz2X/3irfcQGbFabP63GzQ1hl9yy23cNlll7Fu3Tq2bt065e+f+tSnWv5toeCS81dx0VnLEaaFhS4xb1+hpwh9lpNrZoQhsCISE81ITKQCMXMEzwt4WfYJzhY7Z/W5MOq2aM9xtSykFXrcy2Xu1XNXwSFAIKMRZPPtod9hXcqPaue0zHJpWNv6S1j8yndgiLp3/Lwg9Cb7OBQ2mUShNw+KNqsRqLeTMJL/nu31GJf/z0YwxJZLIWvrGQYzZLlMh9wJZzeO05tjtLWiyy+/nC9/+cusWrVqyt8ef/xxHn744aZ/W0h4x2vP4bIL1yAsG4HENBSizcf6xqDo3Ge5WIkqbaLQRVzMMHeZLn61QsYIkieDdiGjoKjdhuUyW1jW/HrooH39UJlIL5pYM4+FRcIw2K5Wsi/sbyuX3Fm8mp6NrwRISv+Pl06Lk9HK+44RCAeHuBXzZEKPFXoTQk8avtUHSM92kpSIFfosZtbGl39X3sYQVlse+rFCWyu68MILWbFixZTXPc/jxhtv5KMf/ehcr2veYFp20lujbculYUL7HJOASBP6c6PQg5LuZWETEobt3yhkGOhhIvbck61lx1ku8zOCDrTiDRDIaATZvHroop7vP9vaoDhmczzloacxuWR/MqSo/y5j0jGIYyfNWhs3KHRxhAo9DorOIg897l3flYsVejhjlsuxwlHdYv72b/+WK6+8ktWrV8/VeuYd6cZSk6eptEJjUHQ+LJeoR0oTEotPmnh48lxARg3+bSPEC2ZB6IHf9pSn2cKKjoUzn5ZLVFwURoTebp+ZI4EQBkG0b2db7ZkERY9TyyUOYELz3y7N1I1q8jkfiazJLQGgUaGnB1fMam12FsO0Z50LHrfIJbJcZlOY+FziiGXQL3/5SzZv3sy11157VAvYvHnzEX92cHBw1p8pjY0SN8AtlkptbSOdr71n1w4G1cE5W1+15mJG5d+l8tT1jE8UAXjk4Ych3zzgO1sc3rGVPnRGyS8efIhCtr0TszQ6woASbHnqCQ7tmVsFPTw8nKxJGYKHHnoIOLJj3AqjQyVCJRg5PEQXMDY+MSfbb7aNsbExqq726vfu2cPg4Hjb26t5ko2nFSiP7GBwcNe8rG++EVc3P/bYo+ScRtIte3VxMjw6yhJWJWscmygBEKip645jOE88vhnP1Tdl363N+vf1WlnGJkrsncXnDCRerYQXBlQOH8SqVimNjrHnGOzb6XDEV+UvfvELnnnmGS6/XOeCHjhwgP/9v/83f/EXf8FLXvKStrezfv16MpnZBwkGBwfZuHHjrD+39fCzsE//98DAQFvbkFLBv+8F4MwzTmfjGcvmbH33/uJ2iFJy+wYWTfnM2NYtMAZnnXUWuUUzf287GHz6l4C2XM46+xwW97WXRvXQo3cQjggu2HAeAz2zHck2PZ4YPwS79ZoM02Ljxo1HfIxbYYLdhE8LclFhVP+iqft7tmi1xjueeJDdQ4cAyQknnMDGje2ncAK8+IVHtawZ1zffsL++n9AL2XjBhikNxg49cA9ErtfiJfqcjtf42JPDUAJhOVPWnb/9vxmvlNlw/nl8+xc/5+DYOP19PbP+fWPea7H6ltF1ZnufGxwc5JzTlnLuaYvJPlHA6evBrRyid/ESzjwG+9Z13ZZC+IgJ/Z3vfCfvfOc7k39fdtllfOYzn2Ht2rVHusnnBFaqD0m7losQ9RzY+chyqa9n6mNgbAvE6mROvrIyBsSWS/tWjgoDQiXmvoUwYEfHxTbCeSu8cGyTEIGKslzmNSgqjLqHfpw02JpLxL+5mYeurLqAmxIUNVtnucTbtK16467ZBkUB+l742ll/5vq3bQJgz5a0h36cBkVvuukmLrnkEg4cOMBb3/pWXvWqV833uuYNpj17Dx1I5b3O7UE0Ggi9WZZLFPWfQw9d1EYBrYbjqtF2oMJAtyaeF0KvFxbNR1ER6CEHgRIoP85ymWcPPbLq/icSujkdoac89MnH2nP6+KV7IvuMqUkYVhIUrTfumvNWHDMgTlucqZfLsUJbV87111/P9ddfP+177rjjjjlZ0HzDsp24W0hTAm0F2zTw/HkIiqZbhDZTppGCCeeQ0K2a9nOPRKEHSkxban2ksB0Ll7rlMh9wbJMSAhU+BwrdMI44y+X5gFhBN72ZTaPQDcvmi+VLWV3oarlNSxiJwJqPAP20EFY0ZP55mOVyPMJ2Zm+5QL2ceT6acyXf0aQtrYhHmc228co0sL2Y0IP/v71zD46juvf895x+zEOv0dvyEwzICBswyIHcTQLB+IbciyF3k0pBCFQtVdyqbAiVuglFOYkLiPGmoiSXdarWKZLa2rpVWy6yS0HY2E4hkw3ZSm4uBchA0JUB29iWbMl6WBpp3j3dffaPnu7pkXpGI2m6p0c6n380Gs3jp+kzv/719/weVufFstBV6ERYUql12TZZEbqLDl0UjJ7WOfnK7QjdaqToYt91v0KpMXrPqed8KcnFdNpOayxfsZlPW6xOhG623/af+/SfRS5j19DFMvp6W4+1IoLKLiC7U3E8wVgR+tKKgIqhqwokNQmNEUNyWcqJQlOtBkWVxtTQZaiuXcpKEjWGNutFiloqiN2PrUnJRSDF00JtlZLzJRdLqnHoW2SW/BOS7+TpdYReUCnq5gjDZeI/i1zGHqGLS9HQRXc0dLvzcpoEZBY/VSpCNwfkTuv1EAiDoixhszUXobuBPUIvZwj1cghIRoROc5JLuf3wl4Pdibs5GcmvCJQ49nEBACLmM6QWRujFtXdKSV5HNwuLuIZewJpz6MIyCosAQ0MHKn+JVxChywvtMZ1OpSJ0NVdUNKkZFXNqOl3q4QUQXXUs+KgEQoHk4k5BjSRSqKD5CN3FS2Z7h8U1GaHT4l0QSYkIvVR2jGjrsb/c5lwrhgqAnuUaum+w6bPSEkrY870jKuzQbZdtThKQGcGwCpX+mxH6lG449Gyur0lZ6Bp04k70bJaAS1Bd+6IEJMHo4pcrenE7bdGknPa5qw1Ki0+ssoYlo0SE7iC5CJRafW7yzbmqIblotd3LZTVhjwjEJTh0s9y40huC9ijRSQIynY5WIclFnTMi9GkYA0DUzBIidKa5FqGbXw5K3JvoIkmCNXUGKL+Xz3KwO/S1G6E7u5eCCF0oP0K3d1k0/+5GxlUpiCCCWZvqPEKvOvaIQFrCpqgxm7DyB9C+KJwidNPpVKqXi56OG9PoRaMBgqooZT+X6JpjwUclKNhgcsmhByRasKnrdtqi9T5rz59bxXiOf5MDtsctIcuFLpRcKt79dBEMh16k9a8PWHMOHcuN0EW3HLqtk6NDn3Fa4cIiXUlDJTKE3JdKV8qP0ClT83M5K43ty+FWYZEoFDp0NyP0NZ/lUmJTVJAC0K3Z2EUidAfJRRIFm+TijgS6KFSEnhvF6Mcsl1U1sagc7FHZUiJ0USDubMDY7XHaFDWzXCqkoetK2hjSKweBNKBly4/QKdNcazlrP7G5JbkQQsBsewBeaehr1qEX+b8FUUQWIgJQjStmrfB59p92vnLXtZiN5+baVjPLJevfCH3tOXRbVEaXoL8Jgjs9TOy5uqU2RfUKZbmwbBpZiKCynHPo5W+KUuZen5WCCN3FyNkuGXmmoa9BzUUQim+KSgJFhonGkBVRKnTotuKh+VyzMWLdplXcFAUzivH82MtlDTr05UVonS1hV76Y9kUhSw6Si1B5yUWBZE1sYUuJ0KEtGOpbMTzQ0AEU2E8rNHTbiYK0xbXo0GnxwiJBIFBYbij2vMeYn9ViyQcirU7aYkGwwSN0H2B3SEs4IN/4jzeBLf6wJVPg0B1Gu5lXEZWSXFg2A4WJoLmTh16mQ2eMQUYWuuDOPEQvJBcAnl0JFEourr2Nb6ElJBdRoEgzESqjoJQWfK/M54iLnGyFZQ64WDHUG8luuay5pWY/CEvZ1Ci1QCtlj5OGbjbsqliWi5JGhkmguX4aZivZxWCqAgpW0IejotgdultXAUBB9O+V5LJWK0WL7R2IAkGGSdBAF5zsSkkuha+Ri9CrkLaY/8V/7nPNRegFB8QHZ1i6SNpipdvnsmwaaT0IYso7ZTp0PZMyfkqVHWxhYp8t6WaETmh+E87dLJe1vSn695+5Gsm0876PIFAoEKEzw+nbVzYtkbZY8BpV3BS1bvvw0mvNOfTC9LjqHxAi2NPoFjp0s8VvuZWi6YyKlKKiucHZ8epKGhm93kpbZFp5vVyYkjTsFcubbrRk7MfCzROtICE3cN5xhmuloGtcQ7/thnVF/yZSY1NUA13w2Zh7D05piwWPq2JzLgsfBITzqb5H8xi/bWoUFFY4RIxLLSw60v8h9v23Pxf9O1PSSOkCBDNCL9Oha7kInQTcceh2+cvNCJ3a5JzFnMbK3mdtO/RSiKKxKWpILoWfDRXK3BQVqhOh+11DX3MRut8umUwbdOas6ZtRZLkR+vh0Epenk9B1tuDLwhiDns0grYsImdV6ZUouSjJh2OOSQy/crHRRcrEVsrh74nC+zTH079PZdVBBceu8NZrv5VLaoZsl/0G5ihp6rTr0vr4+9Pf349KlSzh69Ci6u7sxMzODp556CsPDw5BlGVu2bMGBAwfQ0tLits0rY5lZLm5hpSUWuViiogAdANPLG0Qxl1Cg6wyJdBYN4XlpkJoK6BrSuohGSYAKAVQvL0LPJIxp7ELQpQjdo01Ru0MXXNTQ13q3xVKIAsWbynV4U7kOu+ZLLlZhUemz4GdvXo9QQEBTvUub9EUoCAh9uClalkV33303jhw5gg0bNlj3EULw2GOPob+/H0ePHsWmTZvws5/9zDVDKwUhxNqd9sMlkxWhF1kcQok89GQ6i1PnpgvuiyWNiDsaW1gwpGeNMn+FicZ8TYhWK9nFUFJGhC4F68p6/JIpkJ5cjJxtPeepiyeOtZ7lUgp79L1AcilRKWqnqT6A3bs2V964RSiUbGvUoe/atQtdXYVDWyORCG6//Xbr9507d2J0dLSy1rmEdZZ1q3PgErB6tRQ5FEJuo9RJcvn9W8P43i/+jHQmn00QSxgOfS6xUEphub4tGSZCEgVoRAApoqHrOoOm5zOEs0ljU1QKu+PQCzV0F9MJbSeLpcyUXSp2J+5GumstI9oc4fzPplTpvy9YZmGiV1TkFKPrOl588UXs3r27Ei/nPrkD4QcN3dr0LObQTafjILnEU1loOXkFMDRyK0KPO0Xoxn0ZiJAlCg0iKHN26D/6l7dw+KX3rN/VtBmhh8v4r5aBR4VFgj1C56X/VUG0ZaYsyHIpM22xWhCfSbbzqcg357nnnkM4HMbDDz+85OcODg4u+30HBgaW9bwmZpzJ3v/gAzDJpU0+lGdf4tIlbASggTo+fjah4CoAY5cn8Z1/7seX/0MLQrKx6IdHogCAd06+j7ZGCZmsDlUzouoPhk4jmB0reC1hdhSNABQm4dLFYbQREURVHN/37MgUBEowMGCcSDKjF9HBCIYvXoSoXSn7MygbxtCcuzl8cRQKNWxa7jEuxmw8bt0+9eEpBC+OrPg1nWwcHk5Yt4dODWFq1J0pTOVQ6c9wpSTS+avNU6eG0N4kWTZemDCCjsvjYxgYSDg+vxqY9omT59CQu+/0mTNQZyrTY6lSrNih9/X14cKFC3jhhReKD4UtwY4dOxAILH1jY2BgAL29vUt+HgBc+FMAWjaFnbfcChpwJ+Is175hpKB+BIAKjo+fnk0h+icgnmI4PZmG3LgJvduNHN93LvwVQBxbr92G6zY1Y3w6CcCQvZpa1qG3d1vBa6XOyxj7N0Ny6b7uGmhnRUjQHd9XP9aPdEaz/jZ0+i9IMxE33ngDrt/izsb3JycowHRsufpqNO7sXdExLsblTy4DM8btm27eiYZI04per5iNs2wEeNN4oxt3bMfGjoYFj/ECNz7DlZJMZ4FXjGDjpht3YGz4I8vGuvPTwO8nsXnjRvT2XldNMy3sn2HqvIyx3Pmx+/oehDbf4Lk9mUymaCC8Is3h+eefx+DgIA4fPgzZoZe3bzEv6X1wySTkbCg2CUgQKDRGEE8akcu50Vnrb5msEemkchp6zKabz5aQXIxNUQE6EUCZc4SRymQRT2UtfV7PpJBhEoIO7QkqRi4gcDWdUKqC5OJT+aBa2PP/i26KLpK2WC38lvY8n7IsOnjwIO644w5cvnwZjz76KO69916cPn0av/zlLzExMYEHH3wQX/rSl/D444+7bW9FID7S0M1KUVY0y4VCB7E2Rc+NzVl/sxx6rsR6LlnaoRduilIwIkKECsYK245pOkMqY7z2ZDSVe24KaSZXfEi2nfxxcc+h29sriC5uiq71boulEEvsL5jauW9Pgva16YOkivmU9c3Zv38/9u/fv+D+jz76qOIGeYGVReGHCN0sHCqyOChBwWDj86N5h64UidAbwpI1CMCOlbYIw6EnqQgJGlSNQRLzXyB71sxkNIVNnQ1ANoU0RHcLOYj7EbroUYRuPz9zh14IpQSEAIwtdNxm9L5YHnq1sGdgrdosl5qDigCILwoDrF4uRXtHU+iMQCA6KCUYm4ojrRgON6PMc+i5CH1jRwNmE0aEbh8xl4/QJciSAJ2KkIiK7LwB1PamSlO5CB3ZNNJMQsBFh259QVx16Hlp0FXJhRcWFYWQ/ADpBZJL7lexBiSXms1DX20QKvgiOgfyEXqxyzeBEqSYjDBRcOM1rdAZMDIeAwAoqpGBMj9C39hRj9l4Bsmz7+LCf30UajwKwJa2aEouVIIEDUq2MCUymcmnMpoOnappQ3LxQkN38diIshGh68zdSlGuoZfGvCJcILkIPk9bXAt56LUGEURf6OeArTlXkcVBCcG0Xo8WGsff3LgeQF52yeQi9WQmr6GHgyJaGoOIJRSkRs+AqQrU6GUARoTOiDEoWZYEQBAhEQ3KvAg9ZYvQJ2dyTbm0DLKQXP2iEQ8kF3MQd7FCrkpRWCnq6lvVJMUi9KY6GeGgiM5WlyqSV4rPI/Q115wLyDkMn5xdrTFoxRw6JZjW63CdeBld29oRkAVrYzSTnR+hG/1bmuqNqerpKSM1TI0b6XO6koYuyABILkI3HHo2F+krkyMQmzstyYVSYkXogpaBSl3um+FBlouUi9AZ3PWya7197mKYAyrmO/T6sIz/9V/urYZJZUF83m3Rf6cYL6CCbw6GUMYGbZQ1oImm0NEo4ap1jfkIfV6WSyypoKFORlO9EYUqM+MAAC0nubBsGnrOKcuiAAgSJKjIKCrUuSlc/O/fxcwbRyzJZUN7HSajKTDGIOoZaC6NnzOxjomLWS75CN1lh25zVH6VD6qJqZHX2kfDNXQfQgTBNwfDjBgjTcULnGZYAyhhYIkZtDWHEI3nslXmZbnMJRU05iJ0ANBmJ4yfpoaupKEJhkOTJQoIIigBVEVB7P03AF3D3Hu/RzpuaPSbOxsxGU1BV1IggOsO3YssFznXNthtycWetsibcy1EKBKh+53Cbov+CArt+MOreQyhoi8yXIC8bhwKFneWs7liY3V2EiFZtCJye5YL0zVcn3gHn878GY1j70CABpI0pBYtJ7mwbAYaNU4gkihYrWRTySRi7/8BYvM6MCWN4IV/AwBsXtcAJathLmpcEeiCO+PnTKw8dBc3K6WANxF6QdpijTktLxDLHGThO3zeD90fXs1rfJTlYl0plLBHCxml9tnoBEJB0YrI7RF66txf8bf4C7rn3gT7139BhzAHkstd1xJRAEaErhIZlBKIAoGQy8nWLrwPdXYCLXd+DcHNN6D14p/QLY5ha3AGt8lnMDM+CQBgLs0TtfBAQ5dzDr1YM7RKwdMWS2Np6DV29VIwZN4nV/l2/GeRBxhZLj5x6GX0Zv/Bt+4BCDEi9IDh0FVNt9rbpjIqUpfOAADOXv1VQFdxi3zeeN1A2EpbZNk0UpqAtqYgCCFWCl/o9OugwXqEt92G5jsfhKCl8Xjj6+j488/w9fq/IP3X1w1DXHbo1hfERQ09EJCgM+Kphs79+UJqVnKhQv7yyy8+xMaadOhS8zqIzZ3VNgNA3omVikojTXUQGloth64zI+e8nc5hT/ADpNJZJC+dxrjWCK1rOwDg9uA5AEBww3WW5KIraSSyFJ0tRkqYuUEYiI8h8jdfAhVlhDZvx+tX/xNe0vYg+LffxJwehDj2gWGj7HKETkzJxcUIXaRQQd2P0Hn73JJIAgUltbm/YK5PHqH7hJa7vo71X3+22mYYlHm2l5raoc5OIBQwFlM0nsE/hN/BfeF3EcmOI3v5E4yorQg3NUNq34wIiUODgMC6rdASUTCmQ1fSiCkEHS1Gy2BTcknLETTettd6r7gCnA90o/3Wz+Os2glRMTR04mKrYaC8k9tKkSUBGqPQXXYkXHIpjSCQ2v1cFqkdqSZr0qH7iXxDqtKLQ4x0QI3mHfrcpfPYIV8EANzK/h1IzmBYbUVTvYzQph4AxmaqUN8CMB16MgaWTWNOIehsNjJqWMCI1E+17QEVZWSyGnSdIZlREQ4aU40uIj+pyq1WwxYejAaURKOwyssIvRajULcRBVqzVy75CJ07dM58ynRiYlM71Ng0QpLxJSD//hoUJmCctGGXZOjnI1or2iIhBDcbDn1CrYNQZ/T7VuMz0JU0FCais9VwzFrTerxAvo6z4nXIqjoePXACr781jFRaRThgRO9XQvm5jW4NiLbwoJcLIQQaBM8Ki2o2CnUZUaA1+9nkR1j6z336z6I1hqXDLZKqJzZ1AExHfXYGnTSK8OgA3sxci09CN0IgDAwEF9UWw6FvMpruT6l1UKRcyuPcFKBryDAJHbkIHYRAq2tFPKkgGssgllRwemQGyXQWoaCxaNWGLmRgaO2iWwOic3ghuQDGdKhi7YorhemsajUKdZtallyIIAKE+vLKizv0alNmhB7ash1ElBE++T/xcP2/QqMBnEjdhJmI4bxnxVYEwmEEZRFiYyti2+7Fm5lrEdWMjUx1xujnkmEiOlry0kl9SEY8lbWKlcamEkikDckFAJoaQrjA1kFlFPIyJkstCSoAIK5HPoxQ93u5kMKfnEJqWXIx0p796Tr9adVaoow8dMDIzGm/71sgk2exWbyC99r+DjEWgtzcjveVzRhUt6A9knfUwU/9A0a0NkxljOg6M34BAKASCa1NeemkPiQZDj1mdGIcnUoglc4iHMxVsDYE8H/j3fh/6esRDLgbORNCjZRSl7/oOsmdOFzEitC5R3ek1iUXP+rnwBptzuUnrA6DZeRe19/wGcxOXcEr/e9jGlsBjCNSH8D/iH8ehACf6sk76raIcXsyztAuBxH/4I/QIGAqfHVBdV5dWEI8mXfoU9EUCAHCOecdaQjgw+x6fJhdj2+7OdwCME5uLlaJmugQvJNcatRpuU1tSy6SLzNcgDIi9L6+PuzevRvbtm3Dxx9/bN1/7tw5PPDAA7jnnnvwwAMP4Pz5827auXqxdOPyFkjTp/4OJ9I3IRozJBKzbwtjQFsknyfe3BCEKBjdEoW6CMB0vC/fgkDLuoLXqw9JSKSzmInlR9YxBktyidTnX9PVXujIR+huw6gAty9OrU3RWpUVXCYgCZDE2hQI/NR+ez6LWnX33XfjyJEj2LBhQ8H9zzzzDB566CH09/fjoYcewtNPP+2akasZIoigoXojvbAMQrkoOZobMdfckNe125vzkgulBC1NIUzOpCA2tUOob8Zrie3obClMPawPydB1hstXEoXvk5Nc7K/v5rQiw2jB1XmiJjoR3Y/Qc47cjxtnfuDLd12Lf3rw1mqbsTzs1aI+Y1Grdu3aha6uroL7rly5gqGhIezdaxSj7N27F0NDQ5iennbHylUMoQI2/efDaLj5rrIeLwjGcApzCLTZKhfIyywm7ZEQpmZTaP/7b6DtwWcxHmMFG6IAUB82HPfFiTgawvnXsksuJq7OE0Uuy8WDCB3b7wHb/gVX38KaXl+jsoLbrG+rx43XtlXbjGWx6jT0sbExdHZ2Wr28BUFAR0cHxsbG0NJSXqRpMjg4uBwTAAADAwPLfq4XuGWfJDAk0sZQipHzp637p8eHMTAwbv1O9RRGJjP46yeXcGXO6HGeiI5jYCBuPWZ8bAQAcG50Bu1NErIqQVphGL14HgNsHNFEfnrRubNnkJ0dduV/AoC66CyErFrwubnxGYbWr6voazu9TixlNE5TtWzV12m1378c/G6j3b76RBKCqvnS5qpviu7YsQOBZaTDDQwMoLe31wWLKoOb9jX2zyCRNiSSz366Fz//7XHj9u07CyLwwctDODVyBjtvuRVD564AGEfvzddjZ3eHZePNO7bhf//pL0grDBvXtSIYTOH0SBQ37ejB9q2tULIaDv2fYwCAnTdvx5Z1ja78TwCQbBagRidw3a29ln1+PsZAcRujsQzwmzEEZLmq/0Mtf4Z+Yb59l8/0Q1HjVbM5k8kUDYSX5dC7urowPj4OTdMgCAI0TcPExMQCaYbjDmb5vyxSBGQBlAAMQEtTYfOs9uYQVI0hGksjOmdINM0NhY+pD+VllkhDAJJAcXokam2KypKAuqCIRFpFQHL3MjO8daerr+8lPMtlFeNjyWVZyn5rayt6enpw7JgRuR07dgw9PT1Llls4y8Os4gzIAgghCAaMwdBmj2kTU1OfiqYwncuKaW6c79Al63ZzfQBdbUY1qJmHDuR19KDLWS6rCV4punoJbroewc3bq22GI4t+Qw8ePIgTJ05gamoKjz76KCKRCI4fP45nn30W+/btwy9+8Qs0Njair6/PC3s5yG9OyrmIORQQF2yIAsamKABMRdOYmUtDFCgawlLBY+rDhY77qq4mDH5yBS2NAdv9QVyaTLi+KbqayFeKcoe+2ojcfn+1TSjKog59//792L9//4L7r7nmGrz00kuuGMUpjSm5mBJIR3MYm9c1LHic6dAno0lMz6XR3BhYkEYXCoiglEDXGSINAfRc3YIfP/7ZgsdEcrnussuSy2oi35yryoZw1hT8GroGsTT0nIN99h8/bU2AsVMXkhCUBUxGU5iZy6ClYeGACkII6oISYkmloIjITktT0HL8nPLgGjqnGnCHXoPYNXSgUO+2QwhBe3PI0tDXtzl3S6wP5xx6g3O20Zc/fy1uu8EfE55qBdOR88Iijpdwh16DzJdcStGWqxadmUtj+9ZWx8eYG6PFHHpbJOSo0XOKw0v/OdWAK3w1SHie5FKKtkgIl68kEEtm0dLoLKnU56SZkMvdFNcSXHLhVAPu0GuQpUTo7c1hxJJGlej8HHST1qbQgpYAnJVDCe+HzvEWHpLVIJZDLyONsN3egbHRWVL5T3tvQCqjOv6Ns3word0WsZzahDv0GmR+lksp7EMvnLJcAKMFr9mGl1M5KCFcQ+d4CpdcapDgUjZFm/ObmcUidI478Aid4zXcodcg+Qh98cPXmuvvQki+QIjjDZQSnrbI8RQuudQgS9HQg7KIxjoZlBLH4iOOe1BCeD90jqdwh16DmJ0Qy+1+2BYJGe0YOZ5CuIbO8Rju0GuQ5oYg7v3M1ei9vrzqzfs+uxWMcY/uNQIl4P6c4yXcodcglBJ848s3lf34PbdtdtEaTjEo5YVFHG/hoiqH4xKU8CwXjrdwh87huASlXEPneAt36ByOS/A8dI7XrFhDf+ONN/Dzn/8cjDEwxvCtb30LX/jCFyphG4dT0/AsF47XrMihM8bw1FNP4ciRI+ju7saHH36Ir33ta9izZw8oH9XCWeO0NYUWDO7mcNxkxRE6pRSxWAwAEIvF0NHRwZ05hwPgmX/8NI/QOZ6yIodOCMGhQ4fwzW9+E+FwGIlEAr/61a8qZRuHU9OUW/jF4VQKwlZQcaKqKh577DE88cQT6O3txcDAAL773e/i+PHjqKtzHndmkslkMDg4uNy35nA4nDXNjh07EAgU9mdaUYR+6tQpTExMoLe3FwDQ29uLUCiEs2fP4qabyit8cTKqHAYGBqz39SN+tw/wv41+tw/wv41+tw/wv41+s69UMLwisXvdunW4fPkyPvnkEwDA2bNnceXKFWzezCsTORwOx2tWFKG3t7fj2Wefxbe//W2rTeiPfvQjRCKRStjG4XA4nCWw4iyX+++/H/fff38lbOFwOBzOCuD5hRwOh7NKqFq3RTO5RlGUZb9GJpOplDmu4Hf7AP/b6Hf7AP/b6Hf7AP/b6Cf7TJ/plKC4orTFlRCLxfDxxx9X4605HA6n5unu7kZDQ0PBfVVz6LquI5FIQJIkPneRw+FwyoQxhmw2i7q6ugVV+VVz6BwOh8OpLHxTlMPhcFYJ3KFzOBzOKoE7dA6Hw1klcIfO4XA4qwTu0DkcDmeVwB06h8PhrBK4Q+dwOJxVQtVK/5fLuXPnsG/fPkSjUUQiEfT19eGqq66qmj0zMzN46qmnMDw8DFmWsWXLFhw4cAAtLS3Ytm0buru7reT/n/zkJ9i2bVtV7Ny9ezdkWbZ6zz/55JP43Oc+h/feew9PP/00MpkMNmzYgJ/+9KdobW311LaLFy/i8ccft36PxWKIx+N46623itrtBX19fejv78elS5dw9OhRdHd3Ayi9Br1cn072lVqPADxfk8U+w1LH1cs16WRfqfW4mO1Vh9UYjzzyCHv11VcZY4y9+uqr7JFHHqmqPTMzM+zNN9+0fv/xj3/Mvve97zHGGOvu7mbxeLxaphVw1113sY8++qjgPk3T2J49e9jbb7/NGGPs8OHDbN++fdUwr4CDBw+yH/7wh4wxZ7u94u2332ajo6MLbCi1Br1cn072lVqPjHm/Jot9hsWOq9drsph9duzrkbHqrsnFqCnJ5cqVKxgaGsLevXsBAHv37sXQ0BCmp6erZlMkEsHtt99u/b5z506Mjo5WzZ6lMDg4iEAggF27dgEAHnzwQbz22mtVtUlRFBw9ehRf+cpXqmoHAOzatQtdXV0F95Vag16vTyf7/LYenWwshddrcjH7/LQey6GmJJexsTF0dnZCEIzhu4IgoKOjA2NjY9YlZTXRdR0vvvgidu/ebd33yCOPQNM03HHHHXjiiScgy3LV7HvyySfBGENvby++853vYGxsDOvXr7f+3tLSAl3XLbmgGvzhD39AZ2cntm/fbt033+7Gxsaq2AaUXoOMMV+tT6f1CPhnTTodV7+tSaf1CPhrTdqpqQjd7zz33HMIh8N4+OGHAQB//OMf8corr+DIkSM4c+YMDh8+XDXbjhw5gt/+9rd4+eWXwRjDgQMHqmZLKV5++eWCaKhW7PYj89cj4J81WSvHdf56BPxte0059K6uLoyPj0PTNACApmmYmJhY0iWdW/T19eHChQs4dOiQteFk2lVfX4+vfvWrOHnyZNXsM22RZRkPPfQQTp48ia6uroLL8enpaVBKqxadj4+P4+2338Z9991n3edkdzUptQb9tD6d1qNpP1D9NVnsuPppTTqtR8B/a9JOTTn01tZW9PT04NixYwCAY8eOoaenp+pyy/PPP4/BwUEcPnzYunydnZ1FOp0GAKiqiv7+fvT09FTFvmQyiVgsBsBovfm73/0OPT092LFjB9LpNN555x0AwK9//Wt88YtfrIqNAPCb3/wGd955J5qbm0vaXU1KrUG/rE+n9Qj4Z02WOq5+WpPz1yPgzzVpp+ba5549exb79u3D3NwcGhsb0dfXh61bt1bNntOnT2Pv3r246qqrEAwGAQAbN27EY489hqeffhqEEKiqiltuuQXf//73UVdX57mNIyMjeOKJJ6BpGnRdxzXXXIP9+/ejo6MDJ0+exDPPPFOQItbW1ua5jQBwzz334Ac/+AHuuOOORe32goMHD+LEiROYmppCc3MzIpEIjh8/XnINerk+new7dOiQ43o8fPgw3n33Xc/XpJONL7zwQsnj6uWaLHaMgYXrEaj+mlyMmnPoHA6Hw3GmpiQXDofD4RSHO3QOh8NZJXCHzuFwOKsE7tA5HA5nlcAdOofD4awSuEPncDicVQJ36BwOh7NK4A6dw+FwVgn/H4lKm3SGKc5AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-c8d7d5cf5ebb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mmy_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0minterval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_range\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmy_range\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"original\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_range\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_preds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmy_range\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"predicted\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Test and predicted data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not range"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test=ho_pheno\n",
    "x_ax = range(len(y_test))\n",
    "print(x_ax)\n",
    "plt.plot(x_ax, y_test, label=\"original\")\n",
    "plt.plot(x_ax, all_preds, label=\"predicted\") \n",
    "\n",
    "plt.title(\"Test and predicted data\")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "i = 0\n",
    "interval = 65\n",
    "while(i<len(y_test)):\n",
    "    i = i + interval\n",
    "    if(i>len(y_test)):\n",
    "        my_range = range(i-interval,len(y_test))\n",
    "    else:\n",
    "        my_range = range(i-interval,i)\n",
    "    plt.plot(my_range, y_test[my_range], label=\"original\")\n",
    "    plt.plot(my_range, all_preds[my_range], label=\"predicted\") \n",
    "\n",
    "    plt.title(\"Test and predicted data\")\n",
    "\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'targs': [x for x in pheno], 'preds': [x[0] for x in model.predict(vcf)]}\n",
    "\n",
    "testdf = pd.DataFrame(data=d)\n",
    "testdf['minus'] = testdf['targs'] - testdf['preds']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ss = sqrt(mean_squared_error(testdf[\"preds\"], testdf['targs']))\n",
    "mm = np.mean(pheno)\n",
    "print(\"RMSE for dataset is:\" +str(ss) + \"& pheno is \" + str(mm))\n",
    "print(\"this is \"+ str((ss/mm)*100) + \"% of the mean pheno data\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}