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@sshojiro
Created January 22, 2020 05:09
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PLS-VIP.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "PLS-VIP.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/sshojiro/8537c4014a704d0176b9b61d992642d1/pls-vip.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WxFF67B5avmg",
"colab_type": "text"
},
"source": [
"# PLS VIP value."
]
},
{
"cell_type": "code",
"metadata": {
"id": "1FNjr6ngZlBf",
"colab_type": "code",
"outputId": "c8754099-c29c-46fc-a8b6-718d9d93ea43",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 199
}
},
"source": [
"!git clone https://github.com/HIPS/neural-fingerprint.git\n",
"import pandas as pd\n",
"df = pd.read_csv('./neural-fingerprint/data/2015-05-24-delaney/delaney-processed.csv')\n",
"\n",
"!curl -Lo rdkit_installer.py https://git.io/fxiPZ\n",
"import rdkit_installer\n",
"%time rdkit_installer.install()\n",
"import rdkit"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"fatal: destination path 'neural-fingerprint' already exists and is not an empty directory.\n",
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
" 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n",
"100 2415 100 2415 0 0 3659 0 --:--:-- --:--:-- --:--:-- 3659\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
"rdkit is already installed\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"CPU times: user 1.94 ms, sys: 0 ns, total: 1.94 ms\n",
"Wall time: 2.01 ms\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jc1Df44UZtGr",
"colab_type": "code",
"colab": {}
},
"source": [
"import numpy as np\n",
"\n",
"from sklearn import metrics\n",
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
"from sklearn.cross_decomposition import PLSRegression\n",
"\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from rdkit.Chem import rdMolDescriptors, Descriptors, MolFromSmiles\n",
"from rdkit.Chem.Draw import IPythonConsole\n",
"from rdkit.Chem import Draw\n",
"mpl.rcParams['figure.figsize'] = [12, 8]\n",
"mpl.rcParams['font.size'] = 30."
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4RRz4G3id4MU",
"colab_type": "code",
"colab": {}
},
"source": [
"df['mol'] = df.smiles.apply(MolFromSmiles)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ILbuhVUTbULb",
"colab_type": "code",
"colab": {}
},
"source": [
"count_frags =lambda moleobj: np.array(list(map(lambda m: getattr(Descriptors, m)( moleobj ),\n",
" [method for method in dir(Descriptors) if method.startswith('fr_')])))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bDYg_QvTcGG8",
"colab_type": "code",
"colab": {}
},
"source": [
"methods = [method for method in dir(Descriptors) if method.startswith('fr_')]\n",
"n_descs = len(methods)\n",
"n_samples = df.shape[0]\n",
"X = np.zeros((n_samples,n_descs))\n",
"for ix, mol in enumerate(df.mol):\n",
" X[ix,:] = count_frags(mol)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PyxMCYK0crO8",
"colab_type": "code",
"colab": {}
},
"source": [
"TARGET = df.columns[8]\n",
"M = pd.DataFrame(X, columns = methods)\n",
"A = pd.concat((df,M),axis=1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-DQc2hgLdE40",
"colab_type": "code",
"outputId": "3dff21ea-c70b-42e5-9782-4858f1d70ca4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 163
}
},
"source": [
"\n",
"\n",
"train, test = train_test_split(A, test_size=0.33, random_state = 42)\n",
"params = {'n_components': np.arange(1, 16)}\n",
"model = GridSearchCV(PLSRegression(), params, cv=3)\n",
"model.fit(train[methods], train[TARGET])"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GridSearchCV(cv=3, error_score=nan,\n",
" estimator=PLSRegression(copy=True, max_iter=500, n_components=2,\n",
" scale=True, tol=1e-06),\n",
" iid='deprecated', n_jobs=None,\n",
" param_grid={'n_components': array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15])},\n",
" pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
" scoring=None, verbose=0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wG6HipsueCbf",
"colab_type": "code",
"outputId": "7bbaf18e-b0ee-45ad-9234-299c832c9305",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 361
}
},
"source": [
"with mpl.style.context('seaborn'):\n",
" fig = plt.figure()\n",
" ax = fig.gca()\n",
" plt.plot(train[TARGET], model.predict(train[methods]), 'o')\n",
" plt.plot(test[TARGET], model.predict(test[methods]), 'o')\n",
" xlim = ax.get_xlim()\n",
" ax.set_ylim(xlim)\n",
" plt.plot(xlim,xlim, '-k')\n",
" plt.axis('square')\n",
" plt.xlabel('Measured log solubility')\n",
" plt.ylabel('Estimated log solubility')\n",
" plt.legend({'train', 'test'})"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
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sh3yGGNBpdOAF3mWi8wsla+XPzSiXFWMAkhCLOWJnIQaAJGsOJiRl4Yv6Itj0LZKoivfz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\nBH4B0S6vKrmquqYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x396 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "WFGU1hVSfJTG",
"colab_type": "code",
"outputId": "ecc376a0-aaab-45e4-aa12-84e28d3d7ebb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 53
}
},
"source": [
"print(np.sqrt(metrics.mean_squared_error(train[TARGET], model.predict(train[methods]))),\\\n",
" np.sqrt(metrics.mean_squared_error(test[TARGET], model.predict(test[methods]))))\n",
"print(metrics.r2_score(train[TARGET], model.predict(train[methods])),\\\n",
" metrics.r2_score(test[TARGET], model.predict(test[methods])))"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"1.1153743787284136 1.1615487203232076\n",
"0.710121766066296 0.7032509664048506\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yuRShxT4n7u-",
"colab_type": "text"
},
"source": [
"$$\n",
"VIP_j = \\sqrt{ \\frac{d \\sum_{k=1}^hSS_k(w_{k,j})^2}{\\sum_{k=1}^hSS_k} } \\\\\n",
"\\textrm{such that} \\\\\n",
"SS_k = q_k^2 {\\bf t}_k^T{\\bf t}_k\n",
"$$\n",
"\n",
"where $d$ denotes the number of the variables in $X$, \n",
"$ {\\bf t}_k$ denotes the x_scores of the k-th component, \n",
"$q_k$ denotes the y_loading of the k-th component.\n",
"\n",
"Let $W$ $d$-by-$A$ matrix, $SS$ 1-by-$A$ matrix. Then, \n",
"\n",
"$$\n",
" \\sqrt{ \\frac{d \\times W\\times SS'}{\\textrm{sum}(SS)} }\n",
"$$\n",
"\n",
"would be the VIP of the whole variables ($j=1, \\ldots, d$)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "9J4vMuSafrty",
"colab_type": "code",
"colab": {}
},
"source": [
"def vip(pls_model):\n",
" n_dims = pls_model.x_weights_.shape[0]\n",
" n_components = pls_model.n_components\n",
" t_a = pls_model.x_scores_\n",
" w_a = pls_model.x_weights_# d x a\n",
" q_a = pls_model.y_loadings_\n",
" denom = list(map(lambda ix:(q_a[:,ix] **2.)*t_a[:,ix].T@t_a[:,ix], range(n_components)))\n",
" denom = np.matrix(denom)\n",
" numer = ((w_a **2.) * denom.T).T\n",
" return (n_dims*numer)/denom.sum()\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2EYIdOW2dmDe",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 573
},
"outputId": "98000d0b-e295-4858-e658-cdaff1d37856"
},
"source": [
"vipval =vip( model.best_estimator_ )\n",
"ixshow = np.where(vipval>1)[1]\n",
"print(len(ixshow))\n",
"plt.figure()\n",
"plt.plot(vipval.T, '-o')\n",
"plt.plot(ixshow, vipval[:,ixshow].T, 'o')\n",
"plt.xlabel('variable index')\n",
"plt.ylabel('VIP value')\n",
"plt.show()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"14\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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0R9Ie59yDZvZaSR9ewmP+i3PuoS6sDQC6wg/cgtKdajMuGX0AQEodB/pmtlnSn0l6pWpZ\ndpPkJL2+7ty/lPQGSQecc+d2+pyS5Jx711LuDwD9oOLXNeMWaMYFAGTTUTOumZ0v6S5J1yj8sGDR\nTzP/Izpvu5k9v5PnBIBh4vkLS3cKBVPBaMYFAKSXOdA3s7Kkf5L0FIXB/cckvUjSrza7j3Pue5K+\nF/364uzLBIDhUgmcisWF+ZNSsaAKc/QBACl1ktF/vcLGVyfpl51z1zrn/lXSwTb3u03hB4PLO3jO\n5fZXZvawmc2Z2TEz+56Z/ZWZPbfXCwMwnPzAVct1YuWCkdEHAKTWSaAfj7b8snPuAxnu993o8oIO\nnnO5vVDSVkkjktYpbCx+g6SvmNk/mdlpvVwcgOHinFvUjCuFDbk04wIA0uqkGfcZCrP5n854v6PR\n5YYOnnO5HJf0rwon+RyQ5Es6S9JPRD+S9FMKA/7nOOeme7JKAEOlEmXtk8248e804wIA0uok0I+z\n249lvF+rZt1eeJ+kNznnTjS47T1mdqWkv5e0WdLFkt4j6Y3NHszMrpN0nSRt27at+6sFMDS8qA6/\nWKiv0ad0BwCQXielO1PR5dqM9zsrujza8qwV4pzb0yTIj2//msIypfhd9XXRngHNzv+Ac+5S59yl\nmzZt6vJqAQyTOGtfqg/0CzTjAgDS6yTQfzC63JXxfi+MLvd18Jw94Zy7XdK/RL8WFU4XAoBl5TUt\n3SGjDwBIr5NA/18VluG82sxS1dub2aUKa96dpC908Jy9dFvi+kW9WgSA4RE33DZsxiWjDwBIqZNA\n/xZJc5LWS/qEmY23OtnMnq6w1t0kTUv6UAfP2UvJUqP1PVsFgKFRaVK6Uy4WyOgDAFLL3IzrnDtg\nZu+Q9IeS/qOk75vZ+yWNxudE8+e3KsziX6NwbKWT9F/7cHLN6Ynrx3q2CgBDo5rRr5ujXyoaU3cA\nAKl1MnVHzrl3m9lmSf9F0jZJ74pvii6/nDg9Tkm9wznXb9l8SXpe4vq9PVsFgKFRbcat3xm3UFCF\nOfoAgJQ6Kd2RJDnn3izp5ZK+ozCYb/bzXUlXO+fevuTVrjAze45qDbiB+q+/AEAfohkXANANHWX0\nY865z0j6jJk9U9KVkrYr3Fl2RtJBSV9xzt251EV2m5m9RuE+AF90zjV81zSzKyR9SrVvJD7qnDuw\nQksEMMQq1dIdmnEBAJ1bUqAfc859R2Fmf9mZ2TmSXl93+JmJ61eZWf2/61POubsSv+9QWHZ0wMy+\nIOluSZNavDNu/C77XUlv7s6/AABaa1a6Uy4WNOd5vVgSAKAPdSXQX2FnS/rdFrdfGf0k3Sfprgbn\nbpX0hjbP94+S3uicoxEXwIrwgybNuAVKdwAA6fVjoN8NN0u6U9KPKczunyFpo6QxhTv/Pijp3xSW\n6+zt1SIBDKeK36QZt0gzLgAgvb4L9J1zt6lWUtPpYxyU9PHoBwBypWUzLuM1AQApZQ70zcxf4nM6\n51zffcAAgJVSiUp3iouacQvVGfsAALTTScC9pGw6AKA1P87o19Xolwtk9OvtvvUWbd17sza7SR22\nTTqw4wbtuvr6Xi8LAHKhk0D/q6ptjNVMQWHN+wWSitH5d0k63sHzAcBQiUdoLq7Rpxk3afett+ji\nPTdq3OYlk7ZoUuv23KjdEsE+AKiDQN859/y055rZeklvknSjpFWSrnHO3Z/1OQFgmFSqNfqLm3GZ\no1+zde/NYZCfMG7z2rr3ZolAHwA63xk3DefcMefcH0r6GUkXSrrVzFYt53MCQL/zqjX6i0t3KmT0\nqza7ySbHj6zwSgAgn5Y10I855z4n6VZJFynM8AMAmojLcxbvjEszbtJh29Tk+MYVXgkA5NOKBPqR\nf1bYyHvNCj4nAPSduOGW8ZqtHdhxg2bdyIJjs25EB3bc0KMVAUC+rGSg/0R0ee4KPicA9J04a9+w\nGZdAv2rX1dfrm8+4SY8EGxU40yFt0r6d76QRFwAiKznP/mnRZXkFnxMA+k6lSelOqVCQHzg552TG\npGNJ2v781+qKO7frlTvP0p/+7LO0pdcLAoAcWZGMfjR955cVjtl8cCWeEwD6VW285uLSHUk05CbM\necGCSwBAzbIF+mZWMrNtZvZaSd+UtD266dPL9ZwAMAji8pxGzbjh7QS1sTkv3Kx9rrLUTdsBYPBk\nLt0xs6W8mv5Q0p8u4f4AMPA8v3kzriTq9BPI6ANAc51k9K3Dn69Juso5N7X0ZQPA4PL8QGZScVGN\nfhToU7pTNVeJA30y+gBQr5Nm3K8qrLVvZ07SMUnfk/R559wdHTwXAAydSuAWle1ItZp9ZunXVEt3\nyOgDwCKZA33n3POXYR0AgIgfOJUKi79wrTbjUrpTNR+X7lQI9AGg3krO0QcApFDxg0Uz9KVEMy4Z\n/ao4kz/P3wQAFiHQB4Cc8Xy3qBFXohm3kVrpDjX6AFCPQB8AcsYLgkWNuJKq5Tw049bMUboDAE0R\n6ANAzni+U7lhM268YRZBbaw2dYe/CQDUa9qMa2YPLNNzOufcucv02ADQ97zALdoVV6J0pxFKdwCg\nuVZTd7YrHKO5OK20NLxDAUALNOOml9wwyzkns26/ZQFA/2oV6D8sgnIAWHGe33iOflzOQ0a/Jh6v\n6ZxU8Z1GSgT6ABBrGug757av4DoAABEvCBrO0a9tmEWgH0vW5s95vkZKtJ4BQIxXRADIGS9w1Xr8\npGozbkDpTixZmz9PQy4ALECgDwA54/lNmnEZr7lIcqwmk3cAYCECfQDImYrfeI5+fIxm3JqFpTv8\nXQAgiUAfAHKmWekO4zUXS5buMGITABZqNXUnFTM7U9Jlks6SNCGp2O4+zrl3LPV5AWBQeYFr3YxL\njX7Vgow+u+MCwAIdB/pmtlPSn0h6fgd3J9AHgCY8P2jcjFuId8Ylox+jdAcAmuso0Dezl0v6hKSy\n2m+oVb/pFu9QANBCOEe/0c64NOPWm/MCrR4p6sS8T+kOANTJHOib2RmSPippRNIJSe+R9G+SPq8w\niP89Sd+SdLakF0n66eiuH5X0kSWvGAAGXCUIVGy4M25co0/mOjZX8TUxXtaJeZ/xmgBQp5OM/n+W\ntFphUP9S59xtkpLbju9zzn0uuv5+M9sh6VOSXiPpXufcu5e0YgAYcH7gqrvgJlWbccnoV817gSbG\nynps6hSlOwBQp5OpOz+hMMj/Uhzkt+Kc2xvdZ07SO8xsVwfPCQBDo9kcfZpxF5vzAk2Ml6LrlO4A\nQFIngf550eUXm9xerj/gnNsv6WMKJ/Jc18FzAsDQqNCMm9qc52vtWPi2w9QdAFiok0B/XXT5cN3x\n+ehydZP73R5dPq+D5wSAoeEFruGGWTTjLjbnBVo7VqpeBwDUdBLoz0aX9a+oU9Hltib3i89/SgfP\nCQBDw/ODhlN34tif0p2auahGP7xO6Q4AJHUS6MeZ/E11x/dHl89ucr9ndvBcADB0mu2Ma2YqF42d\ncSPOubAZN67Rp3QHABboJND/VnR5cd3x2xXOy3+hmV2SvMHMtku6XmET7z0dPCcADI1mzbiSVCoU\n5PkEtFKtVGfVSEnFgmmevwsALNBJoH+bwoD+qrrjf62wPKco6TYz+2Mzu87M/ljSHklro/M+2eFa\nAWAoVIKg2nhbr1Q0mnEjcaA/WipotFSgRh8A6nQS6H9GYUB/rpn9aHzQOfc9hZtnmcKg/v+T9P7o\ncn102t2S3ruUBQPAIPMDJ+fUsEZfChtyqdEPxTX51UC/Qo0+ACRl3jDLOXfEzM5XuDPu4brbftvM\npiW9RYun7/yDpOucc6c6XSwADLo4iC81qNGXwt1xfWr0Jam6E+5oqajRUpGMPgDU6WRnXDnnHmxx\n2x+a2Xsk/ZikMySdlHSnc+7RzpYIAMMjHp3ZqBlXksoFSndi1dKdckEjlO4AwCIdBfrtRFn7Ly/H\nYwPAIIsD/WKT0p1SkWbcWDxlp1ajT+kOACR1UqMPAFgmlah0p1lGv1Q0VSjdkZSs0S9qtFxgvCYA\n1Mkc6JvZbjP7NTOrn6MPAFiiuP6+aTMu4zWrFk7doUYfAOp1ktHfKenPJB00s8+a2avNbKzL6wKA\noVTxacZNK1mjP1oqVJtzAQChTgJ9T+EIzZKkF0v6n5IeN7MPmVn9bH0AQAZtm3GZo18VB/YjxSI1\n+gDQQCeB/hZJb5L0DYUBfzw3/1pJ/2pmD5vZu82sfudcAEAb8XjNls24zNGXlKjRL1O6AwCNZA70\nnXNPOOfe75x7jqRzJd0k6V7Vgv6zJP2WpG+b2V1m9mYz29LFNQPAwPKispxys51xGa9ZlZy6w3hN\nAFhsSVN3nHMPOufe4Zy7SNLlkt6ncBOtOOh/pqQ/lXTAzL5gZr9gZquWumgAGFRx6U6p2GJnXJpx\nJSWbcYvsjAsADXRtvKZzbrdz7r9IOlPST0n6hKRZhQF/UdILJX1U0qFuPScADBqacdOrjdcshOM1\nyegDwAJdn6PvnPOdc//snPsFhTvjXivpXyUFCoP+1d1+TgAYFF51vCbNuO0snLpDjT4A1FvuDbOC\n6Id3JQBIoVq606wZt0Azbiyu0R8pMl4TABopdfsBzcwk/YSkX5T0MtUy+HF66mS3nxMABoWXYmdc\nj4y+JGne91UsmErFMKM/7wcKAqdCk29DAGDYdC3QN7OdCoP7V0vaHB+OLgNJX5L0MUmf6tZzAsCg\nSdOMWyGjLynM6I+Wwr/TaDm8nPcDjRWKvVwWAOTGkgJ9MztbYXD/C5IujA8nTrlbYXD/N865R5fy\nXAAwDKrNuE2y0sWCySejLyms0Y8D/ZHog9FcJdBYmUAfAKQOAn0z2yDpVQoD/Gcnb4ouH5X0N5I+\n7pz7zpJXCABDpNqM22pnXKbuSAqn7oyWwqA+zuiHk3jKPVwVAORHJxn9Q4n7xe9EM5L+UWH2/v84\n53gXAoAO1KbutGjGZY6+pCijHwX4ccDP5B0AqOkk0I9TJb6kLyoM7v/ROTfbtVUBwJCKg3iacdtb\nUKNfSmb0AQBSZ4H+XQqD+0845x7v8noAYKjFQXyx6Rx9mnFj835QK92pBvr8bQAgljnQd87tXI6F\nAABUDeLLTabusDNuzZzna6Q6dYfSHQCot9wbZgEAMvDb7YxbCHfGpRWqSelOhUAfAGIE+gCQI5U2\nc/Tj42T168ZrUqMPAIv0XaBvZkUzu9jMXmtm7zOzb5jZSTNz0c9NHTzmi83sb83sh2Z2yswOm9nt\nZvZmM1vd/hEAoDvSNONKtek8w2zBeE1q9AFgka7tjLuCPinpFd14IDMblfQRhbv5Jm2Kfp4t6U1m\n9gr2BACwEuIAvmkzbjR2s+KzMRTjNQGgtb7L6Euqf2d7QtL+Dh/rr1UL8o9Kerekn5f065LuiI6f\nK+nzZra1w+cAgNTiqTvlJnP04w8AlO40q9GndAcAYv2Y0b9D0vcl7ZG0xzn3oJm9VtKHszyImb1M\n0jXRrw9LutI593Di9r+Q9EFJr5P0FEn/TdLPLnn1ANCCFwQqmFRoOl4zPF5hlr7m/SAxdYfSHQCo\n13eBvnPuXV16qJsS138lGeRHzxOY2ZskvUDSNkmvNLOLnXP7uvT8ALBIxXdNG3GlWjOuxyx9zVWS\nNfrh5TyBPgBU9WPpzpKZ2fmSLol+3e+c+1yj86Ldfv8qcehVy702AMPN84OmozWl2thNdsddOHWH\nZlwAWGwoA31JL0pc/0Kbcz+fuP7iZVgLAFR5gWsZ6McbaVX84Q5oPT+QF7gGU3eo0QeA2LAG+hcn\nru9pc+63JMXvHP/BzJq/AwPAEnlB0HRXXIlm3Nh89EEnrs03M40UC2T0ASAhdY2+mZ0m6SWSniFp\nvaRpSXdL+pxz7ujyLG/ZXJC4/lCrE51znpkdVFinv1rSmZIeWb6lARhmnu+qs/IboRk3FO+AG2fy\n4+vsjAsANakCfTP7TYXNq402j5o1sz9wzv1xNxe2zNYnrh9Jcf5RhYF+fF8CfQDLouI7lZqM1pRU\nvW3Ym3GrGf1SbeLyaLlA6Q4AJLQt3TGz35V0s6Q1kqzBzypJ7zKzty/jOrttTeL6qRTnzyaur210\ngpldZ2Z3mtmdk5OTS1ocgOHlBUHLjH6JjL6kWkZ/ZEFGv0jpDgAktAz0zexcSW9LHHpY4RSad0WX\nP4xPlfQ7ZnaBhpRz7gPOuUudc5du2rSp18sB0KfSNuN6Q96MG2fu60t3GK8JADXtSnfeEJ3jJP2F\npN9wzlW/FzWzoqT3KNxJtuCwV9IAACAASURBVCDplyS9ZXmW2lUzietjKc4fT1w/3uW1AECV59OM\nm0acuU8G+iMlSncAIKld6c6V0eW3nHO/lgzyJck55zvnfkPSXoVZ/ecuwxqXw7HE9Y0pzj+9yX0B\noKs831WD+UaqzbhDH+hHGf1yskaf0h0ASGoX6F+gMJv/iTbn/a/o8rwlr2hl3Ju4vr3ViWZWUjhp\nR5JOSDq4TGsCAFWCNjvjFijdkZpM3SkydQcAktoF+uuiywfbnPdQ3fl5ty9xfWebcy+RFKeMvuec\nG+40GoBl5QeByq12xqUZV1Lj0h2m7gDAQu0C/XJ0WWlzXnx76rn8PZbcDfdFTc8KJXfD/XzTswCg\nCypt5+gzXlNKBvqJ0p0SG2YBQNJQ7ozrnNsv6a7o1/PN7CWNzjOzMUlvTBz65HKvDcBwoxk3nThz\nz3hNAGhuKAP9SHLu//vNbFvyRjMrKJw0FB//e+dcsuQHALrOC9o040Y1+pTuNN4Zl/GaAFCTttTm\n1WZ2SYvbL4qvmNnvt3sw59w7Uj7vImZ2jqTX1x1+ZuL6VVEDbdKnnHN3JQ845z5jZn8r6RpJZ0va\na2a3SLpb4ZSd10i6LDr9MUm/2emaASAtr93OuFFZz9A348aBfpkafQBoJm2gf02Kc+L00ttanhXq\nONBXGJT/bovbr1RtLGjsPtVKdZKuVbjuVysM7n+nwTn3S3qFc+5A9qUCQDZeEFRHaDZSYrymJGmu\nEm+YlazRp3QHAJLSlO5Yl39ywzk355z7OUkvkfR3kg5ImpN0RNI3FGbxn+Wc+07vVglgmHh+6/Ga\nZcZrSmqxYRbjNQGgql1G/+1tbl9xzrnb1OUPDM65z4uJOgByoBIEKrWo0S8WacaVVK3Fr6/Rn/N8\nOedklqu8EgD0RMtA3zmXu0AfAAZZWKNPM247c16gkWJhQUA/WioocGFDc6vyJwAYFsM8dQcAcsdr\ntzMuzbiSwvGayWy+VKvXp04fAEIE+gCQI+Ec/RbNuAWacaUwmE9O3JFqE3jiRl0AGHYE+gCQI57f\neo6+malUMDL6lWDBxB2pVq8/P+R/GwCIEegDQI5UgtY740rh7rjD3ozbsnSHyTsAIKlNM26aza86\nsZQNswBgkPlB62ZcSSoXCzTjeoFGFgX6heptAID24zVvUm0jrG4i0AeAOs45VdrM0ZfChlwvGO5g\ndt4LNFpeWLozUg30qdEHACndzrjdnlE23GkoAGgiLsdpl9EvFcjoz3m+RotM3QGAVvpuwywAGFRe\nHOi3mQFfLtKMO+cFWjO68C2sNnVnuP82ABBjwywAyIk40I83xWqGZtwwmD99dbMafUp3AEBi6g4A\n5EacpW+f0S8wR9/zG4zXDH+fp3QHACS1CfTNbMtKLQQAhl1cd9+2GZc5+uGGWUzdAYCW2mX0Hzaz\nW83s5WaWpnEXANCheJJO22ZcxmtGU3ea7IxL6Q4ASGof6Jck/ZSkv5f0qJn9dzN71vIvCwCGj+en\nm7pTZrxmlNGvG69ZJKMPAEntAv3jCsdrmqTTJf26pL1mttfMftXMTlvuBQLAsKg247Izbltznr94\nw6wyO+MCQFK7QH+LpGslfSn6PQ76nyXpzxVm+T9pZj9pZjT2AsASpG7GLRRUGeIafedcmxp9SncA\nQGoT6DvnZp1zH3POvVDSdklvk3S/agH/iKSfkfS/JR0ws3eZ2QXLu2QAGEyVlKU7paJVy3yGUcV3\nck6LAv1SwVQwSncAIJY6C++cO+Cc+wPn3PmSnivpw1pY2rNF0m9L+r6Z3W5mrzeztcuxaAAYRLVm\n3DZTd4Z8vGacsa+v0TczjZaKjNcEgEhH5TbOua87516vMLh/raQvRzfFQf+PSvqApMfM7K/N7Me7\nsFYAGGipd8Yd8vGacSBfP3UnPkZGHwBCS6qrj0p7Puqce4GkcyTdpIWlPask/aKkL5rZA2b2+2Z2\n9hLXDAADKS7HoRm3tTiQry/diY9Row8Aoa410DrnHnbOvSMq7XmepL+WNKNa0L9dYY3/fd16TgAY\nJHGWvth2vOZwN+PGgX791J34GFN3ACC0LJNynHNfc869TmFpz+skPSbJKQz4mc4DAA1UquM1UzTj\nDnVGv3GNfnyM0h0ACC3bbrdmtk3Sa6KfLcv1PAAwKPy0zbiFwlBP3Ykz9pTuAEBrXQ30zWyVwnGb\nr1VYvhOnpeLLGUmf7OZzAsCgqI7XbNeMWzRKd9Qso08zLgDEuhLom9lzFQb3r5S0Oj4cXTpJX1E4\njvPvnXMnu/GcADBovOocfZpxW6mW7jSaukPpDgBUdRzom9l2hbvmvkZho61UC+4l6WGFDbkfcc49\n2OnzAMCwqM7Rb5vRH+5m3PlWU3fKBT15srLSSwKAXMoU6EelOa9SGOBfqcWlObOS/lFh9v5Lzrnh\nTTkBQEbV8Zpta/SHvRm3TelOhRp9AJBSBvrRhlfXSnqFFpfmSNIdkj4k6X8556a7ukIAGBJpM/ql\n4pA340alO43Ga7IzLgDUtAz0zeztCktztsWHEjc/Luljkj7snPv+8iwPAIZHpmbcYHiD2VZTd0Zo\nxgWAqnYZ/d9Tbf69JFUk/ZPC0px/ds7x/SgAdEm8YVaaZlznpCBwKrTZXGsQsTMuAKSTpnTHJH1H\nYXD/P51zR5Z3SQAwnOK6+zTNuJJUCQKNFhbXqQ+62tSdJhtmsTMuAEhqH+j/D4WlOXetxGIAYJjF\ngX6aZlwpbN4dXbZtD/Or3dQdSncAINTyLcI59+srtRAAGHbV0p0Uzbjh+cPZkDvnBSpY7QNP0mip\noHk/kHNOZsNX1gQASa3TRgCAFVNtxm1Td1+OPggMa0PunBdotFRsGMjHIzfJ6gMAgT4A5IYXBCoW\nrG0muhh9EBjW3XHnKn7D0ZpSrZyHQB8ACPQBIDe8wLXN5ku1Gv5h3R03zOg3fvsaqQb6TN4BAAJ9\nAMgJz3fViTqtxDX8w1yjP1puk9Fn8g4AEOgDQF54flAty2ml2ow7tDX6frUWv148cpPSHQAg0AeA\n3KgErtpo20o5+jBQGdKM/nyL0p1RSncAoIpAHwBywvdd211xJZpxW9Xox8fnyegDQKqdcQEAK6AS\nBG1n6EuJnXFbNOPuvvUWbd17sza7SR22TTqw4wbtuvr6rq21l+YqQfPSHcZrAkAVGX0AyAnPTzd1\np9qM2ySjv/vWW3Txnhu1RZMqmLRFk7p4z43afestXV1vr8x5LcZrlhmvCQAxAn0AyAkvCKqNtq2U\n2ozX3Lr3Zo3b/IJj4zavrXtvXvoic6DleM1iPHWHGn0AINAHgJxIm9EvtxmvudlNNjl+pPPF5Ug4\nXrNx6c4YGX0AqCLQB4Cc8IJ0c/TbNeMetk1Njm/sfHE50nrqDjX6ALpr96236NBN5yl42zoduum8\nviqDJNAHgJyo+N1pxj2w4wbNupEFx2bdiA7suGHpi8yBcI4+4zUBLL9+73ki0AeAnOhWM+6uq6/X\nF8//XT0SbFTgTIe0Sft2vnOopu4wXhNAN/R7zxPjNQEgJ/wg3Rz9ds24klS+5NW6Yt/5uvCMtfrC\nm5+rLV1bZe/NeQFTdwCsiM1uUmqQf+mXnicy+gCQE+nn6LduxpWk6dlKeHmq0p3F5UQQOM37aabu\nEOgDWLp+73ki0AeAnPD87jTjStKx2fCr5jjgHxTz0bcYcea+XqFgGikWqNEH0BX93vNEoA8AOVHx\ng2oQ30q1GTdonrWeigL8E/O+vBYlPv0mztQ3q9GXpJFSgdIdAF2x6+rrtW/nO3XQhT1Pj2pjX/U8\nUaMPADkRjtdM0YxbaF+6M5XI5B8/5WnD6pGm5/aTOT/M1Dcr3YlvI6MPoFt2XX293jSzS5/9zmN6\n9yueoZ+7bFuvl5QaGX0AyInUzbhtxmtK0tSsV70+SHX6tYx+m0CfGn0AXRSXQU71WTkkgT4A5ET6\nOfqtx2tK0rGTtXFw04mgv9/FJTnNdsaNb6N0B0A3EegDAJYk7Rz9NM2407MVjUUNqwOV0Y9KckZa\nNC2PlgrM0QfQVVME+gCApfCCoFqW00o5xRz9qdmKtm5YJWmwJu/UMvrU6ANYOdOnwm9G++31lEAf\nAHLCC5zKKTL6hYKpYO2bcbeeFgX6g5TRT1WjT+kOgO5xzpHRBwAsjee7VBl9KWzIbTZeMwhclNEf\nlzRYNfrVOfqM1wSwQk7O+9VSSTL6AICOVPwgVY2+JJULJr9JRn9m3lPgpKeuH5eZdHygMvqM1wSw\nspJZfDL6AICOeIFLNXVHChtym03dmToZvhFtWDWitaOlam3pIIgz9WOtavTLjNcE0D1xcH/66hEC\nfQBAds651HP0pXB33GbNuPEb0bpVZU2Ml/vuq+ZWqs24LUp3qNEH0E3xa+jW01Zp+pQn55r3R+UN\ngT4A5ECcnU+zM64klYrWtBk3flNaN17WxFh5sJpx4/GabUp3GK8JoFumEoG+HzjNzPXPt6QE+gCQ\nA3HQnroZt9C8GfdYMtAfLw1UM27qnXGp0QfQJXGgv+208QW/94OhDvTN7DYzcyl/Hur1egEMrjho\nT92MW7SmG2ZNDXRGP0XpDjvjAuiiuM9pWzyyuI+SJ0Md6ANAXlQz+ikD/WKheelOHOivH8Aa/bgk\np1XpzkiR8ZoAumdqtiIz6cz1q6q/94tSrxeQIy9vc/vJFVkFgKHkxRn9lKU77Zpxy0XTeLkYZfT7\nJ/vUzpznq1w0FVt8IBotFeQHTp6fbqdhAGhleraiNaMlrV9VlkSg35ecc5/u9RoADK84O5+pGbdJ\n6c6xkxWtGy/LzDQxXtLMnDcwQe+cF7Qs25HC8ZrxuYPwbwbQW9Oz4WvquvFy9fd+wSsgAORAHOgX\nU47XLBWaZ/SnZyuaiN6QJsbCy36aEtHKnOe3LNuRavX7lO8A6Iap2Yomxspa14cZfQJ9AMiBuBk3\nbUa/XTPu+ijQXzsWfnHbT81jrcxVgpYTd6TaRB5GbALohulTYUZ/zUhJZgT6AICM4qA97YZZ7Zpx\n46+Y48z+oEzeCUt32gT61dIdRmwCWLr4NbVQME2MlQn0+5GZfdbMHjOzeTM7ambfMrP3mdklvV4b\ngMEXl+GUUmf0W83Rn68F+mP9V1PaynyaGn1KdwB00fSsp4nx8NvRdeME+v3qJyVtkVSWdJqkZ0n6\nVUl3mdmHzGy8l4sDMNiyjtcstcron0xm9KPSnYHJ6PvVjH0zccY/3lwLAJYi+S3puvH+2puEqTvS\nUUlfkLRH0qOSTNJ2ST8t6dnROa+TtM3MXuycG4xCVwC5knW8ZqnJeM0gcDo+52ndqhFJiYz+gIzY\nTFO6EzfrUroDYKnmvUCzFb/6WtpvGf1hD/TfKulO51yj/2LvNrOXS/q4pFWSXiDptyX9YaMHMrPr\nJF0nSdu2bVue1QIYWNXxmkvcGff4KU/OaXGNfh+9MbUy5wUaL1O6A2BlxNn7eOLOuvGyHp2a7eWS\nMhnq0h3n3DeaBPnx7f8o6Y2JQzeY2WiTcz/gnLvUOXfppk2bur1UAAMunomfNqNfLBQaztGPM01x\noL92NJwSMTgZ/TTjNcnoA+iO+tfUftttfKgD/TScc38j6Z7o13WSntPD5QAYUHEZTqsdX5PKBWtY\nunNsdl5S7U2pUDCtGS311RtTK6nGa5ap0QfQHXGgX1+641zjHqm8IdBP57bE9Yt6tQgAg6ujnXEb\nNOPWZ5+k8A2qn5rHWpn308zRL1bPBYCliJMkE4kBBxXfabbSH98YEuinczRxfX3PVgFgYHkZ5+iX\nioVqA29SHOivX5UI9MfLA7ZhVrsafTL6ALqjPnkSX/ZLQy6BfjqnJ64f69kqAAwsL+vOuAVLVaMv\nSRNjpYHJ6Gcar0mNPoAlivubknP0pf7ZbZxAP53nJa7f27NVABhY1Tn6WZpxG5TuHDvZINDvs+ax\nVrKN1ySjD2BpphvU6Etk9AeGmf2canX5xyV9vYfLATCgqjvjZhiv2agZd3q2opFSQWOJEZQTY2Ud\nH5ipO+yMC2DlTM1WNJp4TSXQ7xNm9utmdnmbc/6TpA8mDr3HOXdqeVcGYBjVxmtmaMZtUrqzPpHN\nl8KvnAcho+/5gfzAtR2vWS6azKS5PmmWA5Bf04ldcaX+C/SHecOsqyT9uZndI+n/SPquwqbbeGfc\nl6q2M64kfVnSu1d4jQCGROZm3EJBfuDknJNZ7cPBVN2bkhRl9Oc8+YFLPb4zj+IMfbvSHTPTaKlA\nRh/AktW/phLo958Lo59mnKS/kvRm59z8yiwJwLDx/IzNuNF5XuAW3OfYyQaBfvT7zCmvurtjP5pP\nGeiH5xQJ9AEs2fSpSvU1VJLWjhHo94v/KumfJP2opGdJ2ixpo8K/yTGFTbdfl/Rh5xwNuACWVdxY\nmzbjXowy/57vlCjH19RsRU9ZN7bg3Imx8KV++lSlrwP9aka/3LpGXxIZfQBdMTVb0ea1tdfUYsG0\nto82IRzaQN85d7+k+7WwBh8AeqJSHa+ZrnQnzuJXgkDjqgW+U7MVXfSUtQvOjTNQ/T5iMx6XmSqj\nXy4wXhPAkk3NVnT+5oWvqRPR7rj9YGibcQEgT/x4vGbKjH58Xv2IzfrGMak2/7lf5j43U6vRb5/R\nHymS0QewdNOzXvVb0di6PhpZTKAPADlQCbKV7sTz9r3EiE3PD3R8zmvYjCsNQEa/krFGn51xASxB\nELiw5LHuNXUdGX0AQBaeH6hUsAUTdFpJNuPG4h0cG70pSeqbDFQzcSlOu/GaEqU7AJbu+Jwn57Sg\nGVci0AcAZOQFLvUMfWlhM24sfuNZv6pZRr+/S3eyTd2hdAfA0lR3xSXQBwAshec7lVPO0JcWNuPG\n4jee+oz+mnjqTp+8MTWTbepOsfrBAAA60ew1dd0qAn0AQAZeEGTK6JdaZPTr35Sq4+D6vUY/y9Qd\nMvoAlih+zYy/FY2tGy9rzgt0qg923ybQB4AcqPiuWo6TRvyhoJJoxj12MtzTrz7Ql8Kvngdn6k6a\nGv0iNfoAlmS6SfJkoo/6ngj0ASAHPD9IvSuuVCvd8ZPNuNU3pZFF568dG4CMfiXjhllM3QGwBNVv\nSRf1PZUW3J5nBPoAkAN+p824KWr0pTijn/83pVaqU3dSbCo2QukOgCWKvwVtNEdf6o+RxQT6AJAD\nlSBjM24hLt1ZWKM/Xi42HD85MVbu+6k7tWbctDX6lO4A6NzUbEUFk9aMNg70yegDAFLx/IzNuMXF\nzbjHTi7e2CU2MV4agIx+xg2zyOgDWILpUxVNjJcX7W9CoA8AyKTjZty60p2mgf5YuS++Zm4lDtzT\nlO6Mlgqa9wI559qeCwCNNHtNrQb6J/P/mkqgDwA54AUZm3GjDwV+XelOfdNYbGK8rJk5T0HQv4Hv\nnOdrtFRItXtwXN4z75PVB9CZZoH+RDWjn/9ySAJ9AMgBP3AqFbI044bn1jfjNs/ol+ScNDOf/zem\nZuYqQaqyHSks3ZFE+Q6Ajk3PVhbN0JekcrGg1SNFSncAAOlU/KBad59GdWfc+ox+0xr9/pn73Myc\nF6QarSnV6vgZsQmgU61eU9eN98fuuAT6AJADnp8to19txs1Qoy+przfNmvP8VPX5kqqTh5i8A6BT\nU7OeJsZLDW+bGO+PvicCfQDIgUrgMmX0S3XjNSt+oJPzvta3KN2R+mPuczPzXpBqtKaUyOhTugOg\nQ/HUnUYmyOgDANLyg6A6Gz+NcvShIN4Zt9kOjrGBKd0ppS3diWr0Kd0B0IFTFV/zXtCydKcfXk8b\nfx+BobH71lu0de/N2uwmddg26cCOG7Tr6ut7vSxg6Hh+1p1xo2bcaKpMq11xpUTpTh9vmhUG+ikz\n+mVKdwB0Lg7iGzXjSuFr7T4CfeTZ7ltv0cV7btS4zUsmbdGk1u25Ubslgn1ghVX8QKUsO+PWNeMe\ni+Y5N/+aOSrd6YM3pmbmKn6GqTvReE1KdwB0oF3yhGZc5N7WvTeHQX7CuM1r696be7QiYHh5QbaM\nfn0zbhzAN6vRj7dw7+ca/WxTdxivCaBzaQL9k/O+Kjnfq4NAf4htdpNNjh9Z4ZUACKfudN6M2+5N\nqVQsaM1oqc+n7mSZo08zLoDOxUmRZt+SVnfHzXlWn0B/iD1uGxseP9zkOLCSdt96iw7ddJ6Ct63T\noZvO0+5bb+n1kpZV5p1xmzXjNnlTksLJO/2d0ferYzPbGWW8JoAlSJPRT56XVwT6Q+r7j03rT71r\ndNKNLDg+60Z0YMcNPVoVEIr7R7ZoUoWof+TiPTcOdLDv+a7aYJtGsWAyqzXjtqvRj2/r5xr9+UwZ\nfabuAOhc/O1nPJq4Xr/0PRHoD6EDT5zUtR+6Q7eveoHueMZNOqRNCpzpoNuofTvfSSMuem4Y+0cq\nflDN0qdVKpgqiYz+mtFSy8eYGOuPDV6ayTRes0zpDoDOxZn6fi/dYerOkHnixLyu/dAdOlXx9Xe/\n/GxduOUF0ivfpHd97vv6yL89pO/91It6vUQg7B9pkNwe5P4RP8i2M64klQqFBeM1W5XtSGEG6tFj\npzpeY691MnWH0h0AnZiarWj1SLFp8qRfAn0y+kMgWes8+ydP1zOf/II+eO0uXbhlbfWcC89Yq3kv\n0ENHT/ZwpUDosG1qcnxw+0ey7owrSaWiLWjGbVW2I4UZ/eNz+X5TamUu0864Yeaf8ZoAOjHd5jW1\nXzYhJNAfcPW1zmfaEb2r9EHZ3X+34Lw46L/n0PFeLBNY4MCOGzQ7ZP0jnp+tGVcKG3JrzbjzWjfe\n+kvasEa/P6fuOOcyle6MMHUHwBK0+5aUjD5yoVGt86oGtc7nbV6jYsH0g0PTK7k8oKFdV1+vz53z\nVj0SbFTgTI8EG/XBDb8xsP0jQeAUOGVqxpXCGv14jn6q0p2xko6fqiiIPhz0k/moRClt6U6xYCoX\njdIdAB2ZPtU6oz9aKmqsXCDQR2+lnZU/Vi7qnI2r9QMy+siJw9tfpivm36uZt07qL/+fT+t9R3bo\n6Mxcr5e1LCpRsN5RM26idGf9+EjL8yfGywqcdGK+/7L6cQlO2kA/PLfI1B0AHZma9TQx1jp50g+7\n4xLoD7gstc4XbllL6Q5y4/7JGW1eO6qJsbJ+6TnnaN4L9PF/f7jXy1oWcflN5mbcYl0z7qr2NfqS\nNH2q/wL9uQ4C/ZFSgdIdAB2ZTvEtKYE+ei5LrfNFZ6zVw0+c1Im5/gsCMHjun5zRuZvWSApLy666\naLM+9u8P6VRl8Eox4qx8R824gdOpiq9TlSDV1B0p/81jjdQC/XQ1+uG5BUp3AHQkbMZt3fe0rg/6\nngj0B9yuq6/Xly64sVrrfEibms7KrzbkPk5WH73lnNP9h2d07ubV1WNvuOIcHZmZ12e+dbCHK1se\ncVY+a0a/XCjI9101cE8zdUfq00A/+oCXduqOFAf6ZPQBZOMHTsfnvBR9T/nP6DNHfwhMnfdyXXH3\nebr9LVfpzPXj2tLkvIu2TEgKJ+/s2LZhSc+5+9ZbtHXvzdrsJnXYNunAjhsGtpES3XdkZl7Tpzw9\nbeOa6rEfO/d0Pf0pE/rg1x7Uqy7dKrNsQXGeeXHpTsapO6Vi2Iwbv9Gsb5vRH67SndFSkfGaADKL\nkyFpSnfy3ttIRn8I7D98XKtHinrqurGW5521YVyrR4pLrtOvH+m5RZO6eM+N2n3rLUt6XAyP+ydn\nJEnnbq4F+mamN1xxjvYfntFX9w/WxllxoF8udNaMO5XyTWnt2JCV7pTJ6APILt5BvF0zbjiyON+v\npwT6Q+C+wzM6b/OathnQQsF0wZa1Sx6x2Wik53iDkZ5AM9VAf9PqBcdf+qynavPaUX3waw/0YlnL\nplq6kzmjX1iQ0U/zNbNUexPrJ3HpzkimjD41+gCyS/uaum68rONzXnWgQh4R6A+B/Y/P6LzNa9uf\nKOmiaPKOc53/T5t2pCfQzAOTJzRWLuip68YXHB8pFXTts7fra/uPDNSEqLgZt5M5+hXf6djJrBn9\n/ivdyTpHPzyX8ZoAsotfI9v1Pa3rg91xCfQH3PSpig5Nn9L5Z6xpf7KkC89YqydPVnT4eOfzyrOM\n9ER2u2+9RYduOk/B29bp0E3nDWRJ1P2TM3raxjUqNAh8f/6ybRorF/T/f31wsvpeh3P0451xqzX6\nbcZrlooFrR4p9mlGP3vpDuM1AXQiS0Y/eX4eEegPuPsOhyUQ529OGehHDblLaS7JMtIT2QxL/8P9\nkzML6vOTNqwe0a+dvle//p1XDMyHHc/vdI6+yfNrpTtr29STSv1RU9pItUY/89QdSncAZEOgj75x\n3+NhoH9eykD/onjE5hLq9Hddfb3+5bzfqY70fCTYqM9s+22m7nTBMPQ/nKr4euTJ2UX1+bHdt96i\nX3ryz3RW4cjAfNipNuN2uDPu1GxFa8dKqUp/JsbK/ZnRjwL2bKU7ZPQBZFdtxm03R39V/vueCPQH\n3P7DxzVaKuisDatSnb9h9YjOmBhd8rioR856qa6Yf6+Ov2VSr9/wYX3k+GVLqvtHaBj6Hx48ckLO\nqbpZVr1B/LATN+Nmr9GvNeO2yzzFJsZLOt7X4zWzbJhFjT6GxzCUda6UqdmKykXTeLn160084ICM\nPnpm/+Fwd9EsAcSFWyb0g8eWFujvOzils09fpXXjZb3+ynP0g0PH9fX7BicY7ZVh6H+IJ+48rUlG\nfxA/7NR2xu2kdCfM6Lerz4/1bUa/0kFGv1yoNvECg2xYyjpXynSUPGk3rZDSHfTc/sdnUjfixi7a\nslb3Tc5Us4yduPvglC4+c50k6WWXPFUb14zqg197sOPHQ+jAjht0csD7H+4/fEKSFmyWlTSIH3aW\n0ozrRc246TP6+d+yvZE4YM88XrNCjT4G3yB+09lLU7OVtjP0JQJ99NiJOU8Hj82mbsSNXXjGWs17\ngR46eqKj5z12cl6PPDmri58aBvqjpaKu/bGz9ZV7J3Xv44MzErEXdl19vd676td00IX9DwfdRu3b\n+c6B6n944MiMzlw/9NpqggAAIABJREFUrvGRxl+ZDmKzd3Vn3A7Ga8bNuKkD/bFSn2b0OxyvSY0+\nhsAgftPZS1OzlbajNSVprFzQSLFAoI/eiEsg0s7Qj130lPD8Tuv09x0MG3mfEWX0JekXf/RsjZUL\nA7fR0Uo7VfH14eO79MFLb9VvPv3LevnILbr0pdf1elktZa0bbTVxRwo/7Ozb+U4d0qaw2dtt1L6d\nf9DXH3biqTuZm3GLpkoQztHPltGv9F3PzJwXqFgwlTL8jUZK4TceS/l2EugHg/hNZy9Nn/JSBfpm\nlvtJZgT6A2x/NHEna+nOeZvDmv5O6/T3PTolSfqRp05Uj21YPaJX7jxLn77rUU0uYUb/sLvjwSc0\n5wV67gWbtPPsDTp8fE4Hj832ellNZa0bDQKn+w+faDpxJ7br6uu15ab79Imf/LaumHuvNj37/12O\n5a+YJTXj+oGmU2afpLBGP3DSifn+KmmZ8/xM2Xyplv2nTh+DbhC/6eyl6Qzfkq4bL5HRR2/sPzyj\nctF09mnpJu7ERktFnbNxdccZ/bsPTumsDePasHrhi84vPeccVYJAH/vGQx09LqSv3DupkVJBP3rO\n6dpx9gZJ0p4fPtnjVTWXtW700PQpzVb8phN36l169mmSpDtz/DdIo1Idr5m9GXdmztO8H2j9+Ej7\nO6g2Li7PGahG5ryg40CfyTsYdLuuvl63/8jvLxhrffeOd/T1N529FJZDth6tGVuX874nAv0Bdt/h\n43raxjWZvuqOXbhlre55vLNZ+t89OFWtz0962qY1esFFZ+hj//5DzfZZNjEvvnLvpC4/5zSNjxR1\n4RlrtWqkqL05DnKz1o3G5WZpA/3zN6/RxFhJe374RGcLzAk/asYtFbI348YTe9LX6Od/7nMjc5Ug\n02hNSRqNRuNRp49hMLn9Zbpi/r1635W7dcX8ezW+8+d7vaS+5JwLvyVN0Ywrha+9ZPTRE/sPz+i8\njGU7sadvWasDT8xqZi7bp9TpUxU9dPSknnHW4kBfkt545Tl68mRFn9r7SEfrGmYHj83qvsMzet4F\nYS1mqVjQJVvXa8/D+Q30s9aN3h/t5Hzu5talO7FCwbTj7A2686H8/g3S6Hi8ZqLUJ0uNvqRcZ6Aa\nmfeDTBN3pETpDoE+hsA3HziqjWtG9erLtoa/P3i0xyvqTyfnfXmBy/SaSqCPFXeq4uvAEyczT9yJ\nXbglrK+/J2P5znejRtxkfX7SZeecptetvUM//s9XsalHRl+9N8yOx4G+JO08e4O+/9hxncj4gSyr\nTjdiuf+Zv5lpHOj9kye0drSkTWtGU69t1/bTtP/wjI6dnG9/ck7FzbhZM/odBfpxRj/Hb0yNdFaj\nH2f0+QYRg805p28++IQuP+c0nTExpu2nr9I3H+zvbzp7Jf62M32NPoE+euCByRMKnHR+xok7sYu2\nhPfLGujvOxg24l58ZuOM/p3/+wO6Yf4vdaYdYVOPjL5676Sesm5M5yU+vO04e4P8wOnbjxxbtudd\nykYsT5z7n/SWyhv0qMK60UfVehzoA0dm9LTNa9puUpK0sw96FdqJ5+hn3zCr9hKeesOsuEa/H0t3\nyh3W6JPRx4B75MlZPTZ1Spc/Lexbuvyc07X7oScUBP01XSsP4qA97YCDdePhJoR5/VsT6A+o/YfD\nAD3rxJ3YmevHtXqkqHsOZavT3/folJ6ybkwbm2Rkt+69WavY1COzih/o6/uP6HkXbFoQBO/YGga5\ny1mnv5SNWL58z2F9dezHdcbv36eXb/6s/vOmj7ZsDkszcafes85ar1LB+rohtzpeM2tGv5g9o7+2\nbzP6ndTox4E+GX0Mtjh7f9k5p1Uvj52s6N7D7F2T1dTJ7Bl956Tjy/zNeqcI9AfUfYdnVCyYtp+e\nLWiKFQqmC7aszTx5J7kjbiNs6tGZbx04puNznp57wcKa93Wryjp/85plzWZ3+t8sCJy+cs+knnfB\nJhULphdctFnffuSYjsw0Hq86M+fp0PSp1I24sfGRon7kzHW686H+/Zq604x+8oNB2uzT2rE4o5/P\nN6VmOindGSkydQfD4Y4Hj2r9qrIuiL7FjwP+OyjfySx+bUzbjFvre8pn8oRAf0Dtf3xGZ5++KnPz\nWtJFWyb0g0PHU2+sMzPn6cEjJxpO3ImxqUdnvnrvpIoF03POW/x32nn2Bu19+NiyfW3Y6X+zuw9O\n6eiJef34hZslSVddtFnOSbfd0/iDwwMZJ+4k7Tp7g779yFTfZm7jZtzMc/SjDwZm0trRdKPgysWC\nVo0UdbzfSnc6Ga/J1B0MiTsefEK7tp+mQvQasvW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LZA3GdUerRpKexP0NejpU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\nQFTRVxRFURRFUZQE5P8BfmmceqwcH2gAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
}
]
}
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