{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Python: Potential Quantiles and Quantile Treatment Effects\n", "In this example, we illustrate how the [DoubleML](https://docs.doubleml.org/stable/index.html) package can be used to estimate (local) potential quantiles and (local) quantile treatment effects. The estimation is based on [Kallus et al. (2019)](https://arxiv.org/abs/1912.12945)." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Potential Quantiles (PQs)\n", "\n", "At first, we will start with the estimation of the quantiles of the potential outcomes." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Data\n", "We define a data generating process to create synthetic data to compare the estimates to the true effect." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import doubleml as dml\n", "import multiprocessing\n", "\n", "from lightgbm import LGBMClassifier" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "The data is generated as a location-scale model with\n", "\n", "$$Y_i = \\text{loc}(D_i,X_i) + \\text{scale}(D_i,X_i)\\cdot\\varepsilon_i,$$\n", "\n", "where $X_i\\sim\\mathcal{U}[-1,1]^{p}$ and $\\varepsilon_i \\sim \\mathcal{N}(0,1)$.\n", "Further, the location and scale are determined according to the following functions\n", "\n", "$$\\begin{aligned}\n", "\\text{loc}(d,x) &:= 0.5d + 2dx_5 + 2\\cdot 1\\{x_2 > 0.1\\} - 1.7\\cdot 1\\{x_1x_3 > 0\\} - 3x_4 \\\\\n", "\\text{scale}(d,x) &:= \\sqrt{0.5d + 0.3dx_1 + 2},\n", "\\end{aligned}$$\n", "\n", "and the treatment takes the following form\n", "\n", "$$D_i = 1_{\\{(X_2 - X_4 + 1.5\\cdot 1\\{x_1 > 0\\} + \\epsilon_i > 0)\\}}$$\n", "\n", "with $\\epsilon_i \\sim \\mathcal{N}(0,1)$.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f_loc(D, X):\n", " loc = 0.5*D + 2*D*X[:,4] + 2.0*(X[:,1] > 0.1) - 1.7*(X[:,0] * X[:,2] > 0) - 3*X[:,3]\n", " return loc\n", "\n", "def f_scale(D, X):\n", " scale = np.sqrt(0.5*D + 0.3*D*X[:,1] + 2)\n", " return scale\n", "\n", "def dgp(n=200, p=5):\n", " X = np.random.uniform(-1,1,size=[n,p])\n", " D = ((X[:,1 ] - X[:,3] + 1.5*(X[:,0] > 0) + np.random.normal(size=n)) > 0)*1.0\n", " epsilon = np.random.normal(size=n)\n", "\n", " Y = f_loc(D, X) + f_scale(D, X)*epsilon\n", "\n", " return Y, X, D, epsilon" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "We can calculate the true potential quantile analytically or through simulations. Here, we will just approximate the true potential quantile for a range of quantiles." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Potential Quantile Y(0): [-3.33065771 -2.7142787 -2.2114026 -1.77261209 -1.37433503 -1.00032016\n", " -0.64362337 -0.29675887 0.04608822 0.38898864 0.73569431 1.09245337\n", " 1.46598119 1.86610467 2.30628774 2.81128199 3.43361991]\n", "Potential Quantile Y(1): [-3.23801203 -2.54010127 -1.97499195 -1.48353114 -1.03783666 -0.62195343\n", " -0.22563538 0.15946647 0.53849791 0.91771387 1.30229388 1.70007159\n", " 2.11789998 2.56670073 3.06397789 3.63817859 4.35053317]\n" ] } ], "source": [ "tau_vec = np.arange(0.1,0.95,0.05)\n", "n_true = int(10e+6)\n", "\n", "_, X_true, _, epsilon_true = dgp(n=n_true)\n", "D1 = np.ones(n_true)\n", "D0 = np.zeros(n_true)\n", "\n", "Y1 = f_loc(D1, X_true) + f_scale(D1, X_true)*epsilon_true\n", "Y0 = f_loc(D0, X_true) + f_scale(D0, X_true)*epsilon_true\n", "\n", "Y1_quant = np.quantile(Y1, q=tau_vec)\n", "Y0_quant = np.quantile(Y0, q=tau_vec)\n", "\n", "print(f'Potential Quantile Y(0): {Y0_quant}')\n", "print(f'Potential Quantile Y(1): {Y1_quant}')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Let us generate $n=5000$ observations and convert them to a [DoubleMLData](https://docs.doubleml.org/stable/guide/data_backend.html#doublemldata) object." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n = 5000\n", "np.random.seed(42)\n", "Y, X, D, _ = dgp(n=n)\n", "obj_dml_data = dml.DoubleMLData.from_arrays(X, Y, D)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Potential Quantile Estimation\n", "Next, we can initialize our two machine learning algorithms to train the different nuisance elements. Then we can initialize the `DoubleMLPQ` objects and call `fit()` to estimate the relevant parameters. To obtain confidence intervals, we can use the `confint()` method." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Quantile: 0.1\n", "Quantile: 0.15000000000000002\n", "Quantile: 0.20000000000000004\n", "Quantile: 0.25000000000000006\n", "Quantile: 0.30000000000000004\n", "Quantile: 0.3500000000000001\n", "Quantile: 0.40000000000000013\n", "Quantile: 0.45000000000000007\n", "Quantile: 0.5000000000000001\n", "Quantile: 0.5500000000000002\n", "Quantile: 0.6000000000000002\n", "Quantile: 0.6500000000000001\n", "Quantile: 0.7000000000000002\n", "Quantile: 0.7500000000000002\n", "Quantile: 0.8000000000000002\n", "Quantile: 0.8500000000000002\n", "Quantile: 0.9000000000000002\n" ] } ], "source": [ "ml_m = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10, verbose=-1, n_jobs=1)\n", "ml_g = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10, verbose=-1, n_jobs=1)\n", "\n", "PQ_0 = np.full((len(tau_vec)), np.nan)\n", "PQ_1 = np.full((len(tau_vec)), np.nan)\n", "\n", "ci_PQ_0 = np.full((len(tau_vec),2), np.nan)\n", "ci_PQ_1 = np.full((len(tau_vec),2), np.nan)\n", "\n", "for idx_tau, tau in enumerate(tau_vec):\n", " print(f'Quantile: {tau}')\n", " dml_PQ_0 = dml.DoubleMLPQ(obj_dml_data,\n", " ml_g, ml_m,\n", " quantile=tau,\n", " treatment=0,\n", " n_folds=5)\n", " dml_PQ_1 = dml.DoubleMLPQ(obj_dml_data,\n", " ml_g, ml_m,\n", " quantile=tau,\n", " treatment=1,\n", " n_folds=5)\n", "\n", " dml_PQ_0.fit()\n", " dml_PQ_1.fit()\n", "\n", " ci_PQ_0[idx_tau, :] = dml_PQ_0.confint(level=0.95).to_numpy()\n", " ci_PQ_1[idx_tau, :] = dml_PQ_1.confint(level=0.95).to_numpy()\n", "\n", " PQ_0[idx_tau] = dml_PQ_0.coef.squeeze()\n", " PQ_1[idx_tau] = dml_PQ_1.coef.squeeze()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Finally, let us take a look at the estimated quantiles." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile Y(0) Y(1) DML Y(0) DML Y(1) DML Y(0) lower \\\n", "0 0.10 -3.330658 -3.238012 -3.408565 -3.128312 -3.813293 \n", "1 0.15 -2.714279 -2.540101 -2.855780 -2.495752 -3.245512 \n", "2 0.20 -2.211403 -1.974992 -2.345903 -1.978977 -2.638264 \n", "3 0.25 -1.772612 -1.483531 -1.924002 -1.533900 -2.189737 \n", "4 0.30 -1.374335 -1.037837 -1.482483 -1.148161 -1.683942 \n", "5 0.35 -1.000320 -0.621953 -1.246879 -0.700102 -1.509196 \n", "6 0.40 -0.643623 -0.225635 -0.932973 -0.291406 -1.244455 \n", "7 0.45 -0.296759 0.159466 -0.665264 0.145245 -0.949456 \n", "8 0.50 0.046088 0.538498 -0.077319 0.496551 -0.411582 \n", "9 0.55 0.388989 0.917714 0.378834 0.760104 0.070020 \n", "10 0.60 0.735694 1.302294 0.479928 1.216344 0.168614 \n", "11 0.65 1.092453 1.700072 1.059384 1.655284 0.677614 \n", "12 0.70 1.465981 2.117900 1.544097 2.036147 1.215342 \n", "13 0.75 1.866105 2.566701 1.700015 2.493219 1.400823 \n", "14 0.80 2.306288 3.063978 2.187690 2.988463 1.872768 \n", "15 0.85 2.811282 3.638179 2.631333 3.542647 2.226524 \n", "16 0.90 3.433620 4.350533 3.113207 4.243246 2.753523 \n", "\n", " DML Y(0) upper DML Y(1) lower DML Y(1) upper \n", "0 -3.003836 -3.448745 -2.807879 \n", "1 -2.466047 -2.687345 -2.304159 \n", "2 -2.053541 -2.177496 -1.780458 \n", "3 -1.658267 -1.684502 -1.383297 \n", "4 -1.281024 -1.319759 -0.976562 \n", "5 -0.984562 -0.844707 -0.555498 \n", "6 -0.621490 -0.428255 -0.154557 \n", "7 -0.381072 0.015698 0.274793 \n", "8 0.256944 0.367625 0.625477 \n", "9 0.687647 0.627560 0.892648 \n", "10 0.791241 1.088048 1.344640 \n", "11 1.441153 1.524657 1.785911 \n", "12 1.872852 1.907115 2.165178 \n", "13 1.999207 2.360004 2.626433 \n", "14 2.502612 2.857161 3.119766 \n", "15 3.036143 3.408539 3.676756 \n", "16 3.472891 4.098712 4.387780 \n" ] } ], "source": [ "data = {\"Quantile\": tau_vec, \"Y(0)\": Y0_quant, \"Y(1)\": Y1_quant,\n", " \"DML Y(0)\": PQ_0, \"DML Y(1)\": PQ_1,\n", " \"DML Y(0) lower\": ci_PQ_0[:, 0], \"DML Y(0) upper\": ci_PQ_0[:, 1],\n", " \"DML Y(1) lower\": ci_PQ_1[:, 0], \"DML Y(1) upper\": ci_PQ_1[:, 1]}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "plt.rcParams['figure.figsize'] = 10., 7.5\n", "fig, (ax1, ax2) = plt.subplots(1 ,2)\n", "ax1.grid(); ax2.grid()\n", "\n", "ax1.plot(df['Quantile'],df['DML Y(0)'], color='violet', label='Estimated Quantile Y(0)')\n", "ax1.plot(df['Quantile'],df['Y(0)'], color='green', label='True Quantile Y(0)')\n", "ax1.fill_between(df['Quantile'], df['DML Y(0) lower'], df['DML Y(0) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax1.legend()\n", "ax1.set_ylim(-3, 4)\n", "\n", "ax2.plot(df['Quantile'],df['DML Y(1)'], color='violet', label='Estimated Quantile Y(1)')\n", "ax2.plot(df['Quantile'],df['Y(1)'], color='green', label='True Quantile Y(1)')\n", "ax2.fill_between(df['Quantile'], df['DML Y(1) lower'], df['DML Y(1) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax2.legend()\n", "ax2.set_ylim(-3, 4)\n", "\n", "fig.suptitle('Potential Quantiles', fontsize=16)\n", "fig.supxlabel('Quantile')\n", "_ = fig.supylabel('Potential Quantile and 95%-CI')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "### Quantile Treatment Effects (QTEs)\n", "In most cases, we want to evaluate the quantile treatment effect as the difference between potential quantiles.\n", "Here, different quantiles can be estimated in parallel with `n_jobs_models`.\n", "\n", "To estimate the quantile treatment effect, we can use" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Cores used: 5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "================== DoubleMLQTE Object ==================\n", "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", "0.10 0.267950 0.231986 1.155025 2.480800e-01 -0.186735 0.722634\n", "0.15 0.293218 0.190982 1.535318 1.247057e-01 -0.081100 0.667536\n", "0.20 0.388071 0.167547 2.316193 2.054771e-02 0.059685 0.716456\n", "0.25 0.406285 0.161243 2.519710 1.174516e-02 0.090255 0.722316\n", "0.30 0.466756 0.151819 3.074426 2.109079e-03 0.169196 0.764315\n", "0.35 0.606129 0.149285 4.060201 4.903056e-05 0.313535 0.898722\n", "0.40 0.708459 0.158007 4.483711 7.335609e-06 0.398770 1.018148\n", "0.45 0.725166 0.156021 4.647873 3.353748e-06 0.419371 1.030962\n", "0.50 0.509461 0.164608 3.094999 1.968134e-03 0.186836 0.832086\n", "0.55 0.488811 0.161236 3.031639 2.432300e-03 0.172793 0.804828\n", "0.60 0.542989 0.162153 3.348617 8.121584e-04 0.225175 0.860804\n", "0.65 0.699035 0.190809 3.663529 2.487641e-04 0.325056 1.073013\n", "0.70 0.516528 0.195377 2.643752 8.199281e-03 0.133596 0.899460\n", "0.75 0.685107 0.245062 2.795647 5.179588e-03 0.204794 1.165419\n", "0.80 0.893851 0.222843 4.011131 6.042844e-05 0.457088 1.330615\n", "0.85 0.896023 0.209894 4.268942 1.964025e-05 0.484640 1.307407\n", "0.90 1.229443 0.177995 6.907176 4.944045e-12 0.880579 1.578307\n" ] } ], "source": [ "n_cores = multiprocessing.cpu_count()\n", "cores_used = np.min([5, n_cores - 1])\n", "print(f\"Number of Cores used: {cores_used}\")\n", "\n", "dml_QTE = dml.DoubleMLQTE(obj_dml_data,\n", " ml_g,\n", " ml_m,\n", " quantiles=tau_vec,\n", " n_folds=5)\n", "dml_QTE.fit(n_jobs_models=cores_used)\n", "print(dml_QTE)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "As for other ``dml`` objects, we can use ``bootstrap()`` and ``confint()`` methods to generate jointly valid confidence intervals." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2.5 % 97.5 %\n", "0.10 -0.399692 0.935591\n", "0.15 -0.256416 0.842853\n", "0.20 -0.094118 0.870260\n", "0.25 -0.057762 0.870332\n", "0.30 0.029831 0.903681\n", "0.35 0.176495 1.035762\n", "0.40 0.253724 1.163194\n", "0.45 0.276148 1.174185\n", "0.50 0.035730 0.983192\n", "0.55 0.024782 0.952839\n", "0.60 0.076322 1.009656\n", "0.65 0.149898 1.248171\n", "0.70 -0.045754 1.078810\n", "0.75 -0.020166 1.390379\n", "0.80 0.252524 1.535179\n", "0.85 0.291963 1.500084\n", "0.90 0.717185 1.741702\n" ] } ], "source": [ "ci_QTE = dml_QTE.confint(level=0.95, joint=False)\n", "\n", "dml_QTE.bootstrap(n_rep_boot=2000)\n", "ci_joint_QTE = dml_QTE.confint(level=0.95, joint=True)\n", "print(ci_joint_QTE)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "As before, let us take a look at the estimated effects and confidence intervals." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile QTE DML QTE DML QTE pointwise lower \\\n", "0.10 0.10 0.092646 0.267950 -0.186735 \n", "0.15 0.15 0.174177 0.293218 -0.081100 \n", "0.20 0.20 0.236411 0.388071 0.059685 \n", "0.25 0.25 0.289081 0.406285 0.090255 \n", "0.30 0.30 0.336498 0.466756 0.169196 \n", "0.35 0.35 0.378367 0.606129 0.313535 \n", "0.40 0.40 0.417988 0.708459 0.398770 \n", "0.45 0.45 0.456225 0.725166 0.419371 \n", "0.50 0.50 0.492410 0.509461 0.186836 \n", "0.55 0.55 0.528725 0.488811 0.172793 \n", "0.60 0.60 0.566600 0.542989 0.225175 \n", "0.65 0.65 0.607618 0.699035 0.325056 \n", "0.70 0.70 0.651919 0.516528 0.133596 \n", "0.75 0.75 0.700596 0.685107 0.204794 \n", "0.80 0.80 0.757690 0.893851 0.457088 \n", "0.85 0.85 0.826897 0.896023 0.484640 \n", "0.90 0.90 0.916913 1.229443 0.880579 \n", "\n", " DML QTE pointwise upper DML QTE joint lower DML QTE joint upper \n", "0.10 0.722634 -0.399692 0.935591 \n", "0.15 0.667536 -0.256416 0.842853 \n", "0.20 0.716456 -0.094118 0.870260 \n", "0.25 0.722316 -0.057762 0.870332 \n", "0.30 0.764315 0.029831 0.903681 \n", "0.35 0.898722 0.176495 1.035762 \n", "0.40 1.018148 0.253724 1.163194 \n", "0.45 1.030962 0.276148 1.174185 \n", "0.50 0.832086 0.035730 0.983192 \n", "0.55 0.804828 0.024782 0.952839 \n", "0.60 0.860804 0.076322 1.009656 \n", "0.65 1.073013 0.149898 1.248171 \n", "0.70 0.899460 -0.045754 1.078810 \n", "0.75 1.165419 -0.020166 1.390379 \n", "0.80 1.330615 0.252524 1.535179 \n", "0.85 1.307407 0.291963 1.500084 \n", "0.90 1.578307 0.717185 1.741702 \n" ] } ], "source": [ "QTE = Y1_quant - Y0_quant\n", "data = {\"Quantile\": tau_vec, \"QTE\": QTE, \"DML QTE\": dml_QTE.coef,\n", " \"DML QTE pointwise lower\": ci_QTE['2.5 %'], \"DML QTE pointwise upper\": ci_QTE['97.5 %'],\n", " \"DML QTE joint lower\": ci_joint_QTE['2.5 %'], \"DML QTE joint upper\": ci_joint_QTE['97.5 %']}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = 10., 7.5\n", "fig, ax = plt.subplots()\n", "ax.grid()\n", "\n", "ax.plot(df['Quantile'],df['DML QTE'], color='violet', label='Estimated QTE')\n", "ax.plot(df['Quantile'],df['QTE'], color='green', label='True QTE')\n", "ax.fill_between(df['Quantile'], df['DML QTE pointwise lower'], df['DML QTE pointwise upper'], color='violet', alpha=.3, label='Pointwise Confidence Interval')\n", "ax.fill_between(df['Quantile'], df['DML QTE joint lower'], df['DML QTE joint upper'], color='violet', alpha=.2, label='Joint Confidence Interval')\n", "\n", "plt.legend()\n", "plt.title('Quantile Treatment Effects', fontsize=16)\n", "plt.xlabel('Quantile')\n", "_ = plt.ylabel('QTE and 95%-CI')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Local Potential Quantiles (LPQs)\n", "\n", "Next, we will consider local potential quantiles and the corresponding local quantile treatment effects." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Data\n", "We add a counfounder and an instrument to our data generating process." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import doubleml as dml\n", "import multiprocessing\n", "\n", "from lightgbm import LGBMClassifier" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The data is generated as a location-scale model with confounding of the treatment $D_i$ and instrument $Z_i$\n", "\n", "$$\\begin{aligned}\n", "Y_i &= \\text{loc}\\left(D_i,X_i, X^{(conf)}_i\\right) + \\text{scale}\\left(D_i,X_i, X^{(conf)}_i\\right)\\cdot\\varepsilon_i,\\\\\n", "D_i &=1\\{0.5Z_i -0.3X_{i,1}+0.7X^{(conf)}_i + \\eta_i > 0\\},\n", "\\end{aligned}$$\n", "\n", "where $X_i\\sim\\mathcal{U}[-1,1]^{p}$, $X^{(conf)}_i \\sim\\mathcal{U}[-1,1]$, $Z_i\\sim \\mathcal{B}(1,1/2)$, $\\varepsilon_i \\sim \\mathcal{N}(0,1)$ and $\\eta_i \\sim \\mathcal{N}(0,1)$.\n", "Further, the location and scale are determined according to the following functions\n", "\n", "$$\\begin{aligned}\n", "\\text{loc}(d,x) &:= 0.5d + 2dx_5 + 2\\cdot 1\\{x_2 > 0.1\\} - 1.7\\cdot 1\\{x_1x_3 > 0\\} - 3x_4 - 2x^{(conf)}\\\\\n", "\\text{scale}(d,x, x^{(conf)}) &:= \\sqrt{0.5d + 3dx_1 + 0.4x^{(conf)} + 2}\n", "\\end{aligned}$$" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def f_loc(D, X, X_conf):\n", " loc = 0.5*D + 2*D*X[:,4] + 2.0*(X[:,1] > 0.1) - 1.7*(X[:,0] * X[:,2] > 0) - 3*X[:,3] - 2*X_conf[:, 0]\n", " return loc\n", "\n", "def f_scale(D, X, X_conf):\n", " scale = np.sqrt(0.5*D + 3*D*X[:, 0] + 0.4*X_conf[:, 0] + 2)\n", " return scale\n", "\n", "def generate_treatment(Z, X, X_conf):\n", " eta = np.random.normal(size=len(Z))\n", " d = ((0.5*Z -0.3*X[:, 0] + 0.7*X_conf[:, 0] + eta) > 0)*1.0\n", " return d\n", "\n", "def dgp(n=200, p=5):\n", " X = np.random.uniform(0, 1, size=[n,p])\n", " X_conf = np.random.uniform(-1, 1, size=[n,1])\n", " Z = np.random.binomial(1, p=0.5, size=n)\n", " D = generate_treatment(Z, X, X_conf)\n", " epsilon = np.random.normal(size=n)\n", "\n", " Y = f_loc(D, X, X_conf) + f_scale(D, X, X_conf)*epsilon\n", "\n", " return Y, X, D, Z" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Next, we will just simulate the true quantile for a range of quantiles." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "tau_vec = np.arange(0.1,0.95,0.05)\n", "n_true = int(10e+6)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compliance probability: 0.3245837\n" ] } ], "source": [ "p=5\n", "X_true = np.random.uniform(0, 1, size=[n_true,p])\n", "X_conf_true = np.random.uniform(-1, 1, size=[n_true,1])\n", "Z_true = np.random.binomial(1, p=0.5, size=n_true)\n", "eta_true = np.random.normal(size=n_true)\n", "D1_true = generate_treatment(np.ones_like(Z_true), X_true, X_conf_true)\n", "D0_true = generate_treatment(np.zeros_like(Z_true), X_true, X_conf_true)\n", "epsilon_true = np.random.normal(size=n_true)\n", "\n", "compliers = (D1_true == 1) * (D0_true == 0)\n", "print(f'Compliance probability: {str(compliers.mean())}')\n", "n_compliers = compliers.sum()\n", "Y1 = f_loc(np.ones(n_compliers), X_true[compliers, :], X_conf_true[compliers, :]) + f_scale(np.ones(n_compliers), X_true[compliers, :], X_conf_true[compliers, :])*epsilon_true[compliers]\n", "Y0 = f_loc(np.zeros(n_compliers), X_true[compliers, :], X_conf_true[compliers, :]) + f_scale(np.zeros(n_compliers), X_true[compliers, :], X_conf_true[compliers, :])*epsilon_true[compliers]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Local Potential Quantile Y(0): [-4.07538443 -3.53606675 -3.10878571 -2.74402577 -2.41805621 -2.11724226\n", " -1.83287529 -1.56018481 -1.29299726 -1.02900983 -0.76228406 -0.4895498\n", " -0.20400735 0.10089588 0.43453524 0.81827267 1.29107127]\n", "Local Potential Quantile Y(1): [-3.19031969 -2.53273833 -2.01574297 -1.57245066 -1.17700723 -0.81190107\n", " -0.46807543 -0.13585644 0.1881752 0.51214922 0.84030318 1.17655394\n", " 1.53094017 1.91102953 2.33175566 2.81828926 3.4284675 ]\n" ] } ], "source": [ "Y0_quant = np.quantile(Y0, q=tau_vec)\n", "Y1_quant = np.quantile(Y1, q=tau_vec)\n", "\n", "print(f'Local Potential Quantile Y(0): {Y0_quant}')\n", "print(f'Local Potential Quantile Y(1): {Y1_quant}')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Let us generate $n=10000$ observations and convert them to a [DoubleMLData](https://docs.doubleml.org/stable/guide/data_backend.html#doublemldata) object." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "n = 10000\n", "np.random.seed(42)\n", "Y, X, D, Z = dgp(n=n,p=p)\n", "obj_dml_data = dml.DoubleMLData.from_arrays(X, Y, D, Z)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Local Potential Quantile Estimation\n", "Next, we can initialize our two machine learning algorithms to train the different nuisance elements. As above, we can initialize the `DoubleMLLPQ` objects and call `fit()` to estimate the relevant parameters. To obtain confidence intervals, we can use the `confint()` method." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Quantile: 0.1\n", "Quantile: 0.15000000000000002\n", "Quantile: 0.20000000000000004\n", "Quantile: 0.25000000000000006\n", "Quantile: 0.30000000000000004\n", "Quantile: 0.3500000000000001\n", "Quantile: 0.40000000000000013\n", "Quantile: 0.45000000000000007\n", "Quantile: 0.5000000000000001\n", "Quantile: 0.5500000000000002\n", "Quantile: 0.6000000000000002\n", "Quantile: 0.6500000000000001\n", "Quantile: 0.7000000000000002\n", "Quantile: 0.7500000000000002\n", "Quantile: 0.8000000000000002\n", "Quantile: 0.8500000000000002\n", "Quantile: 0.9000000000000002\n" ] } ], "source": [ "ml_m = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10, verbose=-1, n_jobs=1)\n", "ml_g = LGBMClassifier(n_estimators=300, learning_rate=0.05, num_leaves=10, verbose=-1, n_jobs=1)\n", "\n", "LPQ_0 = np.full((len(tau_vec)), np.nan)\n", "LPQ_1 = np.full((len(tau_vec)), np.nan)\n", "\n", "ci_LPQ_0 = np.full((len(tau_vec),2), np.nan)\n", "ci_LPQ_1 = np.full((len(tau_vec),2), np.nan)\n", "\n", "for idx_tau, tau in enumerate(tau_vec):\n", " print(f'Quantile: {tau}')\n", " dml_LPQ_0 = dml.DoubleMLLPQ(obj_dml_data,\n", " ml_g,\n", " ml_m,\n", " score=\"LPQ\",\n", " treatment=0,\n", " quantile=tau,\n", " n_folds=5,\n", " trimming_threshold=0.05)\n", " dml_LPQ_1 = dml.DoubleMLLPQ(obj_dml_data,\n", " ml_g,\n", " ml_m,\n", " score=\"LPQ\",\n", " treatment=1,\n", " quantile=tau,\n", " n_folds=5,\n", " trimming_threshold=0.05)\n", "\n", " dml_LPQ_0.fit()\n", " dml_LPQ_1.fit()\n", "\n", " LPQ_0[idx_tau] = dml_LPQ_0.coef.squeeze()\n", " LPQ_1[idx_tau] = dml_LPQ_1.coef.squeeze()\n", "\n", " ci_LPQ_0[idx_tau, :] = dml_LPQ_0.confint(level=0.95).to_numpy()\n", " ci_LPQ_1[idx_tau, :] = dml_LPQ_1.confint(level=0.95).to_numpy()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let us take a look at the estimated local quantiles." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile Y(0) Y(1) DML Y(0) DML Y(1) DML Y(0) lower \\\n", "0 0.10 -4.075384 -3.190320 -4.231310 -3.306963 -4.976088 \n", "1 0.15 -3.536067 -2.532738 -3.188223 -2.826492 -4.009122 \n", "2 0.20 -3.108786 -2.015743 -2.641528 -1.935730 -3.164801 \n", "3 0.25 -2.744026 -1.572451 -2.387426 -1.590813 -2.838114 \n", "4 0.30 -2.418056 -1.177007 -1.927074 -0.878281 -2.301371 \n", "5 0.35 -2.117242 -0.811901 -1.799403 -0.583534 -2.155120 \n", "6 0.40 -1.832875 -0.468075 -1.564045 -0.134211 -1.923607 \n", "7 0.45 -1.560185 -0.135856 -1.412127 0.023256 -1.765792 \n", "8 0.50 -1.292997 0.188175 -1.144669 0.317487 -1.566024 \n", "9 0.55 -1.029010 0.512149 -0.970065 0.516255 -1.441209 \n", "10 0.60 -0.762284 0.840303 -0.649158 0.793570 -1.180951 \n", "11 0.65 -0.489550 1.176554 -0.352246 1.556792 -0.916984 \n", "12 0.70 -0.204007 1.530940 -0.077883 1.742907 -0.742128 \n", "13 0.75 0.100896 1.911030 0.354371 1.995248 -0.379614 \n", "14 0.80 0.434535 2.331756 0.692725 2.562013 0.009986 \n", "15 0.85 0.818273 2.818289 0.842746 2.720571 0.146037 \n", "16 0.90 1.291071 3.428467 1.196189 3.946433 0.145625 \n", "\n", " DML Y(0) upper DML Y(1) lower DML Y(1) upper \n", "0 -3.486532 -3.867565 -2.746361 \n", "1 -2.367323 -3.883622 -1.769361 \n", "2 -2.118255 -2.724338 -1.147121 \n", "3 -1.936739 -2.377311 -0.804316 \n", "4 -1.552776 -1.563503 -0.193060 \n", "5 -1.443686 -1.182633 0.015565 \n", "6 -1.204482 -0.728710 0.460289 \n", "7 -1.058463 -0.559522 0.606034 \n", "8 -0.723314 -0.260360 0.895333 \n", "9 -0.498921 -0.098319 1.130829 \n", "10 -0.117366 0.070884 1.516256 \n", "11 0.212491 0.793735 2.319850 \n", "12 0.586362 0.973241 2.512572 \n", "13 1.088357 1.168931 2.821566 \n", "14 1.375465 1.685807 3.438219 \n", "15 1.539455 1.827735 3.613408 \n", "16 2.246753 1.889733 6.003134 \n" ] } ], "source": [ "data = {\"Quantile\": tau_vec, \"Y(0)\": Y0_quant, \"Y(1)\": Y1_quant,\n", " \"DML Y(0)\": LPQ_0, \"DML Y(1)\": LPQ_1,\n", " \"DML Y(0) lower\": ci_LPQ_0[:, 0], \"DML Y(0) upper\": ci_LPQ_0[:, 1],\n", " \"DML Y(1) lower\": ci_LPQ_1[:, 0], \"DML Y(1) upper\": ci_LPQ_1[:, 1]}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot as plt\n", "plt.rcParams['figure.figsize'] = 10., 7.5\n", "fig, (ax1, ax2) = plt.subplots(1 ,2)\n", "ax1.grid(); ax2.grid()\n", "\n", "ax1.plot(df['Quantile'],df['DML Y(0)'], color='violet', label='Estimated Local Quantile Y(0)')\n", "ax1.plot(df['Quantile'],df['Y(0)'], color='green', label='True Local Quantile Y(0)')\n", "ax1.fill_between(df['Quantile'], df['DML Y(0) lower'], df['DML Y(0) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax1.legend()\n", "ax1.set_ylim(-3, 4)\n", "\n", "ax2.plot(df['Quantile'],df['DML Y(1)'], color='violet', label='Estimated Local Quantile Y(1)')\n", "ax2.plot(df['Quantile'],df['Y(1)'], color='green', label='True Local Quantile Y(1)')\n", "ax2.fill_between(df['Quantile'], df['DML Y(1) lower'], df['DML Y(1) upper'], color='violet', alpha=.3, label='Confidence Interval')\n", "ax2.legend()\n", "ax2.set_ylim(-3, 4)\n", "\n", "fig.suptitle('Local Potential Quantiles', fontsize=16)\n", "fig.supxlabel('Quantile')\n", "_ = fig.supylabel('Local Potential Quantile and 95%-CI')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Local Quantile Treatment Effects (LQTEs)\n", "As for quantile treatment effects, we often want to evaluate the (local) treatment effect.\n", "To estimate local quantile treatment effects, we can use the `DoubleMLQTE` object and specify `LPQ` as the score. \n", "\n", "As for quantile treatment effects, different quantiles can be estimated in parallel with `n_jobs_models`." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Cores used: 5\n", "================== DoubleMLQTE Object ==================\n", "\n", "------------------ Fit summary ------------------\n", " coef std err t P>|t| 2.5 % 97.5 %\n", "0.10 1.103497 0.463325 2.381689 1.723345e-02 0.195396 2.011598\n", "0.15 1.052502 0.590736 1.781681 7.480133e-02 -0.105318 2.210323\n", "0.20 0.707868 0.411295 1.721071 8.523794e-02 -0.098256 1.513992\n", "0.25 0.931479 0.427725 2.177751 2.942460e-02 0.093153 1.769805\n", "0.30 1.182849 0.326740 3.620156 2.944253e-04 0.542451 1.823247\n", "0.35 1.220772 0.297682 4.100923 4.115060e-05 0.637326 1.804219\n", "0.40 1.369796 0.292105 4.689392 2.740180e-06 0.797280 1.942312\n", "0.45 1.403425 0.287041 4.889293 1.011988e-06 0.840836 1.966015\n", "0.50 1.501403 0.284073 5.285276 1.255151e-07 0.944630 2.058175\n", "0.55 1.642016 0.304130 5.399056 6.699259e-08 1.045932 2.238101\n", "0.60 1.502995 0.350518 4.287926 1.803492e-05 0.815993 2.189998\n", "0.65 1.901148 0.335846 5.660776 1.506900e-08 1.242902 2.559394\n", "0.70 1.827381 0.331521 5.512108 3.545605e-08 1.177611 2.477150\n", "0.75 1.844308 0.339570 5.431306 5.594316e-08 1.178763 2.509853\n", "0.80 1.916914 0.305775 6.269043 3.632747e-10 1.317607 2.516222\n", "0.85 2.087947 0.346206 6.030934 1.630150e-09 1.409395 2.766499\n", "0.90 2.278907 0.541010 4.212317 2.527644e-05 1.218546 3.339268\n" ] } ], "source": [ "n_cores = multiprocessing.cpu_count()\n", "cores_used = np.min([5, n_cores - 1])\n", "print(f\"Number of Cores used: {cores_used}\")\n", "\n", "dml_LQTE = dml.DoubleMLQTE(obj_dml_data, \n", " ml_g=ml_g,\n", " ml_m=ml_m, \n", " quantiles=tau_vec, \n", " score='LPQ', \n", " n_folds=5)\n", "dml_LQTE.fit(n_jobs_models=cores_used)\n", "print(dml_LQTE)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "As for other ``dml`` objects, we can use ``bootstrap()`` and ``confint()`` methods to generate jointly valid confidence intervals." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 2.5 % 97.5 %\n", "0.10 -0.208300 2.415294\n", "0.15 -0.620026 2.725031\n", "0.20 -0.456617 1.872354\n", "0.25 -0.279524 2.142482\n", "0.30 0.257762 2.107935\n", "0.35 0.377955 2.063590\n", "0.40 0.542769 2.196824\n", "0.45 0.590738 2.216113\n", "0.50 0.697118 2.305687\n", "0.55 0.780943 2.503089\n", "0.60 0.510586 2.495405\n", "0.65 0.950280 2.852016\n", "0.70 0.888757 2.766005\n", "0.75 0.882896 2.805720\n", "0.80 1.051186 2.782643\n", "0.85 1.107746 3.068148\n", "0.90 0.747164 3.810650\n" ] } ], "source": [ "ci_LQTE = dml_LQTE.confint(level=0.95, joint=False)\n", "\n", "dml_LQTE.bootstrap(n_rep_boot=2000)\n", "ci_joint_LQTE = dml_LQTE.confint(level=0.95, joint=True)\n", "print(ci_joint_LQTE)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "As before, let us take a look at the estimated effects and confidence intervals." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Quantile LQTE DML LQTE DML LQTE pointwise lower \\\n", "0.10 0.10 0.885065 1.103497 0.195396 \n", "0.15 0.15 1.003328 1.052502 -0.105318 \n", "0.20 0.20 1.093043 0.707868 -0.098256 \n", "0.25 0.25 1.171575 0.931479 0.093153 \n", "0.30 0.30 1.241049 1.182849 0.542451 \n", "0.35 0.35 1.305341 1.220772 0.637326 \n", "0.40 0.40 1.364800 1.369796 0.797280 \n", "0.45 0.45 1.424328 1.403425 0.840836 \n", "0.50 0.50 1.481172 1.501403 0.944630 \n", "0.55 0.55 1.541159 1.642016 1.045932 \n", "0.60 0.60 1.602587 1.502995 0.815993 \n", "0.65 0.65 1.666104 1.901148 1.242902 \n", "0.70 0.70 1.734948 1.827381 1.177611 \n", "0.75 0.75 1.810134 1.844308 1.178763 \n", "0.80 0.80 1.897220 1.916914 1.317607 \n", "0.85 0.85 2.000017 2.087947 1.409395 \n", "0.90 0.90 2.137396 2.278907 1.218546 \n", "\n", " DML LQTE pointwise upper DML LQTE joint lower DML LQTE joint upper \n", "0.10 2.011598 -0.208300 2.415294 \n", "0.15 2.210323 -0.620026 2.725031 \n", "0.20 1.513992 -0.456617 1.872354 \n", "0.25 1.769805 -0.279524 2.142482 \n", "0.30 1.823247 0.257762 2.107935 \n", "0.35 1.804219 0.377955 2.063590 \n", "0.40 1.942312 0.542769 2.196824 \n", "0.45 1.966015 0.590738 2.216113 \n", "0.50 2.058175 0.697118 2.305687 \n", "0.55 2.238101 0.780943 2.503089 \n", "0.60 2.189998 0.510586 2.495405 \n", "0.65 2.559394 0.950280 2.852016 \n", "0.70 2.477150 0.888757 2.766005 \n", "0.75 2.509853 0.882896 2.805720 \n", "0.80 2.516222 1.051186 2.782643 \n", "0.85 2.766499 1.107746 3.068148 \n", "0.90 3.339268 0.747164 3.810650 \n" ] } ], "source": [ "LQTE = Y1_quant - Y0_quant\n", "data = {\"Quantile\": tau_vec, \"LQTE\": LQTE, \"DML LQTE\": dml_LQTE.coef,\n", " \"DML LQTE pointwise lower\": ci_LQTE['2.5 %'], \"DML LQTE pointwise upper\": ci_LQTE['97.5 %'],\n", " \"DML LQTE joint lower\": ci_joint_LQTE['2.5 %'], \"DML LQTE joint upper\": ci_joint_LQTE['97.5 %']}\n", "df = pd.DataFrame(data)\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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xWOp1nESxjiGIiILimQri+dQ5EE/7Ws9lGzdujISEBBQUFGDXrl1+HeAq4mk/PHPmzIBT6Pbs2VPtsVdk3LhxeOGFF7B9+3Zs2rSp0g5xRUVF3rH99re/9TvPE4hyc3MDXtfzabKv/Px8LFq0CJqmYdGiRUhLSyt3/rFjx6rzcOpcmzZtAJhVwDfeeCPi3kzXdHyffPIJCgsL8dvf/hZPPvlkufND+RwMBc/jHDx4MF566aV6Hk1kqulzwVMl27lzZ8jH1KNHD/To0QP33HMPRARfffUVJk6ciE8++QRvvfUWrr/++pDfJxGViqxXLCKKWJ758u+++27AaVYLFixAZmYmkpOTvWHCYrF4d3V//fXXg7qf06dPAwDatWtX7rzjx4971xiFwrBhw3DeeecBAG677bZKp/089NBDOHHiBNLT0zF16lS/8zyhr+yeKgBQUFCA5cuXlzs9Ozsbuq4jJSWlXAACgHfeeSck1S5fnrDmWV9VWy1btkTv3r2Rm5vrXTsRSWo6vsqegyKC+fPnB7xeqI9vsEaPHg0A+Pjjj8M+BdLDU62s68daUzV9LowaNQqAue7qyJEjQV2nJs8DpRQuvvhiTJw4EQCwefPmoK9LRDXDEEREQZkwYQLatm2LI0eO4K677vJ7gd+/fz/uvvtuAMDtt9/ut+D7wQcfhNVqxUsvvYRXXnml3Bv7n3/+GZs2bfL+27MY+1//+pd3ITpghobJkycjOzs7pI/rnXfeQVpaGjZs2IBLL7203KaSnilRzzzzDKxWK/73v/+hadOmfpcZPnw4AODll1/2W0Sen5+Pm266KeBGlWeccQbS09ORlZWFt99+2++89evX469//WuoHqJX69atAZQ2lwiFxx57DIC5Aesnn3xS7nwRwYYNG/Dll1+G7D6roybj8zwH33//fW8TBMBshvHwww9X2OShadOmsNvtOHbsmDdI1YV+/frhiiuuwKFDh3D55ZcH3LA0Pz8f8+bNC3r9W1XC8VwKt5o8F/r27Ytx48ahsLAQ48aNw8GDB/2u43K58PHHH/udVtWxeeutt/z+5nnk5uZ6G9AECuBEFFqcDkdE+Nvf/oZXX321wvNfeeUV9O/fH++//z5GjRqFWbNmYdGiRTjvvPOQm5uLr776CkVFRRg5ciQeeeQRv+uec845mD17Nm644QbcdttteOqpp3DOOed4N0vdsmULHn74YW/16M4778Rbb72FRYsWoWPHjjjvvPPgdDqxcuVKJCQk4A9/+APeeOONkD32Tp06YfXq1Rg3bhyWLl3qvc82bdogKysLa9asQU5ODpo3b45Zs2bh4osvLncbV155JZ5//nl8++23OPPMM3H++efDMAx8++23sNvtAcdssVjw8MMP4y9/+Quuu+46vPzyy+jYsSMOHjyItWvX4ve//z1WrVoVcCpdTY0fPx4zZszAiy++iB9++AFt2rSBpmm47LLLcNlll9XoNseOHYsXXngBd999Ny677DJ07twZXbt2RWpqKk6cOIEtW7bg+PHjuO+++3DJJZeE7LGEc3xjx47FWWedhU2bNqFLly4YMmQIEhMTsWHDBhw5cgT33XdfwGlyNpsNl112Gd5//3307dsX559/PhISEgAA//73v8P6OOfMmYOsrCx8/vnn6Nq1K/r06YMOHTpARHDgwAFs2bIFJSUl2LFjB84444xa398VV1yBZ555BsOHD8dFF13kbdjw5JNPVri5byDTpk2rtDPaHXfcgf79+9d6vEDNn6tz5szBmDFjsH79emRkZPhtlrpt2zacOHHC78OdkSNHIjExEQsXLsT555+PjIwMWCwWDB48GNdffz0+/PBDTJ48GS1btkTfvn29XTXXrFmD7Oxs9OzZEzfeeGNIHjMRVaKOW3ITUQTx7LlR1Zfv/iwHDx6U2267TTp27Ch2u12Sk5Nl4MCBMmvWLL+NH8v68ccf5Y9//KN06NBBHA6HpKamSo8ePWTq1Kny448/+l12//79MmnSJGnbtq04HA5p166d3HzzzXLs2LEK9+CoyT5BvoqLi+XVV1+VESNGyBlnnCFWq9X7+Fu0aCGnTp2q9PqZmZkydepUad26tdhsNmnVqpXcdNNN8uuvv1a6b8jChQtl0KBBkpaWJklJSXL22WfLK6+8IoZhVLgnSlV7pVS2H9CCBQtk8ODBkpycLEqpcuOq7j5BHtu2bZObbrpJMjIyJC4uThISEqRjx44ycuRIefHFF+WXX36p+OBVIdDzUKR0n5shQ4ZUeRvVHV9ubq488MAD0rVrV4mLi5NmzZrJ+PHj5dtvv630fk+dOiV/+tOfpG3btmKz2crtW1PVHjJVHefK9qTSdV3mz58vY8aMkTPOOENsNps0btxYevbsKddff70sWLDAb5PjYI5f2fF7FBYWyr333iudO3cWu91erc1Pg90nCIAsWLDA77q12SfIoybP1eLiYpk1a5ZccMEFkpaWJna7XVq3bi0jRoyQl19+udzlV61aJcOHD5f09HTRNM3vb9OqVavkzjvvlHPPPVeaN28udrtdmjdvLgMHDpR//vOfkpeXV+UxJKLaUyIhnnRORBQjDh06hPPPPx8HDx7E9ddfj9mzZ5fbu4WIiIiiD9cEERFVoE2bNli2bBmaN2+OOXPmlGuIQERERNGJlSAioips377d2x77mmuuqdbGr0RERBR5GIKIiIiIiKhB4XQ4IiIiIiJqUBiCiIiIiIioQWEIIiIiIiKiBiWqN0s1DANHjhxBcnIy29YSERERETVgIoLc3Fy0bNkSmlZ5rSdiQtATTzyBv/71r/jzn/+M559/PqjrHDlyBG3atAnvwIiIiIiIKGocOnQIrVu3rvQyERGCNm7ciNdeew29e/eu1vWSk5MBmA80JSUlHEMLmtPpxJdffolLLrkENputXscSi3h8w4/HOLx4fMOLxze8eHzDi8c3vHh8wyuSjm9OTg7atGnjzQiVqfcQlJeXh0mTJuH111/HY489Vq3reqbApaSkREQISkhIQEpKSr0/AWIRj2/48RiHF49vePH4hhePb3jx+IYXj294ReLxDWaZTL2HoNtuuw2/+c1vMHz48CpDUHFxMYqLi73/zsnJAWAefKfTGdZxVsVz//U9jljF4xt+PMbhxeMbXjy+4cXjG148vuHF4xtekXR8qzOGet0s9b///S9mzpyJjRs3Ii4uDkOHDkXfvn0rXBM0ffp0zJgxo9zp8+fPR0JCQphHS0REREREkaqgoAATJ05EdnZ2lbPE6i0EHTp0CGeffTaWLFniXQtUVQgKVAlq06YNTp48GRHT4ZYsWYIRI0ZETCkwlvD4hh+PcXjx+IYXj2948fiGF49vePH4hlckHd+cnBw0adIkqBBUb9PhNm3ahOPHj6N///7e03Rdx6pVq/DSSy+huLgYFovF7zoOhwMOh6PcbdlstkoPuq7rYS/R6boOq9UKXderbMlH1cfjG36xcoxtNlu5vx2RpKq/V1Q7PL7hxeMbXjy+4cXjG16RcHyrc//1FoIuvvhibNu2ze+066+/Ht26dcN9990XkjcxIoJjx44hKyur1rcVzH01b94chw4d4p5FYcDjG36xdIzT0tLQvHnzqH8cREREFB71FoKSk5PRs2dPv9MSExPRuHHjcqfXlCcANWvWDAkJCWF9Q2QYBvLy8pCUlBTVn6JHKh7f8IuFYywiKCgowPHjxwEALVq0qOcRERERUSSq9+5w4aLrujcANW7cOOz3ZxgGSkpKEBcXF7VvICMZj2/4xcoxjo+PBwAcP34czZo1i+ipcURERFQ/IioErVixImS35VkDxK5xRA2P5/fe6XQyBBEREVE50ftxb5C4JoCo4eHvPREREVUm5kMQERERERGRL4agBmLu3LlIS0ur72FUSzSOmYiIiIgiH0NQBJoyZQqUUuW+Ro0aFdT127dvX27D2auuugq7d+8Ow2j91XVwUUph4cKFFZ6v6zqee+459OrVC3FxcUhPT8fo0aOxZs0a72WGDh0a8Hh7voYOHQrAPK6Bzn/iiSfC/CiJiIiIKJQiqjEClRo1ahTmzJnjd1qgjWKDFR8f7+2a1VCICK6++mosXboUTz/9NC6++GLk5OTg5ZdfxtChQ/G///0P48ePx4cffoiSkhIAwKFDh3Duuedi6dKlOPPMMwEAdrvde5uPPvoobrzxRr/7SU5OrrsHRURERES1xkpQhHI4HGjevLnfV3p6OgDzzf306dPRtm1bOBwOtGzZEnfccQcAs6rx888/4y9/+Yu3UgGUr9BMnz4dffv2xRtvvIG2bdsiKSkJt956K3Rdx1NPPYXmzZujWbNmmDlzpt+4/vGPf6BXr15ITExEmzZtcOuttyIvLw+A2d3v+uuvR3Z2tve+p0+fDgAoLi7GtGnT0KpVKyQmJmLAgAHlugHOnTsXbdu2RUJCAn7729/i1KlTtTqG7733Ht5//3289dZbuOGGG9ChQwf06dMH//rXv3DZZZfhhhtuQH5+Pho1auQ9xk2bNgUANG7c2Htao0aNvLeZnJxc7ueSmJhYq3ESERERUd1qUCFIRCAlYfxyVnC6SEgfxwcffIDnnnsOr732Gvbs2YOFCxeiV69eAIAPP/wQrVu3xqOPPoqjR4/i6NGjFd7Ovn378Pnnn2Px4sX4z3/+g9mzZ+M3v/kNDh8+jJUrV+LJJ5/E//3f/2HDhg3e62iahhdffBE//vgj3nzzTXz11Ve49957AQCDBg3C888/j5SUFO99T5s2DQAwdepUrFu3Dv/973+xdetWTJgwAaNGjcKePXsAABs2bMAf//hHTJ06FZs3b8awYcPw2GOP1eo4zZ8/H126dMHYsWPLnXf33Xfj1KlTWLJkSa3ug4iIiIiiT8OaDucEsp7MCutd5CCn3Glp96UB9vKXrcynn36KpKQkv9MeeOABPPDAAzh48CCaN2+O4cOHw2azoW3btjj33HMBAI0aNYLFYvFWLCpjGAbeeOMNJCcno0ePHhg2bBh27dqFRYsWQdM0dO3aFU8++SSWL1+OAQMGAADuvPNO7/Xbt2+Pxx57DDfffDNeeeUV2O12pKamQinld98HDx7EnDlzcPDgQbRs2RIAMG3aNCxevBhz5szB3//+d7zwwgsYNWqUN1B16dIFa9euxeLFi6t34Hzs3r0b3bt3D3ie5/TqrpO677778H//939+p33++ee44IILajZIIiIiIqpzDSsERZFhw4Zh1qxZfqd5pmVNmDABzz//PDp27IhRo0ZhzJgxGDt2LKzW6v0427dv77ee5YwzzoDFYoGmaX6nHT9+3PvvpUuX4vHHH8fOnTuRk5MDl8uFoqIiFBQUVLgx7bZt26DrOrp06eJ3enFxMRo3bgwA2LFjB37729/6nT9w4MBahSAAVVbhfNf7BOOee+7BlClT/E5r1apVdYdFRERERPWoYYUgm7sqEwaGYSAnNwcpySl+IcJzv9WVmJiIzp07BzyvTZs22LVrF5YuXYolS5bg1ltvxdNPP42VK1fCZgv+zspeVikV8DTDMAAABw4cwKWXXopbbrkFM2fORKNGjbB69Wr88Y9/RElJSYUhKC8vDxaLBZs2bYLFYvE7r2y1K5QyMjKwY8eOgOd5Ti8bzKrSpEmTCn8uRERERBQdGlQIUkpVe1pa0LdtKCibgrIrKC38u9XHx8dj7NixGDt2LG677TZ069YN27ZtQ//+/WG326Hresjvc9OmTTAMA88++6w36L333nt+lwl03/369YOu6zh+/HiF08a6d+/ut/YIANavX1+r8V5zzTWYOHEiPvnkk3Lrgp599lm0bNkSI0aMqNV9EBEREVH0aVAhKJoUFxfj2LFjfqdZrVY0adIEc+fOha7rGDBgABISEvDOO+8gPj4e7dq1A2BOc1u1ahWuvvpqOBwONGnSJCRj6ty5M5xOJ/75z39i7NixWLNmDV599VW/y7Rv3x55eXlYtmwZ+vTpg4SEBHTp0gWTJk3Cddddh2effRb9+vXDiRMnsGzZMvTu3Ru/+c1vcMcdd2Dw4MF45plnMG7cOHzxxRdBT4Xbv38/Nm/e7HdaRkYGrr76arz33nuYPHlyuRbZn376KRYvXlytyhkA5Obmlvu5JCQkICUlpVq3Q0RERET1p0F1h4smixcvRosWLfy+zj//fABAWloaXn/9dQwePBi9e/fG0qVL8cknn3jX1zz66KM4cOAAOnXq5G35HAp9+vTBP/7xDzz55JPo2bMn5s2bh8cff9zvMoMGDcLNN9+Mq666Ck2bNsVTTz0FAJgzZw6uu+463H333ejatSvGjx+PjRs3om3btgCA8847D6+//jpeeOEF9OnTB19++WW5BgQVueuuu9CvXz+/r++//x5KKfzvf//DAw88gOeeew5du3ZFnz598P777+P777/HsGHDqn0MHn744XI/F08zByIiIiKKDkpC3b+5DuXk5CA1NRXZ2dnlPokvKirC/v370aFDB8TFxYV9LIZhICcnBykpAdYEUa2F6vh+9913GD58OP74xz/i6aefDuEIo18sPYfr+vc/GE6nE4sWLcKYMWOqXYGkqvH4hhePb3jx+IYXj294RdLxrSwblBXd73SIqql///5YtmwZEhMTsW/fvvoeDhERERHVA64JogbHM2WOiIiIiGpHdHNSWbRNLmMliIiIiIiIasTIM9zf1O84qoshiIiIiIiIqk1cAiM7ytKPG0MQERERERFVm5FnQAqiaxqcB0MQERERERFVi7gERpYRtWkiSodNRERERET1xcgzIIUCFafqeyg1wu5wREREREQUNG8VyFHfI6k5VoKIiIiIiCho0V4FAhiCiIiIiIgoSL5VIKUYgihElFKVfk2fPr3OxjJ06FDceeedlV7m008/xZAhQ5CcnIyEhAScc845mDt3rvf86dOnV/mYAGDKlCkBzxs1alQYHyERERERVUcsVIEAhqCIc/ToUe/X888/j5SUFL/Tpk2b5r2siMDlctXbWP/5z39i3LhxGDx4MDZs2ICtW7fi6quvxs033+wd57Rp0/zG37p1azz66KN+p3mMGjXK7/SjR4/iP//5T309PCIiIiLyEStVIIAhKOI0b97c+5WamgqllPffO3fuRHJyMj7//HOcddZZcDgcWL16NaZMmYLx48f73c6dd96JoUOHev9tGAYef/xxdOjQAfHx8ejTpw/ef//9Go/z0KFDuPvuu3HnnXfi73//O3r06IHOnTvj7rvvxtNPP41nn30WGzZsQFJSkt9jslgsSE5O9jvNw+Fw+J3evHlzpKen13iMRERERBQ6sVIFAhpYdzgRQYGzICy3bRgG8p35sJRYoGn+2TLBlhDStHz//ffjmWeeQceOHYMOCY8//jjeeecdvPrqq8jIyMCqVavw+9//Hk2bNsWQIUOqPYb3338fTqfTrzLl8ac//QkPPPAA/vOf/2DAgAHVvm0iIiIiiiyxVAUCGlgIKnAWIOnxpDq/37y/5iHRnhiy23v00UcxYsSIoC9fXFyMv//971i6dCkGDhwIAOjYsSNWr16N1157rUYhaPfu3UhNTUWLFi3KnWe329GxY0fs3r27Wrf56aefIinJ/+fzwAMP4IEHHqj2+IiIiIgodLxVoNToD0BAAwtBseLss8+u1uX37t2LgoKCcsGppKQE/fr1C+XQ/Njt9mpdftiwYZg1a5bfaY0aNQrlkIiIiIiommKtCgQ0sBCUYEtA3l/zwnLbhmEgJzcHKckpAafDhVJion9VSdM0iIjfaU6n0/t9Xp75mD/77DO0atXK73IOR812ucrIyEB2djaOHDmCli1b+p1XUlKCffv2YeTIkdW6zcTERHTu3LlG4yEiIiKi8Ii1KhDQwEKQUiqk09J8GYYB3aYj0Z5YLgSFW9OmTfHDDz/4nbZ582bYbDYAQI8ePeBwOHDw4MEaTX0L5He/+x3uu+8+PPvss3j22Wf9znv11VdRUFCA6667LiT3RURERET1IxarQEADC0Gx6qKLLsLTTz+Nt956CwMHDsQ777yDH374wTvVLTk5GdOmTcNf/vIXGIaB888/H9nZ2VizZg1SUlIwefLkCm/7xIkT2Lx5s99pLVq0QNu2bfHUU09h2rRpiIuLw7XXXgubzYaPPvoIDzzwAB577DH07NmzWo+juLgYx44d8zvNarWiSZMm1bodIiIiIgqNWKwCAQxBMWHkyJF46KGHcO+996KoqAh/+MMfcN1112Hbtm3ey/ztb39D06ZN8fjjj+Onn35CWloa+vfvX2XTgfnz52P+/Pl+p/3tb3/D//3f/+Evf/kLOnbsiGeffRYvvPAC8vPzAQD/+c9/cPXVV1f7cSxevLhco4WuXbti586d1b4tIiIiIqqdWK0CAQxBEW3KlCmYMmWK999Dhw4tt/bHY8aMGZgxY0aFt6WUwp///Gf8+c9/Dvr+V6xYUeVlxo0bh3HjxgEATp8+jYsvvhizZs3CZZddhoSE8muhDhw4EPB25s6di7lz5wY9NiIiIiIKr1itAgHcLJVCqFGjRli6dCkuvvhirFu3rr6HQ0REREQ1FMtVIICVIAqxxo0b4+GHH67vYRARERFRLcRyFQhgJYiIiIiIiHyIS2Bkxm4VCGAIIiIiIiIiH0aeASkSqLjYDEAAQxAREREREbk1hCoQwBBERERERERuDaEKBDAEERERERERGk4VCGAIIiIiIiIiNJwqEMAQRERERETU4DWkKhDQQEOQOAVSFIav4gCnOaXOH9/cuXORlpZW5/fbvn17PP/883V+v+Fw7NgxjBgxAomJid5jqZTCwoULK7zOgQMHoJTC5s2b62SMDRmPNRERUWg1pCoQ0AA3SxWnoGRXCaQotOFERIAioCSuxC89qzgFe1c7lC34J9SUKVPw5ptvAgBsNhvatm2L6667Dg888ACs1qp/ZFdddRXGjBlTrfEPHToUffv2rVWI2bhxIxITE2t8/WB8//33eOKJJ7Bq1SpkZ2ejTZs2GDp0KO655x506dIlZPfz3HPP4ejRo9i8eTNSU1MBAEePHkV6enrI7qO+zJ07F3feeSeysrKCvk4onh9EREQUmRpaFQhoiJUgHWYAspoBJZRfcPjfJqzu+9KrP8xRo0bh6NGj2LNnD+6++25Mnz4dTz/9dFDXjY+PR7Nmzap/p7XUtGlTJCQkhO32Fy9ejEGDBqG4uBjz5s3Djh078M477yA1NRUPPfRQSO9r3759OOuss5CRkeE9ls2bN4fD4Qjp/TQ0JSUl9T0EIiIiKqOhVYGAhhiC3JRVQdlD+GVTgA1QNp/TrDV/IjkcDjRv3hzt2rXDLbfcguHDh+Pjjz8GAGRmZuK6665Deno6EhISMHr0aOzZs8d73bLT4aZPn46+ffvi7bffRvv27ZGamoqrr74aubm5AMzK08qVK/HCCy9AKQWlFA4cOICzzz4bzzzzjPd2xo8fD5vNhry8PADA4cOHoZTC3r17AfhPhxMRTJ8+HW3btoXD4UDLli1xxx13eG+ruLgY06ZNQ6tWrZCYmIgBAwZgxYoVFR6PgoICTJ06FaNHj8bHH3+M4cOHo0OHDhgwYACeeeYZvPbaa97Lrly5Eueeey4cDgdatGiB+++/Hy6Xy3v+0KFDcccdd+Dee+9Fo0aN0Lx5c0yfPt17fvv27fHBBx/grbfeglIKU6ZMAVB+Otw333yDfv36IS4uDmeffTa+//77cuP+4YcfMHr0aCQlJeGMM87Atddei5MnTwY9FgDIysrCn/70J5xxxhmIi4tDz5498emnn3rPX716NS644ALEx8ejTZs2uOOOO5Cfn1/hsSzL9/nRu3dvpKenB/X8CPbxTZ06FXfeeSeaNGmCkSNHYuLEibjqqqv8xuB0OtGkSRO89dZbAMzAe/755yMtLQ2NGzfGpZdein379gX9mIiIiCg4DbEKBDTgEBRt4uPjvZ+iT5kyBd9++y0+/vhjrFu3DiKCMWPGwOl0Vnj9ffv2YeHChfj000/x6aefYuXKlXjiiScAAC+88AIGDhyIG2+8EUePHsXRo0fRpk0bDBkyxBtMRARff/010tLSsHr1agBm2GjVqhU6d+5c7v4++OADPPfcc3jttdewZ88eLFy4EL169fKeP3XqVKxbtw7//e9/sXXrVkyYMAGjRo3yC3O+vvjiC5w6dQr33HNPwPM9oe+XX37BmDFjcM4552DLli2YNWsWZs+ejccee8zv8m+++SYSExOxYcMGPPXUU3j00UexZMkSAOa0vlGjRuHKK6/E0aNH8cILL5S7v7y8PFx66aXo0aMHNm3ahOnTp2PatGl+l8nKysJFF12Efv364dtvv8XixYvx66+/4sorrwx6LIZhYPTo0VizZg3eeecdbN++HU888QQsFgsA8+c6atQoXHHFFdi6dSveffddrF69GlOnTg14nCqyb98+fPTRR/jvf/+Ljz/+OKjnR3Uen91ux5o1a/Dqq69i0qRJ+OSTT7xhGjB/vgUFBfjtb38LAMjPz8ddd92Fb7/9FsuWLYOmafjtb38LwzCq9biIiIiocg2xCgQ0wDVB0UZEsGzZMnzxxRe4/fbbsWfPHnz88cdYs2YNBg0aBACYN28e2rRpg4ULF2LChAkBb8cwDMydOxfJyckAgGuvvRbLli3DzJkzkZqaCrvdjoSEBDRv3tx7naFDh2L27NnQdR0//PAD7HY7rrrqKqxYsQKjRo3CihUrMGTIkID3d/DgQTRv3hzDhw/3rms699xzvefNmTMHBw8eRMuWLQEA06ZNw+LFizFnzhz8/e9/L3d7nmpTt27dKj1er7zyCtq0aYOXXnoJSil069YNR44cwX333YeHH34Ymmbm/t69e+ORRx4BAGRkZOCll17CsmXLMGLECDRt2hQOhwPx8fF+x8PX/PnzYRgGZs+ejbi4OJx55pk4fPgwbrnlFu9lXnrpJfTr18/v8bzxxhto06YNdu/e7V3DVNlYli5dim+++QY7duzwXr5jx47e23v88ccxadIk3Hnnnd7rv/jiixgyZAhmzZqFuLi4So+Xh2EYmDNnDkQEKSkpQT0/gn18GRkZeOqpp7yX6dSpExITE7FgwQJce+213uN52WWXeZ+fV1xxhd/43njjDTRt2hTbt29Hz549g3pMREREVLmGWgUCWAmKWJ9++imSkpIQFxeH0aNH46qrrsL06dOxY8cOWK1WDBgwwHvZxo0bo2vXrtixY0eFt9e+fXvvG0wAaNGiBY4fP17pGC644ALk5ubi+++/x8qVKzFkyBAMHTrUWx1auXIlhg4dGvC6EyZMQGFhITp27Igbb7wRCxYs8E5J27ZtG3RdR5cuXZCUlOT9WrlyZYVTnkSCa2SxY8cODBw40O8XefDgwcjLy8Phw4e9p/Xu3dvvesEcj7L307t3b7+QMXDgQL/LbNmyBcuXL/d7jJ4Q5/s4KxvL5s2b0bp16wqbPmzZsgVz5871u4+RI0fCMAzs378/6MdTk+dHsI/vrLPO8rue1WrFlVdeiXnz5gEwqz4fffQRJk2a5L3Mnj17cM0116Bjx45ISUlB+/btAZgBmoiIiEJDz9UbZBUIYCUoYg0bNgyzZs2C3W5Hy5Ytg+oKVxmbzeb3b6VUlVOL0tLS0KdPH6xYsQLr1q3DiBEjcOGFF+Kqq67C7t27sWfPngorQW3atMGuXbuwdOlSLFmyBLfeeiuefvpprFy5Enl5ebBYLNi0aZN3WpdHUlJSwNvLyMgAAOzcuRODBw8O9mFXqCbHo7ry8vIwduxYPPnkk+XOa9GiRVBjiY+Pr/I+/vSnP/mtt/Jo27Zt0GOtyfEI9vEF6hg4adIkDBkyBMePH8eSJUsQHx+PUaNGec8fO3Ys2rVrh9dffx0tW7aEYRjo2bMnGysQERGFiLgEkiUNsgoEMARFrMTExIBrbbp37w6Xy4UNGzZ4p8OdOnUKu3btQo8ePWp8f3a7Hbpevo3dkCFDsHz5cnzzzTeYOXMmGjVqhO7du2PmzJlo0aJFpW2p4+PjMXbsWIwdOxa33XYbunXrhm3btqFfv37QdR3Hjx/HBRdcENT4LrnkEjRu3BhPP/10wBCUlZWFtLQ0dO/eHR988AFExPsLvWbNGiQnJ6N169ZBHo2qde/eHW+//TaKioq81aD169f7XaZ///744IMP0L59+xqH2N69e+Pw4cN+08vK3sf27dsDPldCKdDzozaPb9CgQWjTpg3effddfP7555gwYYI3iHmez6+//rr3+eFZh0ZERESh4a0CpTa8AARwOlzUycjIwLhx43DjjTdi9erV2LJlC37/+9+jVatWGDduXI1vt3379tiwYQMOHDiAkydPeqsAQ4cOxRdffAGr1eqd6jR06FDMmzevwioQYHaomz17Nn744Qf89NNPeOeddxAfH4927dqhS5cumDRpEq677jp8+OGH2L9/P7755hs8/vjj+OyzzwLeXmJiIl588UUsWrQIl112GZYuXYoDBw7g22+/xb333oubb74ZAHDrrbfi0KFDuP3227Fz50589NFHeOSRR3DXXXd51wOFwsSJE6GUwo033ojt27dj0aJFfp30AOC2227D6dOncc0112Djxo3Yt28fvvjiC1x//fUBA2cgQ4YMwYUXXogrrrgCS5Yswf79+/H5559j8eLFAID77rsPa9euxdSpU7F582bs2bMHH330UbUbI1Ql0POjto9v4sSJePXVV7FkyRK/qXDp6elo3Lgx/vWvf2Hv3r346quvcNddd4X08RARETVkDb0KBDTgECQugZSE8MspgNPcjNV7miu0G7J6zJkzB2eddRYuvfRSDBw4ECKCRYsWlZvSVB3Tpk2DxWJBjx490LRpU+/aiwsuuACGYfgFnqFDh0LX9QrXAwHmVLrXX38dgwcPRu/evbF06VJ88sknaNy4sfcxXHfddbj77rvRtWtXjB8/Hhs3bqx0CteYMWOwevVq2Gw2TJw4Ed26dcM111yD7Oxsb/e3Vq1aYdGiRfjmm2/Qp08f3HzzzfjjH/+I//u//6vxsQkkKSkJn3zyibey9eCDD5abFtayZUusWbMGuq7jkksuQa9evXDnnXciLS2tWoHsgw8+wDnnnINrrrkGPXr0wL333usNGb1798bKlSuxe/duXHDBBejXrx8efvhhb8OJUAn0/Kjt45s0aRK2b9+OVq1a+VX3NE3Df//7X2zatAk9e/bEX/7yl6D3yCIiIqKq6TkNdy2Qh5JgV5xHoJycHKSmpiI7OxspKSl+5xUVFWH//v3o0KGD3+J1cQpKdpWYm5iGkIh4p0b5JmoVp2Dvajf3EaIaMwwDOTk5SElJCWlFh0rF0jGu6Pe/PjmdTixatAhjxoyp1QcWFBiPb3jx+IYXj2948fj6E5fAdcgFEYEWX/vX+5LCEizZtgSjR46GPc4eghHWXGXZoKwGtyZI2cxQguBmIwXNMAwU5RbBnmz3fwNpAQMQEREREUUEbxWoga4F8mhwIQhwh5IQfxCgDAVVoqDiFJTWsJ9URERERBR5xCWQ7Ia9Fsgjuue8EBERERFRULgWqBRDEBERERFRjGMVyB9DEBERERFRjGMVyB9DEBERERFRDGMVqDyGICIiIiKiGMYqUHkMQUREREREMYpVoMAYgoiIiIiIYhSrQIE1yBAkukCcdfSlS1gfy4oVK6CUQlZWVljvJ9xEBDfddBMaNWoEpRQ2b96MoUOH4s4776z0eu3bt8fzzz9fJ2Ns6HisiYiIogurQBVrcJulii5w/eICSkJ7u4YYQD7gynJBUz7Z0g5YW1mhLME/8aZMmYKsrCwsXLiwyssOGjQIR48eRWpqalhu/9ixY5g5cyY+++wz/PLLL2jWrBn69u2LO++8ExdffHHQ91mVxYsXY+7cuVixYgU6duyIJk2a4MMPP4TNFuJdbevBgQMH0KFDB3z//ffo27dvUNeZPn06Fi5ciM2bN4d1bERERBS7vFWgVAagshpcCIIBMwBZENJHrwwFOAFlV6X1NZf7vgz3/YWB3W5H8+bNw3LbBw4cwODBg5GWloann34avXr1gtPpxBdffIHbbrsNO3fuDNl97du3Dy1atMCgQYO8pzVq1Chkt99QlZSUwG631/cwiIiIqI6xClS5BjkdDgBgBZRVhfSr7G2GImQVFxfjjjvuQLNmzRAXF4fzzz8fGzdu9J5fdjrc3LlzkZaWhi+++ALdu3dHUlISRo0ahaNHjwIwKwxvvvkmPvroIyiloJTCihUrAt73rbfeCqUUvvnmG1xxxRXo0qULzjzzTNx1111Yv36993IHDx7EuHHjkJSUhJSUFFx55ZX49ddfvedPnz4dffv2xdtvv4327dsjNTUVV199NXJzcwGYlanbb78dBw8ehFIK7du3B4By0+FOnDiByy67DPHx8ejQoQPmzZtXbsxZWVm44YYb0LRpU6SkpOCiiy7Cli1bgh4LABiGgaeeegqdO3eGw+FA27ZtMXPmTO/5hw4dwpVXXom0tDQ0atQI48aNw4EDByr/Qfrw/MyWLVuGs88+GwkJCRg0aBB27doFwPwZzpgxA1u2bPH+jObOnVutx/fvf/8bHTp0QFxcHP71r3+hZcuWMAzDbxzjxo3DH/7wBwBmCB0/fjy6dOmClJQUnHPOOVi6dGnQj4mIiIgiC9cCVa7hhqAoce+99+KDDz7Am2++ie+++w6dO3fGyJEjcfr06QqvU1BQgGeeeQZvv/02Vq1ahYMHD2LatGkAgGnTpuHKK6/0BqOjR4/6VV88Tp8+jcWLF+O2225DYmJiufPT0tIAmIFh3LhxOH36NFauXIklS5bgp59+wlVXXeV3+X379mHhwoX49NNP8emnn2LlypV44oknAAAvvPACHn30UbRu3RpHjx71C3m+br31Vhw+fBjLly/H+++/j1deeQXHjx/3u8yECRNw/PhxfP7559i0aRP69++Piy++2O94VTYWAPjrX/+KJ554Ag899BC2b9+O+fPn44wzzgAAOJ1OjBw5EsnJyfj666+xZs0ab9AsKaneHMsHH3wQzz77LL799ltYrVZvILnqqqtw991348wzz/T+jDzHM5jHt3fvXnzwwQf48MMPsXnzZkyYMAGnTp3C8uXLvZfx/HwnTZoEAMjLy8Po0aOxcOFCbNq0CaNGjcLYsWNx8ODBaj0mIiIiqn+sAlWt4U2HiyL5+fmYNWsW5s6di9GjRwMAXn/9dSxZsgSzZ8/GPffcE/B6TqcTr776Kjp16gQAmDp1Kh599FEAQFJSEuLj41FcXFzpNLq9e/dCRNCtW7dKx7hs2TJs27YN+/fvR5s2bQAAb731Fs4880xs3LgR55xzDgAzLM2dOxfJyckAgGuvvRbLli3DzJkzkZqaiuTkZFgslgrHtHv3bixduhTr16/HgAEDAACzZ89G9+7dvZdZvXo1vvnmGxw/fhwOhwMA8Mwzz2DhwoV4//33cdNNN1U5ltzcXLzwwgt46aWXMHnyZABAp06dcP755wMA3n33XRiGgX//+9/ePypz5sxBWloaVqxYgUsuuaTS4+Vr5syZGDJkCADg/vvvx29+8xsUFRUhPj4eSUlJsFqtfscj2MdXUlKCt956C02bNvVed/To0Zg/f753Hdf777+PJk2aYNiwYQCAPn36oFevXsjJyUFKSgr+9re/YcGCBfj4448xderUoB8TERER1T+uBaoaK0ERbN++fXA6nRg8eLD3NJvNhnPPPRc7duyo8HoJCQneAAQALVq0KFcxqYpIcF3tduzYgTZt2ngDEAD06NEDaWlpfmNs3769N3TUZEw7duyA1WrFWWed5T2tW7du3ooUAGzZsgV5eXlo3LgxkpKSvF/79+/Hvn37ghrLjh07UFxcXGHThy1btmDv3r1ITk723n6jRo1QVFTkdx/B6N27t98YAFR6TIJ9fO3atfMLQAAwadIkfPDBByguLgYAzJs3D1dffTU0zfwTkJeXh3vuuQcDBgxAo0aNkJSUhB07drASREREFGXqugokxeZ7RjHC2xE51FgJikFlO6oppYIONR4ZGRlQSoWs+UGgMZVdo1JbeXl5aNGiRcA1Tr5hqbKxxMfHV3kfZ511VsD1SGWDR1V8x+H5I1XZMQn28QWavjh27FiICD777DOcc845+Prrr/Hcc895z582bRqWLFmCGTNmoFevXkhMTMTvfve7ak/xIyIiovpVl1UgEYFx2v3eJbRv68KOISiCderUCXa7HWvWrEG7du0AmFPdNm7cWOX+OZWx2+3Qdb3SyzRq1AgjR47Eyy+/jDvuuKPcG+usrCykpaWhe/fuOHToEA4dOuStBm3fvh1ZWVno0aNHjcdYVrdu3eByubBp0ybvdLhdu3b57Y/Uv39/HDt2DFar1dtcoboyMjIQHx+PZcuW4YYbbih3fv/+/fHuu++iWbNmSElJqdF9BCPQz6g2jy8uLg6XX3455s2bh71796Jr167o37+/9/w1a9Zg8uTJuPTSS5GSkoKCgoJqNXsgIiKi+lfnVaBsgZEVZenHjdPhIlhiYiJuueUW3HPPPVi8eDG2b9+OG2+8EQUFBfjjH/9Y49tt3749tm7dil27duHkyZNwOp0BL/fyyy9D13Wce+65+OCDD7Bnzx7s2LEDL774IgYOHAgAGD58OHr16oVJkybhu+++wzfffIPrrrsOQ4YMwdlnn13jMZbVtWtXXHzxxbjllluwYcMGbNq0CTfccINf5Wb48OEYOHAgxo8fjy+//BIHDhzA2rVr8eCDD+Lbb78N6n7i4uJw33334d5778Vbb72Fffv2Yf369Zg9ezYAc1pZkyZNMG7cOHz99dfYv38/VqxYgTvuuAOHDx8O2eNt37499u/fj82bN+PkyZMoLi6u9eObNGkSPvvsM7zxxhvehggeGRkZWLBgAbZt24YtW7Zg4sSJIa/UERERUXjVZUc4KRHoJ/Somwbn0XBDkMudlkP4VfY24arZ0AzDgNVqFumeeOIJXHHFFbj22mvRv39/7N27F1988QXS09Nr/NBvvPFGdO3aFWeffTaaNm2KNWvWBLxcx44d8d1332HYsGG4++670bNnT4wYMQLLli3DrFmzAJifMnz00UdIT0/HhRdeiOHDh6Njx4549913azy+irz88sto0aIFhgwZgssvvxw33XQTmjVr5j1fKYVFixbhwgsvxPXXX48uXbrg6quvxs8//+zt7haMhx56CHfffTcefvhhdO/eHVdddZV3rU5CQgJWrVqFtm3b4vLLL0f37t3xxz/+EUVFRSGtDF1xxRUYNWoUhg0bhqZNm+I///lPrR/fRRddhEaNGmHXrl2YOHGi33n/+Mc/kJ6ejpEjR2LcuHEYOXKkX6WIiIiIIltdVoFEBPpJHUaBAS0hOuOEkuouFokgOTk5SE1NRXZ2drk3oEVFRdi/f793rxQP0QWuX1zmJqYhZIiBgvwCJCQmQFM+TwY7YG1lhbIE/2QcNWoUOnfujJdeeim0g4xihmF4O5d5FvNTaMXSMa7o978+OZ1OLFq0CGPGjCm3Lo1qj8c3vHh8w4vHN7wayvF1nXbB+NWASlVhD0FGjgHXQRdUnIKzxInlecsx6uJRcCQ5wnq/VaksG5TV4NYEKYuCtZU15Iu3DMMAcgBritX/DaSGoANQZmYm1qxZgxUrVuDmm28O7QCJiIiIKCbVaRXIJdCPu9ct2xDywkJdaXAhCHCHEkuIb9NQUDb3l1azJ98f/vAHbNy4EXfffTfGjRsX2gESERERUUyqy45w+kkdRr4BLTW6Z400yBAUqRYsWFDfQyAiIiKiKFKnVaA8gXHSvQ4oyvdhje4IR0RERETUgNVVRzhxCVzHXRAIYA/rXdWJmA9BUdz3gYhqiL/3RETUENRlFUg/rcPINaAlxUZ8iI1HEYCn+0dBQUE9j4SI6prn9z6WuwARERHVWRUo35wGp+JV1E+D84jZNUEWiwVpaWl++7uEMyEbhoGSkhIUFRVFfXvhSMTjG36xcIxFBAUFBTh+/DjS0tJgsYS4AwoREVGEqKsqkOgC1wkXYADKESMJCDEcggCgefPmAOANQuEkIigsLER8fHzYy5ENEY9v+MXSMU5LS/P+/hMREcWiuuoIp2fqMHIMaMnR+QFpRWI6BCml0KJFCzRr1gxOpzOs9+V0OrFq1SpceOGFnIITBjy+4Rcrx9hms7ECREREMc1bBYoLcxWo0D0NzqFibhFNTIcgD4vFEvY3RRaLBS6XC3FxcVH9BjJS8fiGH48xERFRdKiLKpAYZjc4lKBO9h+qazGW6YiIiIiIYledVYGyzPtRSbEXgACGICIiIiKiqOGtAoWxSYEUCfTjurkfUIzOMGcIIiIiIiKKAnVRBRIRuE66ICXhb71dnxiCiIiIiIiiQJ1UgbIFkilQibGzJ1AgDEFERERERBGuTqpAJe5pcFbEfPs0hiAiIiIioggX7iqQiEA/oUMKBSo+hktAbgxBREREREQRrE6qQDkCI9OI+WlwHgxBREREREQRLOxVIKdZBYIC0EC2CmQIIiIiIiKKUOIMbxVIRKCf1GHku6tADQRDEBERERFRhNJzw1wFyhMYpwxoiVqDmAbnwRBERERERBSBwl4Fcrm7wQENZhqcB0MQEREREVEECncVSD+lw8gzoJIaUAnIjSGIiIiIiCjChL0KlCcwThrQEhrWNDgPhiAiIiIioggTziqQuASuEy6IIYA95DcfFRiCiIiIiIgiSLirQHqmDiPHgJbUcKNAw33kREREREQRKKxVoAJzGpyKUw06CTTgh05EREREFFmkOHxVINEFruMuwAUzBDVgDEFERERERBHAyDPgOuKCFIenCmRkGpAcaZDd4Mqy1vcAiIiIiIgaMjHEXKdzygA0QKWo0FeBigT6Sd1shMAyCEMQEREREVF9EacZTowsAypBQdnDsA7IcE+DKzEDFtVzDpw1axZ69+6NlJQUpKSkYODAgfj888/rc0hERERERHXCKDCnv+lZOlRyeAIQAEi2QLLc0+CYgQDUcwhq3bo1nnjiCWzatAnffvstLrroIowbNw4//vhjfQ6LiIiIiChsRASuTBf0X3RIiUBL1aAsYQpAxQL9uHsanCUsdxGV6nU63NixY/3+PXPmTMyaNQvr16/HmWeeWU+jIiIiIiIKD3H5TH9zKGhx4atJiLg3RS0SqFSWgHxFzJogXdfxv//9D/n5+Rg4cGB9D4eIiIiIKKSMQsMMQHnmRqXKGt5gItkCyeQ0uEDqPQRt27YNAwcORFFREZKSkrBgwQL06NEj4GWLi4tRXFzs/XdOTg4AwOl0wul01sl4K+K5//oeR6zi8Q0/HuPw4vENLx7f8OLxDS8e3/CKhOMrIpBcgX5Kh7jMUGIoA9DDeJ8l5jQ40QRKU4ARnvtxicv8v8sFzVm/beeq8zNWIiJhHEuVSkpKcPDgQWRnZ+P999/Hv//9b6xcuTJgEJo+fTpmzJhR7vT58+cjISGhLoZLREREREQRqKCgABMnTkR2djZSUlIqvWy9h6Cyhg8fjk6dOuG1114rd16gSlCbNm1w8uTJKh9ouDmdTixZsgQjRoyAzWar17HEIh7f8OMxDi8e3/Di8Q0vHt/w4vENr/o8vlLiXv+Ta0AlKihb3cxJM3IM6Id1qDgV9nlfzgInvs7/GsOHDIc90R7eO6tCTk4OmjRpElQIqvfpcGUZhuEXdHw5HA44HI5yp9tstoj5oxFJY4lFPL7hx2McXjy+4cXjG148vuHF4xtedX18jVxz/Y+l2AJrutWcklYHxCVwnXJBaeFrue13f8qsp1it1np//lbn/us1BP31r3/F6NGj0bZtW+Tm5mL+/PlYsWIFvvjii/ocFhERERFRjYgh0DN1GKcMQDM3J1Wq7roS6Cd1GPkGtNT6XZ8T6eo1BB0/fhzXXXcdjh49itTUVPTu3RtffPEFRowYUZ/DIiIiIiKqNu/0t2wDKqFuKjF+958nME4a0BI0doOrQr2GoNmzZ9fn3RMRERERhYSR725/XWhASw7f5qcVEZfAddwFgdR5+IpGEbcmiIiIiIgoWoiY09/klEAg0FK0Op3+5qGfNhswcBpccBiCiIiIiIhqQFzu6W+ZBlScghZXPwFE8s1pcCqem6IGiyGIiIiIiKiajEID+gl3E4IkDcpaP+lDdIHrhAswAOVgAgoWQxARERERUZBEBJJjVoBEF2ip9TP9zUPP1GHkmOuQKHgMQUREREREQRBdoJ8yp7/BhnoPHlIoME4YZgWIGahaGIKIiIiIiKogxWJOf8s1oBIVlK1+p56JYXaDgxNQqZwGV10MQUREREREFRARSJ57+luxmJufavUfOiRTINkClVT/Y4lGDEFERERERAGIYba/Nk4ZgAUR035aitzNEOwALPU9mujEEEREREREVIaUuNtfZxtQCSpiNiAVcQegEkClRMaYohFDEBERERGRDyPfMANQodl1TVkiJ2xItkCyBCqRewLVBkMQERERxRwxJCLWbVB0EUOgZ+mQUwKBQEup3/bXZUmxQP9VN9/B8118rfDwERERUcwQMddwSLZAxSmoeLOLl7LXfzcvimziLG1/reIVNEdkrP/xEDG700mRsBtcCDAEERERUUwQEeindRgnDcAKSJ572pCmoKwKsAMqwXxzq+wKsCGiPuWn+mMUGmb76wIDWpJmPl8ijGQLjCyD0+BChCGIiIiIop43AJ0wP8X3XcQuIoDTXOgu+QIDhrnGww7zE/84zfzepiJq7QeFn4hActztr/XIm/7mIU6zCgQNgK2+RxMbGIKIiIgoqonhnsZ0MnAXL6Xcgcd9uogAuvnGUrIEhm62P1Y2VTqFzu7+isCKAIWG6KXT32AHtITImv7mIeLuUldgREyL7ljAEERERERRSwz3G8TT5jShYNb9KKUAK/wCjugCuAAjzzDXE8G9lsihgHhwCl2MkSKB66QLkmtuNhrJYVfyBMYpA1qixmlwIcQQRERERFFJDHOKkHHaqPUbWWVRZjXIoby3DRdgFBtAHiqeQmdXMd2FzlM1g+EOijrMKpqIGQY1mG/M3f9XWoDTIig0iggkz91goESgUiL75ycugX5cN//BaXAhxRBEREREUUd0dwDKrH0ACkRplUyhyxQY4g5FVkTtFDoxygcc0cX83gnAZT5eCMzzDPf3FdHcgUehNAQBZri0uMOR1X05zR0+PJfzDVFlTwtRiPJOmzxlNs6Ihqll+kkdRh6nwYUDQxARERFFFdHNT8eNLAMquW6aGfhNoYsvHQecPlPoVGk77vqcQicigIHS4OKp3vgEHHGWBiBvCAIg8KnwuMMKLDAfg1Zx1UTEHZA8X0bp9+ISSEmZ8wOFKd8QVFGIsrj/XTZEKZ/xljnNQz+uA3kIuG4sEnmnwSVwGlw4MAQRERFR1PBMDzKy6y4AVcQ7hQ5lptAV+Uyhs7pDkG9rbjtqPAXLOz0tUMBx+QQcT7gx3N97aPAPOLbS72sT1LwVoEDnBfkO3ltpChSiPBUp39Mh/retfI6r53ECcIoTAGDkGLCmWSN6+puHuASuEy5z098oCGzRiCGIiIiIooKnTbCerZutjCPszWylU+hOl5lCF+/uRGdXEOWuwhjirdB4Ak656o0L/gHHt6LieePvqZxUUb2JNBWNs0YhyidMeapckb7+x5eeqcPIMaClcBpcuDAEERERUcQTp0D/VYeeG5kBKJBKp9DlGuZGrkrBpbkAAK5DLvMNv1QxPc2zrsZSu+pNrKnoOaHpZpCIhmMlhrtt+wkDKk75Teej0GIIIiIioogmJQLXry5zgXiUBKCKBJxCV+w5EyGbnkbRx9u2O1PMKl4cf/7hxBBEREREEUuKBa7jLki+RH0ACkRppYv0laN+1zhR/RARSLZZ6ZQic98ivkMPPx5iIiIiikhSbFaApMC9nwsrIxRjpNjdACFTzKmTqRU3mKDQYggiIiKiiGMUGeYn44UMQBR7vNWf4+7nOKs/dY6Hu4ESp0A/qUNL0LgBFxERRRSj0B2AihmAKPZISek+V9BY/akvDEENkBSZpVcjx4CUmJ8+cA4yERFFAqPAHYCcYu4DxABEMcJb/TmhwygwoCVqZiMMqhcMQQ2MkWdAP6FDSgRaqgbJE3OudTJfZIiIqH4Z+e4A5BJoyZylQLFDStzhJ9Os/mipGqs/9YwhqIEQERjZBowTBkSJd/MtsQiMHMOsBvHTNiIiqidGnjsAGQxAFDtEBJLjnv7G6k9EYQhqAMQQ6Kd0GKcNKLuCFlf64qLiFIx8A1qhBpXAEERERHXPyHUHIAi0JAYgig3idFd/ThuAYvUn0jAExTjvL2C2AZWooGz+v33KqiCGmBvQJfCFh4iI6paR4w5AmvB1iGKCiEBy3dWffFZ/IhVDUAzzNkDIM6AlaxU2P1DxCpIjkDTxbthGREQUbka2YbYIZgCiGOHpvmucYvUn0jEExaiyDRAqW++j7Ap6gQ49T4e1EZ8SREQUXp51qvpxHbACWjwDEEU3Vn+iD9/xxpiKGiBURTkUJFsgqcJ22UREFDYiAj1Th3HSgLIpqDi+5lB0E5e7+nPSAMDqT7RgCIohlTVAqIqKc0+Jyzc3piMiIgo1bwA6YUA5FJSDrzcU3Yxcs6LpXVttr+8RUbAYgmJEVQ0QqqKUglgFerbOzemIiCjkRAT6aXcFKE5xDSpFNd/qj0BY/YlCDEExINgGCFVRcQqS7948NZG/yUREFBoi7pkKJw2oeAYgim6SJ3D96vJWf/h8jk4MQVGuOg0QqqIsCgKBkete0EdERFRL3qnaJ2s2U4EoUoir9LkswupPtGMIilI1bYBQFRWvILnudtlcrEpERLUghnvK0GkGIIpukueedZPD6k+sYAiKQrVpgFAVZXO3y87XYY3j04OIiGpGdJ8AlKSgrHzTSNFHXKVr2URn9SeW8F1ulKltA4RgaA7NbJedIvzUjoiIqk1092tVJgMQRS/JF7iOm9UfFa+4VCDGMARFkVA1QKiSA5BsgZFvwJJmCc99EBFRTBKXOwBlGWa3Ue49R1FGdJ+1P7p7yQHzT8xhCIoSoWyAUBWlFMQu5rzXFA1K4wsYERFVTVxi7pmSzQBE0Uny3R84Z7P6E+sYgiJcuBogVEXFKUieu112El/EiIiocp7p2nq2zg/QKOqIXrr2By6w+tMAMARFsHA2QKiK0hREmdUglcjNU4mIqGLiFOi/6tBzIyMAiUuAIgA2AHbwNYwqJQU+a38cCiqFz5eGgCEoQonT3VUnq/7aiqp49+apRQIVzz8IRERUnpS4N47Mj4wp1OISuI64INkCWM2upyrR/CBROZQZitiogeCu/mTqME64qz/JrP40JAxBEajOGiBUQVkVDN0wN0+N518FIiLyJ8XmJ+iSL+brVSQEoF9cMLLM108Y5mnGSQO6oUNZ3J3q4gAtydzrxRuMWC1qUKTQDO+SLUAcWP1pgBiCIkxdNkAIhorz2TyVG4MREZGbFLvfRBYIVEr9T5v2C0Ce9RwWdyXIM5vBMGdaIB/Qc3TzNJv7MgkKWrx7E0wHq0WxSgyBcdqAflIHSgCVrAA2wm2QGIIiRH01QKiS3WyXrefpsDbi04WIiACjyID+qw4pjJAA5HQHoGyj8gXtGszKj8NzRQAud7XolAFddCittFqkEhU0h08wYrUoqkmhwHXKXf2xAyqVP8+GjO9qI0B9NkCoilIK4hBIjkBShe1OiYgaOKPQHYCKoywABaJQWgkKUC2SHIEBw1xbZHdXizxri1gtCjsRMYMqYP5ffM8McHrZ893/Fpf5jeuQC1an1ex6y+pPg8cQVM8ioQFCVZTDPSUuXzhnloioATMKDeCU+dqlkqM8AFUkyGoRrOaUcZXgrhY5Yq9aJCKAAUCH+X+X+cEtDLOpgF/oALzBwy+8GPAPJJ7zKvpCgO99/i++dxoo9JQdEwCX4fJ+r1KUGX6pwWMIqkeR0gChKkpTEIu7XXYEvOgREUUzMcwPlYxCw3wz5v7y/m31/In1/FOpcqcF83+FMtcL8rqV/Y3Xf9WhoMymA/UsLAEokEqqRVIg/tUi37VFEVotEhEz1HiCje4ONLr7uekSM+w4zf9D97mOJ+RAzOdX2dv2JBDfs7xP68DP73L/91Xm+auUKn87gS5b9r5FAUVmaGUAIg+GoHoSaQ0QqqLiFYwCA1qhBpUQ2WMlIopEImb40bN0SJ74v2kPMJ3H84Yy0JvNgKoKM8G8+SwbmtxvPD2fpIsRGWtW6ywAVaSyatFpdyc6TZnhqWy1yI6Qd9ETwyfY6P7/FkPMwOYON77VHN8qjSfYiBLzuWOB+RzQ3ON1mN97A3YAQT9X65pR3wOgSMQQVMcitgFCFZRFmWPPMaAlRMeYiYgigYhACgVGlrnlADSzI1XI3wiLd76Qz4lV/L/sae43w943je5/G7p5gpZY/3//6z0ABVLTapHdbMBQNjt4Ky+eqWg+wUZ0MSs3lVVrDP9qjWj+wUZZzOl83qDjE2wiNsgQhRhDUB2K5AYIwVBxCpIn5mJYB/9IEhFVxSg0zA++cswQoRJV2KY+l5tOV9PbCXADmh4Zr1cRGYAqUlm1KNOAfko3f2Y2QLeZ7bpdh11mkHHBDDIVTEMTSGmFRnPfl8W8LW+1xqcSyGBDVB5DUB2JhgYIVVF2Bb1AN9tlO/jUISKqiBQL9Gwdkm1+aq8SVcStDYk2URWAAvGtFqG0WgSXuUYYMLvRiVWqnIbGUENUe3wnWweipQFCMFScKm2XzRd0IiI/UiLQc3RIlsBwGdDiNWj2aHu3HnmkROA6EsUBqCIazDVCVgXkAyop9NMkiSgwhqAwi7YGCFVRDmV+spkv3GSMiMhNXKWVH6PEgBanwZLIjUhCwROAJFtiKwARUb1iCAqTaG2AUBWlFMRmvtiHY2EvEVE0EV1g5BowMg1IkUDFKWgp0f+BV6TwDUAqRTEAEVHIMASFQbQ3QKiKinc3SCgQc9dlIqIGRgyB5An0TB1SIOZ+MKncRy2UGICIKJwYgkIsFhogVEVp5j4CRq77MfJFn4gaCBF3+MnSIfliLnRPYVU81BiAiCjcGIJCzHXcBUuhJeobIFTFWw0qNqd/EBHFMhGz+m1kGjDyDcASnr1+yCcA5TAAEVH4MASFmOQJtEaxPx9cWRUM3TA73sXYdD8iIo9yG52q8O7109D5VYBSVa33PCIiqghDUKhZEfMByMOvXXYMTvsjooZNitzT3nIFYghUAvf6CScpMfcBkhwGICIKP4Ygqjk7zHaw+QYsaWwFS0SxQUp8Njp1meFHs7HiHU5S7DMFjgGIiOoAQxDVmFIKYhfv5nWcG09E0UycPhudlhjQEjRoiQw/4cYARET1gSGIakXFKXOqSL5AJfOVi4iij7jc4SdbzL1+4hWr23WEAYiI6gtDENWK0hTEIjByDKgktssmoughus9eP4UCxHGvn7rEAERE9YkhiGpNxSkY+Qa0Qg0qga9iRBTZvBudZrk3OrUx/NQ1bwDKZQAiovrBEES1pqwKYojZLjuB8+eJKDKJmFN39SwdkidmN0/u9VPnpNjdBS7PvQ8QDz8R1QOGIAoJFe9eG5QmUHa+ohFR5Ci314/G8FNfpMgdgPIZgIiofjEEUUgou4JeoEPP02FtxKcVEUUGo9CAkW3AyDEAcKPT+sQARESRhO9WKWSUQ5ndlVKFbzKIqF5Jsc9eP7qY4YcbndYbBiAiijQMQRQyKk5BcqT0RY6IqI5JicCV7TL3+nEZ0OI1aHauVaxPDEBEFIkYgihklFIQq/npq0pmpyUiqhsiAikRAIDriAsW3QItToMlkXv91DcGICKKVAxBFFIqTkHyBVJgTj8hIgo1MQQoMas+RpEBKRS4Cl3mmQrQUjR+CBMBGICIKJIxBFFIKYuCQGDkGtASOQWFiGpPdHelp8RsdCCFArjM06HBfCWLMy+r4liFjgRSJHAddpkfiDEAEcU0cQkSChPqexjVxhBEIefXLjuOr3xEVD2iC6TYDD5SKJAigTgFMGC2t7YpIB7QLKUftCidf2siBQMQUcMgLoF+SIdxwEAPSw+zSh9FGIIo5JRNQc/XoefrsMbxKUZElROXT+gpcH/vFIiYnSaVTZnd3bivT8RjACKKfaIL9MM6XPtdgNN9mkXMTahT6nds1cF3qBQWWpxmdopLFbalJSIvEfdUtmKBUWwAheb34nKHHqs79DgUNI1TaqMJAxBRbBNDoP/iDj/F5mkqXgGtgU1xmzAqZVT9DrCaGIIoPBww9+fIF6hUvhISNVQiAjjdTQyKDUiBAMVm9QcAYDWrxypeQVMMPdGKAYgodokh0I/qcP3kAorcJ8YB1o5WWFpYzMY0efU6xBphCKKwUEpB7D7tsjmNhahBECnTua3A/W/dHXpsgLIrqAQ2MIgVDEBEsUlEYBwz4NrnMhvSAIDdHX5aWaL+vR1DEIWNilOQPHe77KTo/kUhosDE8Onc5m5XjRJ3pUeDGXrilLm2h6HHS5xmRQxW8ytapw0zABHFHhGBcdwdfvJLP8CydrDC0toCZYmNX3SGIAobpSmIEhg5hrmomW+AiKJehe2qXQJY4A09mpVT2wKRErNCLqfdx9Fi/q2EFYDDrJJpVq00GHlCUgS+6ZBC9z5AhQxARLFARGCccIefvNIpy9b2VljaWKL2w5qKMARRWKl49+apRWIuniOiqCIud9e2arSrpvKkuDT8GMUGlEOZFXLDPVXQBaAIMAwDOnQoKLOS5glJnmmEDuUNR34hqY4/ZPILQMkMQETRTERgnHKHnxyf8NPWCktbi/l3PgYxBDVgIuYTPZwvnsqqYOiGuXlqPN8kEdU1EQEEZmgReL8PdLoYUvq97p7WxnbVtSJFZvgxMg1Isbl3mpaqlYYGzX8qnPJNEwYA3f2zKAaMAgPKMDek9gtJFgXY3SHJboYk34AES2j/zjMAEcUO/bRuhp8sd/jRAEtbC6ztrTEbfjwYghogEYHkCvTTOlSSgrVJeJ8GKs5n81R7bP9CEYWCiJQLLZDAp3vCjBjifdMMHaVBxvDcaOntAD6Bx/d+PW+uFcwvd6WH7aqrT4oEepY7/JQECD/B0FBabUNpQFJQpT9PTyWpADDyfEKS8glJVnclyVEakPyqSNWY4iKFAtcxBiCiaGdkmZUf47T7RUIDLK0tsHawNpj3agxBDYiIAPmA67RZ7hSXQCvSIMkC5QjjE95utsvW83RYG/EpRw2PiNkpDQCMHAO6RS8NMJ7QYriDiTvAmBdGaWVGSqs0frddNriU+VJKeQMNrCgNN2A1JxykwB1+sg1zE8E4VD/8BEPBXINlqSQkeZ5XLnclKceAEuV3/XLrkWwB1iP5PE/0ozosRRYGIKIoZeS4w89Jd/hRgKWVO/zENaxfar4jbSCkQMzw4y53qnizW5ORbUDP1GFtHr6nglIK4jCrT5IqEbnAlyjUPK2ijUJzOqierwMAXMdcpb8DlQUXdxUAGvzCDBuMRB4RsxKjZ+owcgzA5d5AMKEeB6XgfYX3C0ceArOhhVG6HklEoEvg9Ui6zXz+sgkCUXQycg24fnLBOO4TflpYYO1obbBrthmCYpwUifnCnOl+YU4wX9A8VLwyp2ukSVg/AVAO95S4AvcUCqIYJU6zgYCRZ+6RIy4x/9LGmedrqRqbCMQIb3U9y/0Bk+H+G5tY3yMLgoI5Ra5MmvH+u8x6JE+IV0kMQETRxMh3V35+NbynaS00WDtaoSU07NcihqAYJSU+4ae44hdm5VCQLIF+Soe1VRirQZqCWARGtgGVxHbZFFtE9wk++e7Wx1bz90tLNF9klM7nfKwoO7UY4q782Kq+btQosx5JMzSgEAxARFHCKHBXfo76hJ8z3OEnqWGHHw+GoBgjLnM+upx0t2GNU1CplX9ypxIVjCwDki5mWAoTFa9gFBjQCrWw3g9RXRDDbBdtFBhmlbPY3BxUOZQ53ZRBP+aIIZA88wMmyXVPLY6LsfBDRFFNigSun1zQj+jeNaRaUw3WTlZoyQw/vhiCYoToYjYfOKWbbVQd1ehEZANQYH6qaY23hu3Nm7Ioc4F4jtHgS7AUnUTMsOMNPkXuKoBDQSWz0UCsEsPdUTNTh+SZ7cK1BI2voEQUMaRY4Nrvgn7YJ/w0doefVL7nCoR/wqOcGALJMcOP5Atgq1knIpVgTotDGoCkcIzUfT9xynwTURzmjnQRQkRYEYgBUmIGHyPXMDe0dBnmfixJDD6xTPQy4UeZ4YfNXYgoUkiJwHXABf2Q7t0SQaUr2DrZoKWHP/yICL7N/Rars1ZjFEaF/f5CiSEoSom4p2Wcck/L0GA2HKjp893m3v/hpAvWBGvY3tgpu4JeqJvtsh2x+/QTp/tnUyT+rWYtZlc+73x7z/eeLkwUMcRlNvLwNjhwmh8yqDgFi9VS38OjMPKGn1M6pEDMrnyJiuGHiCKGOAWun13QD+pmO3wAKlWZlZ9GWtg/gD1ceBjzj87H/CPzsTt/N5ItyXjI9RAccIT1fkMpdt+Fxqhye/1AzIXXIXhPpiVq5qeduWKuIwoT5VDm2FOlWpv0RQsj34B+0j0tMU6Za0UK4bdRJQDzjZWmSvf78Owc7xuYNFV6XtnvKeS8DQ4KDPN3ocT9AUNcaYMDil3iKhN+LO5uaPzRE1GEEJdAP6jD9bPLbG8P80NwaycrtCbhDT95rjx89OtHmHdkHlaeXglxv6lJ0BLQP6U/skuykYKUsN1/qDEERREpFOin3R3fxJzCFtIQ4Q5S+mndnOYTpk89lUNBsgWSH96wVdfEcE+bOe0Op6mV/zESEe+O755d38VVepp3Kp2gNDD57B3j3QXeqgJXlTzfczpepbwNDgoNM5yXuBe8OxRUChscNATicq+pzHSHH2stK+tERCEmukA/pMN1wGVuxAzzQxprJyu0puELP4YYWHV6FeYdmYeFvy5Evp7vPe/C9AsxqdUk/CbpN/i26Fs0S2gWljGEC0NQFJBin3bXzvJ7/YSSSnTv55MrUGlhCkFKQWwCPUePmcXkUuxuSpFtQCUoaPaq3z0ppbw7vgPl9+vwu31DSitJnsDkNKuCMACBlF5f87ltz67wNuXdBd4vIPlUl0SkgnuPPSLuvU8K3Gs9itzH1wGu82lAxGm27TdOGzAK3eu8uBEoEUUQ0QX6Lzpc+11AiXmaSnCHnzPCF3525+/GvCPz8J8j/8HhosPe0zsldMKklpNwTctr0C6+HQDAme8MyxjCjSEogonTHX5OG5BidzeicO9ArgGwAvpJdzUoTNPVVLy7QUKhQCVG7zsO79qsk7rZ7CElPG+gvcHF8+9gApOnouR0Vzc8p/nSSm/bJWZd3XXcBS3evfjbCjMsWVRMrFuSEvd0t1zD/L9uQNmUWVXleo8GQ0oEerZZtTWKqtlNk4ioDojhE36KzdNUvDL3+WmuheX1+HTJabx/7H3MOzIPG7M3ek9Ptabid81/h0ktJ2FA2oCYmSHBEBSBxCUwsgwYpwzzBTqubl+gVYKCkWNAsgWqcZhCkKYgymyXrRKic8qR6O7qT6ZhVlkiZOpUsIFJRLyVJU9YAgAjx4Ceq5de0Hc9kqeqZPdp8mBxT8nzrFeKgGPgS1xlNjJlg4MGS4p9wk8xww8RRR4xBPpRHfpPZnMlAIADsHa0wtLSEvLw4zSc+PLkl5h3ZB4WHV+EEjHLTRZlwYgmI/D7lr/HmKZjEGeJC+n9RgKGoAgiemm7ayO/Hl+glbuL2yndfGNvC1MQ8m2XHRdd70KMQrP5geSZlaxwHaNwUso97ccdmDSL+Y2WrHm/B3wqS7q7qlQspWuWoErXK3lCkCckWX1CksUnRNVBNUkMd/DJL9PgwKGiNnRTzUmRGX6MTMPbnp/hh4giibgE+mEdroOllR/YAWsHKyytLCGdrSAi2JK7BfN+mYf3jr2HEyUnvOf1Su6F37f8PSa0mIDmjuYhu89IFHQISk9PD+qNw+nTp2s1oIbIuxHfaXe7a2vN9voJJRWnYGQb0LN0WJuGJysrmzL3XskzoMVFxwpkEfcaglMGxBW+6W+RxFtZsgauKomI2Z7TKA0fnrVK5g2UaepgNX/2yq78w5FvYKoBEXeDA89GpsVscNDQSZFAz3KHHyfDDxFFHikSuA65Nzl1d3uDHbC2s8LSJrTh52jxUbx75F3MOzIPP+b96D29mb0Zrm5xNSa2nIjeKb1Ddn+RLuh3t88//3wYh9Ewedtdn3SZ4UdFUEciZb55NE4ZkJTwbWyq4nzaZUd4NcWz94+RZQAOQEuJhB9U/VNKef+SVBiSPOuRdPe6nCLx7msAQbkmDcpe2vkuYDXJHWhEBCgxK3NGjlHa4MDOBgcNmRS611Nmm81kEAdo8Qw/RBQ5jDwD+s869KO6d/sMlahgaWeBpUXopr0V6oX49PineOfIO1h2chkM9yeUDs2BS5tdioktJ2JE4xGwag1vcljQj3jy5MnhHEeDI/kCV6YLkiUQCd1eP6HkrQZl6rA2D9Mvhx2QbHPakiUtwg6AD9+9f7QkLSb3NwoXvy54tgqCku+UO91snw7dZ8odyrQAt7k7JDphbmTqEjP4sMFBg+XpbqgfcVfUXeYi4rA3kyEiqgYjy4DrgAvGidJORSpNwdoudK2uRQTrstbhnV/ewYe/fogcV473vPPSzsOklpNwefPLkW5Lr/V9RbOg39lmZmbinXfeweTJk5GS4r8RUnZ2Nt56662A55E/KfKpJujuN22RWgFR5psII9OApIVn3Y5SCmI3p5hpKeHpdlIb1d37h2omqCl37pAEw6z8IB+lG5laWZVraETEDMFFZoc3PccsLRqnDVgSLUBiPQ+QiMhNRGCcMMOPZJduR6E11WBtb4WWFprXsAMFBzDvyDzMPzIf+wv3e09vG9cWE1tOxMSWE9E5sXNI7isWBB2CXnrpJWzduhW33357ufNSU1Px9ddfIycnBw8++GBIBxgr/Pb6KTFLnuHa6yeUlENBsszgZm0VprVBce69ifLFnA4YIWqy9w+FR3X2VKLY5Jn6KMVm6PE0VfFsGigW9xqwlAiZUkxEDZ4YAv2IDv1n90bMAKAAS0sLLO0s5iygWspx5eDDYx9i3pF5WJO5xnt6kiUJv23+W0xqOQnnp58PTfEPY1lBv6v94IMP8Oyzz1Z4/p/+9CdMmzaNIagMcboX5p5ydyWKV2HbhDRcVKKCkeWuBoVhTx+lKYjF3S47qf4XsIu4G1WcCu/eP0RUMW/ocVd6JN8/9MDmnhYZD7NqbSizOkhEVM/E6dPpzb3BKayApbUF1rbWWq+z1kXHV6e+wrxf5uHj4x+jyCgCYH5AeFHjizCx5URc1uwyJFpZEq9M0CFo3759yMjIqPD8jIwM7Nu3r1p3/vjjj+PDDz/Ezp07ER8fj0GDBuHJJ59E165dq3U7kUhcAsl2VxIKo3w/ChuAAsCV6YI1wRqWkKLiFIx8A1qRZs7jryfiMrv0RdreP0Sxrlzo8bQ2ryD0EBFFGikSuH52Qf9FL23+43B3emtlqfV64u152zHvl3n4z9H/4FjxMe/pXRO7YlLLSbi65dVoHde6VvfRkAQdgiwWC44cOYK2bdsGPP/IkSPQtOqV2lauXInbbrsN55xzDlwuFx544AFccskl2L59OxITozS9GuaiN88iemWL4vDjQyWY0+KQBiApDLdvVRBDYOQaZhenehALe/8QRQsRAYrLTG9j6CGiKGTkuZsdHDP8Or1Z21uhNa/deucTJSfwv6P/wztH3sHmnM3e0xvZGmFC8wmY1GoSzko5ix/Y1kDQIahfv35YuHAhzjvvvIDnL1iwAP369avWnS9evNjv33PnzkWzZs2wadMmXHjhhdW6rUihH9WhXGbLYC0l+sOPl81sO+s66a4GhWF6mIp3rw1KE7NFch3x7v1z0oDonP5GFA7e0FO20uPZF4Ohh4iiiIhAMs3Kj3GytNOblq6Z632a1LyRUrFRjM9PfI55v8zDFye/gEvMP5RWZcWopqMwqeUkjG46GnbNHpLH0lAFHYKmTp2Kq6++Gq1bt8Ytt9wCi8VcoazrOl555RU899xzmD9/fq0Gk52dDQBo1KhRwPOLi4tRXFzs/XdOjtnyz+l0wul0BrxOXfHcf0leCVRj98JcgfcTgZgQD+g5Zmc7LTUM1RoLYOQacGW5YE33f2p6jm+of87idE9/y3bv/ZOomT8zvcqrxhyX7vL7P4VWQzu+vqFHis3GJ97Qo+DdNBdx8A89Nfy76TJcfv+n0OLxDS8e3/AK5fEVEcgJgfGzAZR2noZqpqC11aBSFQQCXfRq/S07UHgAX536CstOLcPK0yv92lr3S+mHa1pcgwnNJ6CxvXG5x1XfPCHN5XJBc9ZvA4bqvE9U4tlcIQgPPvggHn/8cSQnJ6Njx44AgJ9++gl5eXm455578MQTT1R/tG6GYeCyyy5DVlYWVq9eHfAy06dPx4wZM8qdPn/+fCQkcDMIIiIiIgo9zdBwRuYZaH2iNeJL4gEAutLxa/qvONz0MIocRdW6vUK9ED/k/YDvc7/H5tzNOFJ8xO/8RrZGGJI+BMPSh6FtfOClKFReQUEBJk6ciOzs7Cq37alWCAKAb775BvPmzcPevXshIujSpQsmTpyIc889t1aDvuWWW/D5559j9erVaN068KKuQJWgNm3a4OTJk/W+P5HT6cSSJUtwvjof9iYxXJ40AMkTWFpbQtbX3peIQHIE1hZWaMmlt+85viNGjIDNVrve4mKYHfsk09z7RyWy+QFgVii+2vwVLup7EayWhrdzdLjF2vENWOkpNje5hYI5vc3qbm1eB79eLsOFVadW4cLGFzbInc/Djcc3vHh8w6s2x1ecAjksMA4ZpWsWrYBqraC10YKevm+IgW2527Ds1DIsO7UM67PWwymlVQuLsuDc1HNxceOLcXHji9E3pS8sKnI3kfflLHDi6/yvMXzIcNgT6/c9cE5ODpo0aRJUCKr2b9q5555b68BT1tSpU/Hpp59i1apVFQYgAHA4HHA4HOVOt9lstX5jHCpWZY3tP2AaIDaBylSwplpr3ekkEMNhQOUrWNPLd6Kr7c/as/cPst0b1dbh2qNoYbVYYbNExu9TLIrW4yuGf/c25Jm/T0o3f4c0u2ZuqGxFva7psWox/je4nvH4hhePb3hV5/gahQb0n3XoR3w6vcW5O721DK7T26/Fv5qh56QZfI6XHPc7v318ewxvMhzDGw/HkEZDkGpLre5DigiizHqK1Wqt9/fj1bn/Wv2m9erVC4sWLUKbNm1qdH0Rwe23344FCxZgxYoV6NChQ22GQ3VEJSizkUC2AUvj0H9KoeKU+alyQej2JeLeP0TBE0MAA4DT3b2tsLSRgejuTUntygw9FrCSSkQxw8h1d3r71afTW5K709sZlXd6KzaKsS5zHZadWoYlJ5dga+5Wv/MTLYkY0miIN/h0SujEv5/1qFYh6MCBA7VaqH7bbbdh/vz5+Oijj5CcnIxjx8ye56mpqYiPj6/N0CicFKAcCsYpA1qKFvJW0spiLio0co2Q7KbMvX+ooRFxT0vzNPlw/9/3dE+YEZd5mujupgWGz2Vd7tM1s4mBilNQFsXubUQUU0QExmmz8mOc8un01kiDpb0FWqPAnd5EBHsL9mLpyaVYcmoJvj79NfJ1/12b+yT3wYgmIzC8yXCcl3YeO7pFkHqtuc6aNQsAMHToUL/T58yZgylTptT9gChoKs6sBulZOqxNQ/808rbLTpda7azMvX8oGom4KzFlQow3vPiEGtHdIcblvo4nyBju2xGU3pb7U02BQLmTjCgxX9w1mOHG/aUsqnRdDxFRDBJDYBw3Kz+SW7pEXjtDMys/KeU/iM12ZmP56eVYenIplp5cioNFB/3Ob2Zv5q30XNT4IjRzNAv746CaqdW71wsuuKBWFZtq9mSgSOJTDZKU2gWVgDdvU9Dzdeh5OqyO6j9N/fb+MTj9jeqflJh/74wcA4YyzJBiSGmwcZVWZaDDPB/iDTTeVqtSJsTAXakpE2SUxV3xVPAPOID3umW/JyJqCEQX6L/o0H/WIUXuP64aYGllMff48dm0XRcdm7I3YenJpVh2ahm+yf7GbH/tZld2DEof5A0+PZN7QlP12yaaglOrELRo0aJQjYOikLcalKnD2jz01SAtToPkCCS1emFZnObaHyPLgHKokEypI6oJ0c2uaUauAVeOuY+CflD3CyMCMUOLJ8R4nq4aSoO77+mKIYaIqCakROD8xQn9kF7a6c0GWNtYYWlj8TZLOlx0GMtOLsPSU0ux/NRynHae9rudLoldMLzxcFzc5GJcmH4hEq2JdfxIKBRq/M41NzcXjz76KFasWAFd1zF48GA88sgjaNKkSSjHR5FMmdPWjEwDkibmIulQcsAMQfkCBLkNlJFvTn8zCgxoSRqn8lCdExGgENDzdEi2lH7K6J4GrlIUNK00mDPEEBGFj4j5PqLTL52g/6CblXWY718s7SywtLSgCEVYnlk6xW1H/g6/20i1pmJoo6FmtafJcLSLb1cPj4RCrcYh6MYbb0R8fDxmzJgBp9OJf/3rX5g0aRK++OKLUI6PIpxyKEi2WXmxtgptNUgpBbEJ9GwdEld5NUgMgZ6pQ06be/9oqYEXMRKFi2evHD1bN4O7AcBudhWCBiiDz0cionASMTvLSo5ZgZdcgZFjAC6gFVoBAFSyGX52Ju7EstPLsPT7pViduRrFRuk+lBo0nJV6FoY3GY4RjUfg7NSz2bo8BgX9E33uuedw5513et9Ybty4Ebt374bFYrZI7tq1K84777zwjJIimkpQMLLc1aAQtbT23nacMlvzFlYcgjx7/xjZBlSCgmbn9DeqG+JyB59c3WzkUSJQVgUVr+q57QxRw2ZkG+aHD4lcDxqrxHBv0uwOOp7QAz3AhRWwL2EftjbZihXFK7Bs1zIcKT7id5FWca0wvPFwjGgyAkMbDUUje6O6eSBUb4J+md63bx8GDBiA1157Df369cOIESPwm9/8BuPHj4fT6cTbb7+NkSNHhnOsFKlsAAoB12kXrAnlNzitDaUpiDI/0SmLe/9QfRBDgAJAz3dPdyt2Vx/jNDP88ClIVK9cB1xw7THX4EEzq7FaigaV7P5/El8roo0YAskzw44n9EieeKe2+dHMas+JxBPYoDZgvWs91uStwZbcLX4d4OK0OFzQ6AIMb2xOceuW2I0zSBqYoEPQSy+9hPXr1+MPf/gDhg0bhscffxzvvPMOlixZAl3XMWHCBEydOjWcY6UIpuLNaXFIB5AUhtvO968Eld37R0tl9YfCS4oERp5hvvgW+Ex3S+a+U0SRwnXYJwBZYLaPzxHoOT7lAeUTjFIYjCKN6O7qTq5ROq0tT0o7ZPqyuP8GJyvsc+zDemM91hWsw7qsddiXua/cxXsk9TD37Gk8HIPTByPOEhf+B0QRq1oTNs477zxs3LgRTz75JAYOHIinn34aH3zwQbjGRtHEBkihwHXSXQ0K4YuJsirvxo4A9/6huiMu89NHPcdc5yMlYm4amqDMN1hEFDH0YzpcO8wAZGlvgbWzFVLoXh+S478+RHLNaaz4xX1lVUHFyMLXl3ASZ2ng8YSesh96etkALdkMrq5EF7ZqW7Gu0Aw864+ux4mSE34XV1DomdQTA9MHYkDqAMAAJrSawLU95FXtZ4LVasWDDz6IK6+8EjfffDPefPNNvPTSS2jevHk4xkdRREvUzHURuQKVGvq1QQCgZ+pADrj3D4WNd7pbns90N43T3YgimX5Sh/MHs+expZUZgJRyf2CRAFiam59aiJhrTL1TqnICBCMPTzBKLlMxYjCqESkpU93JqWS9rx1+gTQvPg8bijZgXdY6rMtch40HNqLQKPS7ikNz4JzUczAwfSAGpQ3CgLQBSLOlAQBchgtfnfgqzI+Qok3QIWjLli244YYbsHPnTvTu3RtvvPEGli1bhjlz5mDQoEG45557cMstt4RzrBTpLAAUoJ/WQ/9C4W4vrJ/QYUmwcO8fCikRAYphTnfLMsy21gYAh9nSmtPdiCKXkWnAucUJCKCdocHaveK1qX7B6AyfYFQUoGLk9AlGnjX0ymy24FcxSmYwKkuK/Y+lkWsARRVcOM6s8Pge0yNyBOsy12Ft1lqs3bsWP+T+AKPMAqBGtkYYmDYQg9IHYVD6IPRN6QuH5gj/g6OYEXQI+sMf/oAhQ4bg7bffxuLFi3HzzTdj+fLluP7663HppZfiL3/5C9566y2sW7cunOOlCKcSlVkNyhGo9BBOiXO/oKkk5d3MjKi2xOnf1lqcAmXndDeiaGHkGijZXAIYgNZYg62nrdofWijl7ugY7x+MUGTevl/FyAlzimyefwsylaS8b+AbUjAqd5w8AbIk8OVVvDKrap7qWrIGsQl25u/E2sy1WHd0HdbuWIufC38ud90O8R0wKH2QN/h0SewCTfEDUaq5oEPQ7t278e6776Jz587IyMjA888/7z2vadOmeOedd/Dll1+GY4wUTTQAVkA/pZsvAiHerLQhvKhQeIlu7iPhnY5R4jPdLcQt3okofIx8AyWbSgAXoNIUbH1sIZsirZQ7FMVbYGnmE4yKYVY2yrzhlzx397KjpdUKlVj6ht8bjCJ0A28x3K2ldfNvJHSYUwTd33tP83zvAiTf3bnVGfg2/R5/svvx2xSKjWJszN6ItZlrsfbntVifuR6Zrky/62rQ0DulNwalmVWe89LOQ8u4luE+DNTABB2Chg4diptuuglXX301vvrqKwwePLjcZS655JKQDo6ik0pQMLINGNkGLI35cTrVP8+nld51PpzuRhTVpEhQ8l0J4DS7g9n72sP+IZlSCogDLHEBglHZilGJGRIkP0Aw8lSM3NWQ6gSjgGFFN5u4BAwrZcKM7+m+5wfsvBb0gSmzdirZvxKW6czE+qz1WLd/HdZmrsWmnE1+G5MCQIIlAeemnutdz3Nu2rlItibXYlBEVQs6BL311luYOXMmPvroI/Tp0wf3339/OMdF0UwByqFgnDLMT7/YvY3qiZSUme7mck93S1Rm1ZKIoo6UiFkBKjI/dLP3t9fb64xfMGpa+qGfd02MuwmAkWMAxT7B6JhPMEpQQDKQ4cyA/qsOQzcChxUdgffFCekDgjkV2OKeeWEBYC393nuaxZzapiWXbxZxsPAg1v66Fmuz1mJd5jr8mPdjubtpZm/mndY2MH0g+iT3gU2zhfnBEfkLOgSlp6fjmWeeCedYKIaoOLMapGfpsDZlO8pIJ2JOCwPc62REzBdDDVFXJRFdvNM0jFzDbGutKag4BZUUXY+FiPyJ06wASYEAcTADUASuE1UOZYaipqWnVdQsQArMjpQt0ALi/q/qO0D5sGIxt5Qod5pFme/2tNLzfS/jPd+Cak8n1EXHj3k/mut53I0MDhcdLne5jIQMs8qTPgiD0gahU0KnqHttodhTq3en+/fvx969e9GiRQv07NkzVGOiWKDcQeiUAUkRKAf/2NU3EXMeN5zuoOMyGwFIkQAlgMtl7q/h+slVGn7cQcj7oqmZL5ZKc1dSlPt7T2Dy+d7v/77nh+uxFZaZ7gYADrPNKttaE0U/0QUlm0sguQLY3AEoPnp+uQMGoxIzEOk5On7K+wkdUzpCs2r+waRsFcYK829vPYSIEyUn8H329/gu5zusz1qPDVkbkO3K9ruMVVnRN6UvBqWZVZ7z0s7DGY4z6nysRFUJOgTdeuuteOqpp5CUlITCwkJce+21WLBgAUQESikMGTIEH3/8MZKSksI5XooiyqHMP+6ZOqzNWQ2qC35BxyWAEzCcBqTYDDqeeeOeKRWixHxh1eCdTqJsCgrKvC3DfVmn+X8Rd5VIzI3oAJifWqrSf5s3Ar8QpOAOTb5fnhd1zf1/5ROugNLQVCZI+QUs97x7z3Q36DDX+SRxuhtRLBFD4NzqhGQJYDUDUCxslaDsCpYmFkgjwcETB9G5aeeI2czzdMlpfJ9jBp7vc77Hd9nf4WDRwXKXS7Ik4by087zrec5OPRuJ1sR6GDFR9QT9m/baa69h+vTpSEpKwt/+9jds2LABS5cuxYABA/D9999j8uTJmDlzJh5//PFwjpeiiacalGlAUiWqPrGLZN6g4zIrOtUOOnHwBgS/4OKZa+6ZNlFB+aTs6X7/Fp//G6X/FsMMTt4FuO7zDRhmoJLSTUgFYt6moDT8AH5hyFOlEt3d1tribnEbGe8diCiERATOH50wThqABtj72s0KL4VMljMLm3M2+wWe/YX7A142IyED/VP745zUczAofRB6JvWMmOBGVB1BP2tFSueofvLJJ3jqqacwbNgwAMDgwYPxj3/8A/fccw9DEPlRDgXJFuindVhaWjgHOEjlgo7LJ+gUl7YoNTOE+bvpnTLhCTqewFCXc8GUz/8135ODC1TlTpPAX54qlbK6ww+fVkQxSUTg2ukyGwkowNbbBi2dAag2clw52JKzBd/lfIfvss3Qs7dgb8DLdozviP6p/dE/pT/6p/ZHn+Q+SLWl1vGIicKjWtHd8wb22LFj6N27t995ffr0waFDh0I3MooZKkHByDJgSbMArJB7ibgrNs4aBB2rAuyIysYF1eJbCfI7OYYfMxF5ufa6oB82Nya19bT5dWCjquW58rA1dyu+y/7OW+XZnb87YPOFdvHtzLDjDjx9U/oi3ZZeD6MmqhvVCkEPPfQQEhISoGkajhw5gjPPPNN73qlTp5CYyHe4FIANQCHgOu2CNcEa22/ay/ALOhWt0XFPEfNUW/0WwTaEoENEFIDrgAv6ATMAWbtbYWnOAFSZAr0A23K3+QWenXk7zWnHZbSJa4N+Kf28VZ5+Kf3Q2N64HkZNVH+CDkEXXnghdu3aBQDo0aMHfv75Z7/zFy1a5BeKiHypBHNanKQJVHLsv6EXlzkFUHKkNOgEqugw6BARleM67IJrj9mx0trZCmtrrjnxVaQXmYEnxx14sr/Hjvwd0EUvd9mWjpblAk8zR7N6GDVRZAn6r8qKFSsqPX/ixImYMmVKLYdDMctqVjr0U7q5Y3aYWiVHAskTuE64YOQYUA7FoENEVA36MR2uHWYAsrS3wNqhYQegYqMYP+b+6F3D813Od9ietx0ucZW7bDN7s9I1PCn90S+1H1o4WtTDqIkiX8j+snTs2DFUN0UxSkvUILkCyRWo1NgLA6Kb1R/jhAHRxexexPW7RERB00/qcP7gBABYWllg7dywApDTcOLHPP/A80PuD3CKs9xlm9iaoF9qP+8anv4p/dHS0ZIfthEFqVp/XQoLC/Gf//wHq1evxtGjR6FpGjp27Ijx48fj4osvDtcYKVZYACiY1aAkd4UkRkihwPVrafUnFvavICKqS0amAecWJyCAdoYGa/fYXkNaoBfgh9wfsC13GzbnbMaq06twsOggio3icpdtZGuEfin9vNPazko5C63jWsf08SEKt6BD0N69ezF8+HAUFhbC4XDg8OHDGDNmDDZu3IhZs2bh8ssvx/z582G1NqxPbah6VKIyq0E5ApUe/X+8xRAYmQb0EzpQAmjJrP4QEVWXkWugZHMJYABaYw22nraYeYMvIjhafBRbc7diW+42bMvdhq25W7E3f2/ApgWp1lQz7PhUeNrFt4uZ40EUKYJOLHfccQdGjRqFWbNmQSmFJ598EitXrsT69euxZ88eXHLJJXjssccwffr0MA6Xop4GwOquBiUrs9VzlJIigeu4y9zB3I6YnOJHRBRuRr6Bkk0lgAtQaQq2PraoXTfqNJzYlb/LG3S25W7D1pytOOk8GfDyzezN0Du5N85MOhNWWPH71r9HRmIGNMVP04jCLegQtHLlSmzevNn7ScRf/vIXPPTQQzh16hQyMjLw/PPP484772QIoiqpBAUj2zD3DmoSfS1PRcTcAPZXHVIsUElmpzciIqoeKRKUfFcCOAGVrGDva4+aqdKZzkxvZWdLzhZsy92GHXk7UCIl5S6rQUOXxC7ondwbvZJ7oXeK+f/mjuYAAJfhwlcnvkKnhE4MQER1JOgQlJaWhtzcXO+/CwoK4HK5YLfbAQC9e/fG0aNHQz9Cij0KUA4F45QBLVWDskXHCx4ASIlAP67DyDQAK6BSVMDNPImIqHJSImYFqMj8cMze3x6RrweGGDhQeABbc7aWVndyt+JQUeAN4pMtyX5Bp1dyL5yZdCbiLfF1PHIiqkzQIWjEiBG466678Oqrr8LhcOCvf/0r+vbti+TkZADAwYMH0awZ+85TcFScWQ3SM3VYm0X+OjIRcx2TflyHUWCYjQ9s9T0qIqLoJE6zAiQFAsTBDED2+g9ABXoBfsz90W8627bcbcjT8wJevl18O2/Q6Z3cG72Te6NdfDtWc4iiQNDvPp966imMGzcOPXr0gFIKbdq0wYIFC7znnzhxAvfcc09YBkkxSLmD0GkDkipQjvp/8auIuAT6CR3GKQNQgJaqsfpDRFRDogtKNpdAcgWwuQNQfN3+URURHCs5hq05W/0Cz578PQGbFTg0B7ondfcGnd7JvdEzuSfSbGl1Om4iCp2gQ1CzZs2wbt067NmzB8XFxejWrZtfJ7jf/e53YRkgxS7l8KkGNY/MapDkCVzHXDDyWf0hIqotMQTOrU6zoYzVDEDh3lLAaTixO393ue5sJ0pOBLx8U3tTb9DxVHm6JHaBTeMLAFEsqfY7z4yMjHCMgxoiBah4BSPTXQ2q408CKyMugX5Kh3HSgIiw+kNEVEsiAuePThgnDUAD7H3t5qbSIbz94yXHsT1vO7bnbfeGnu252yttVuCZytYruRd6pfRCc3tztqMmagAi8+N3ajCUQ5md1k7rsLS0RMQLj+Sbra+NHANaghYR89SJiKKZiMC10wXjmDmt2NbbBi295gEo05mJHXk78GPej9iea4aeHXk7KmxF7WlW4F2/k9IbPZJ6IMGSUOMxEFF0YwiieqcSlNkuO80CJNbfOEQ3w5hx0gB0mJ9Qcm0rEVGtufa6oB/WAQC2njZYmga3r0CeKw878nZ4Q872vO34Me9HHC0O3I1WQaFTQid0T+qOnsk90Su5F/ok92GzAiIqhyGI6p8NQCHgOuWCNcFaL9UgKRS4fnVBcgRwmMGMiCjUvPuMndShJWjQmmlRvWl0MFwHXNAPmAHI2t0KS/PyAahIL8Lu/N3eqWw/5v2IHXk7cKDwQIW32yauDXok9cCZSWeie1J39Ejuga6JXVndIaKgMARRRFAJCpIjkDyBSq67NwRiCCTLnP6GEnDjUyIKCykS6Ed16Ed0sy00AB06sBPQmmmwtLRAS9ciYkpwKLkOu+Da4wIAWDtbgZbAzryd3rDj+dqbvzdgVzYAaGZvhjOTzkSPpB7okdwDPZJ6oFtiN6TaUuvyoRBRjAkqBG3dujXoG+zdu3eNB0MNmBUQmM0IVKKC0sL/RkCKBK4TLkimAHZufEpEoSW6wDhhQD/ibrHvYQG0JhokVyAFAuOoAeOoARWnoLXUYGlhgZYQ3VO3DDHw08GfsG3XNuzADuxM2IldR3Zh155dAZsUAEC6NR09knuYVR2fCk8Te5M6Hj0RNQRBhaC+fftCKQURqfJTKl3XQzIwani0BPebghyBSgtfGvFOR/lVhxSJWf1hTZSIQsC7sfIRHfoxHXCVnqfSFCwtLbCcYYGyqtK/RUd0798j/Scd+k96uctGKhHBL8W/YHvuduzI34Efc3/E9rzt2Jm3EwVGQekF80u/TbQklgs6PZJ6oIWjRcxVwogocgX11m///v3e77///ntMmzYN99xzDwYOHAgAWLduHZ599lk89dRT4RklNQwWAArQT+tQyQrKEvoXQylxb3yaaQAWQKWy+kNEtSfFAv2Ye7pbnpSeEQdYWljM6W5lqjtKKag0BS1Ng7WrFcZxA/pRs2okWQJXlguunS5oZ0TGdLlsVzZWnV6FXfm7vGt2tudtR7YrO+DlHXCgi7ULejTt4Z3OdmbymWgT14ZNCoio3gUVgtq1a+f9fsKECXjxxRcxZswY72m9e/dGmzZt8NBDD2H8+PEhHyQ1HCpRlVaD0kP3Yi8ikFyBflznxqdEFBJiCIyT7uluJw3Ak300n3U+jYILLsqizLDUwlJu/ZBnulxlgSpkj0kEJ0pOYGf+Tm9Fx7OGp6L20xZlQUZCBnok9UB3e3dkHM1Ad707OjXuhPh+8XUyvZmIqLqqPQlo27Zt6NChQ7nTO3TogO3bt4dkUNSAaQCsMNcGJauQTAMRl9mJyThp7k/BjU+JqDaMXDP46Ed1wFl6ukp1B5nmFihbzf/IqDgFawcrLO0tftPlUATo+3Xo+2s/XU5E8GvJr9iRtwM783ZiR/4O7/ennKcCjwsK7ePbexsUeKo7GYkZcGgOGPkGSjaWAAag0hXsfe0MQEQUsaodgrp3747HH38c//73v2G32wEAJSUlePzxx9G9e/eQD5AaHpWgYGQb5t5BTWrXqk3yzNbXRh6rP0RUc+L0qc7k+kx3s/tUZ5JCW50pN13Op8lCuelyLQJXnUQER4uPmgEnfyd25JWGnUxXZuD7dYed7knd0T2pO7oldkNGQgaOFR7DmOZjYNXKv3WQIkHJdyWAE1DJ7gAUhinNREShUu0Q9Oqrr2Ls2LFo3bq1txPc1q1boZTCJ598EvIBUgOkAOVQME4Z0FK1Gn2iKi73xqcnDIgIqz9EVG0iAuOUO3gc95nupgCtqXu6W2OtTqodyqJgaW5WmbzT5Y7qkHxzupx+RMcvjl+wJ30P9sTtwY4SM/TszNtZ4ZodDRo6JnREt6Ru6JbYzRt6uiR2KbfXjstw4auSrwLejpQISjaVAEXmh1j2/vZaVcKIiOpCtUPQueeei59++gnz5s3Dzp07AQBXXXUVJk6ciMTExJAPkBomFWdWg/RMHdZm1XuaSoG574+RbUDFK2gOLsAlouAZ+T7T3YpLT1dJCpZW7ulu9vp5ky8iOCSHsCNpB3a02IEdmTuwI3sHdpbsRF5RHnC0/HUsyoJOCZ3QLbEbuiV18+6zk5GYgXhLfO3G4zQrQFIgQBzMAFRPx4aIqDpq1Bg4MTERN910U6jHQlRKuYPQaQOSIkFNYxNdoGea1R+4AC1FM9cYERFVQVw+3d2yfaa72WBWYFpZoCXX3R8UQwwcLDyIHfnuNTvuaWy78nchT88LeB0LLOhs6Ywuehd0RVd0U93QTeuGjGYZSGiVEHSThmCJLijZXGJOD7S5A1A8AxARRYcahaA9e/Zg+fLlOH78OAzDf4fnhx9+OCQDI1IOdzUoSweaVn5ZKTLX/ki2AA73xqdERJUQERiZ7uluvxqAz8uZ1sQ93a1peKe76aLj58KfyzUo2JW/CwV6QcDr2JQNGYkZ/pWdpG7onNAZds1uTpfzBLp8AX4FnL86AYfP+qXE2gU6MQTOrU5IlgBWMwDV9jaJiOpStUPQ66+/jltuuQVNmjRB8+bN/T5VUkoxBFHoKHMDVSPTAJICX0QMMRcIH3cBJeZ0FdSulwIRxTij0B18jhiQotKqj0p0d1xrYYFyhDb4OA0nfir4ybtOZ1f+LuzI24Hd+btRaBQGvI5d2dElsYt3zY4n7HRK6ASbVnF5XMUpWNtbYWln8d+4tRjQD+jQD+hmJztPd7lqrt8RETh/cJodNzXA3tduVt6JiKJItUPQY489hpkzZ+K+++4Lx3iI/NkBFMIMQmVIscB1wgXJNKdiqBRufEpEgYkuMH51hx/fvydWwHKGOd1NpahaTxcr1AuxO3+3N+Tsyt+FXXm7sLdgL5ziDHgdh+ZAl8Qu6J7YHd2SShsUdIzvGLATW7CUUlCpClpqgO5y2QJXtguuXS7/Jg9VPH4RgWuXy6ycKcDW2wYtnQGIiKJPtf+6ZmZmYsKECeEYC1FAKkHByCp90yIi5t4Zx3VIoZjVn5q/TyCiGOX9W/GLe58dvfQ8rZH7jX8zrUatnLOd2diVv8tb2dmZvxO78nbhQOEBCCTgdRIsCeia2BXdEruha1JXM+wkdkf7+Pa1CjvBUJoyw94ZFkixT7vvfDMc/n979x4eaVXnCfz7O+9bl1RVrt1JOn2nL+nmjoAgF5FxYFhZhbnt6MIqMrv6zIjPjLLsqMsogpfxmXFQd727Cvu4DrCuKAw6iqKCLaJctel7N62A9P2Se1L1vue3f5y3Kkkn3Z10py5v1ffDkyckqXROTiqV91vnd37H7rHj5XI9R2/3bXdY6Mvu+0uckYDXyaV3IoqnWT/q/of/8B/w8MMP46/+6q/KMR6iqRIAotJ4zUetrw+5Mgxp5eoPEU1WaiH9Sui6lkWkaUK52ww28Ksq9ub3urAzIehsHtqMXWPTtGGLtPvtWJtbizVZF3TWZNdgbW4tFqcXw0j1V00kNaFcbuAY5XJHHPy6eO9i6G43n/6pPrwFDEBEFF+zDkGrVq3CBz/4QTzxxBM488wzkUhMrkv+m7/5mzkbHFGRNIk7Lf0lt/rDg0+JaCINFZ2HOxG+HCI4GIx/wIvK3RZ6kLbpy92sWrw8+vKkvTpbhrZgy9AWHCwcPOrX7En1lFZ1ik0K1mTXoCvZNadd2MpFRCAtAtNi4PcepVxuqyuX0ybFit0rAAD+Kh/+Yi6/E1G8zfpR7Mtf/jJyuRweffRRPProo5M+JiIMQVQe0T1V8zz4lIiiUrdhhT1oSy+nBqeWStGkbcLGf989YAQ2cM0JorBTXN3ZOrQVQ+HQtF9HIFjetHxS0FmbXYvebC/aEm2V+nbLbkq5XLG73KArlyvdbpnAP4UBiIjib9aPZDt37izHOIhmRLIsfyNqVDo2HnrCA5MPMgWA0cQomhY1IewJsd1ud0Hnt+Pd2LYPbUde89P+2774WJ1ZjTW5NZPK2HqzvSd9oGjcSErgL/PhLZ1QLrcvxEu5l7B85fJqD4+IaE7w6RwiIqpJWnDn+BSDjw5NbjhwAAewI7cDO5p2YKvZiseHHsf+Xfvxuxd+B4upHSUBoMk0lVZ1int11mbXYkVmxTHbTjeiieVy0ivYuW8nTpFTqj0sIqI5cUIh6OWXX8aDDz6IF198Efn85GfV7rzzzjkZGBERNRa1Cnt4QujpV+RtHjuxE9uL//nbsc1sw7ZgGw6Fh4B+uJcjtPltpT06xaCzNrcWS9JLaqI5ARERVdesQ9AjjzyCa665BitWrMDmzZtxxhln4Le//S1UFeeee245xkhERHVIVV251YEQew7swdbDW7HNbsN23Y5t2Ibt2I7f4XcIJ/a2PuKoncXpxViTXYOVmZWABa5ZcA1Oaz4N3cnuWDQnICKi6ph1CPrABz6AW265Bbfffjuam5vxrW99C11dXbj++uvx7/7dvyvHGImIqA6MBCPYfmg7tu7d6gLP8DZstVuxHdvRP91yTiTn5bA6uxq92V6szkSvs6uxKrMKWT8LwDU9+PG+H+OyjsvKfuYOERHF36z/UmzatAn33HOP+2Tfx8jICHK5HO644w5ce+21+Ou//us5HyQREcWDqmLX2C5sG9qGrcNbsbV/K7b2bcXW4a14MXzxqAeJCgTLUsuwOjcecoqvF6YWclWHiIjm1KxDUDabLe0D6unpwY4dO3D66acDAPbv3z+3oyMiopo0HA5j+9B2t5oztLX0sn1oOwbCgaN+XgtasNpfjdVNq9Hb2ovejl705nqxMrOy4bqwERFR9cw6BL3mNa/BunXrcOqpp+Lqq6/Gf/2v/xXr16/H/fffj9e85jXlGCMREVWBquL3o793KzpDW93qThR2Xhp96aifZ2CwHMuxKvqvGHjWdK3Bgs4FMD4bExARUXXNOgTdeeedGBwcBADcfvvtGBwcxH333YfVq1ezMxwRUQzlbR7bh7djy+AWbB7ajC2DW7BlaAu2DW/DcDh81M9rkzas1tUu6MjqUuhZ0bQCTfOaYOYZmHYDSbKUjYiIasusQ9CKFStK/5/NZvHFL35xTgdERETlMRAMYMvQllLIKQaeF0ZeQKjhtJ/ji49T0qdgtb8aq+wqrBxbidWhCzzzMA9iBEgApsO40NNhYJq40kNERLWNLXSIiOqIqmJfft+kkFP8/9+P/v6on9fsNaM304s1TWuwJrkGvV4vVo+txtL+pfBHfWBswo19wLRHgafDQHLCxgVERHNFAVgAXrUHUt8YgoiIYsiqxUujL2Hz4GYXcqLXW4a24GDh4FE/r9PrxJqECzm90ote7cXqcDUWFhZChgQYmuaTBJBWgdfhudDTKm4FiIiITp4CCADNKxAAkOhFAWlyq+009xiCiIhqWN7msWN4R2m/zuZBt7qzbXgbhu30+3UEgiWyBL1wIadXetGLXqzBGrRrO5A/xhf04PbwpADTEq32tBuIz9BDRDQnFEAB0MKE0OO7wCNZgUkbiBEEhwPoYQVGAMkIr9rnGKeTiKjKVBVDY0PYcngLNvdHKzojW7B5dDN2FnYiQDDt5yWQwAqscGEHvVgja9CLXqzCKmQk425U/OOaFCDpXktKJr8dhR5JCsRj2CEimlPThZ7EEaEn7VZ8JpYW+1kfaAOCgwG0X6GqMBnDq/c5wmkkIiozDRR2v8Wi/Yuw9+BebBvZhi0jW7AlvwVbgi3YarfiZbx81M/PIYfVWO2iTrSq0+v34pTkKUikEqVQc9Sgw2BDRFQ5xdCTVyDEeOjJTAg9xcfoYxARIBeFoSEgOBBABxSKKAxxz9BJmXEIOu2007Bu3Tp0dHQAAN71rnfhjjvuwPz58wEAe/fuxfLlyzE8fPR2qkREjUBV8fLoy9i0bxM27dqEzf2bsdVuxVZsxQEcOOrnzcd89Jpe9CZ6sSa1BmvSa7A2sxaLMovcH80kgw0RUc3RKPAUMDn0ZAUmFwWetEASJ/a4PTEM6YAiPBhCBxmGTtaMQ9DmzZsRBOMlGf/n//wf3HLLLaUQpKoYHR2d+xESEdWogi1gx/COSV3YtgxuwdahrRiy03UYcJb6S9Gb7sWazBqsza7F2ta1WNO6BvPT8ys4eiIiOiE2Km0rwJW3GUwKPSZtXInxCYaeoxERSItAcgIdjMLQgLrmNVlx46AZO+FyOFWd8j62SCWiejQUDJU6r00MPDuGdyDQ6ffr+PCxAivcak77WvR29KK/0I/rFl+H1kRrhb8DIiI6YcXQk3f/X1rpyQlMNgo9aVSsgYyYCWGoXxEeisKQFzVQYBiaEe4JIiKK7M/vn9Ruuvj6pdGXjvo5WZPFalmN3nBCF7amNVi1eBXSi9Klmu/ABvjxvh8j62Ur9e0QEdGJmBB6NFQXOhICtAAmU/nQczRiBNImkGZxZXIHojDkR2GIaxPHNOMQJDL1MDyu/BBR3BT36xRXdDYPbS6Vse0v7D/q53UmO7EmuwZrsmvQ6/di1dAqrD60GgvDhTBwNdmmy8Bb7LmW0nx8JCKKBzu+p0dtFHp8F3q8rAeTqo3QczTiTQhD/VEY6tdSBzqGoenNOASpKv7wD/8Qvu8+ZWRkBG9605uQTCYBYNJ+ISKiapt2v85QtF8nPMZ+nfRSrMm5vTprsmuwNuded3gdCHeHCF+O/rhEJCPwFnvwFnrH7fRDREQ1IHQrPVpQwGJ8pacV8DO+a1edqt3QczTiCaR9Qhg6GML2WddUh2FoihmHoNtuu23S29dee+2U2/zZn/3ZyY+IiGgWTmi/jvhYlVnlVnaKgSe3Br2ZXmT9yeVqdsAi3BFibPcYSsf1SLTqs8iD6eCqDxFRTbPRq0ELq7YUekybgcm47m1xDD1HI75AOty+Ie2LwlB/FIbSDENFMw5BN954IxYvXgxjuNuKiCpvOBzG5sHN2Di4ERsGN2DDwIbj79fxsujN9pZCzpqsCzwrMiuQMImjfp6G6lZ9fh9C+yas+jQJvEXRqk+Kf0WIiGpWdFaPHbWlZl5eq4dELjG+0lPnRw2IL5B5AmkV2MMW9qB1K0MphiFgFiHolFNOwa5du9DV1VXO8RBRgyuWsRWDTjH0vDD8AhRTu1ICk/frFMvX1mbXYlF60axWaeyARfj7EOGucPKqT2e014erPkREtS0AdFRdQ4OkwGv3gCyAbXCr917jPZkvvsCb78G0GoR9IfSAMgxhlnuCiIjmiqrixdEXJwWdjQMbsWVoC/Kan/Zz5ifm4/Tm03Fa7jScljsNp+ZOxZrsGsxLzjvxcYQKu8cieDngqg8RURxZQMeiFtbGnZnjt/qQnDtgWkNewwLu3CJ/vg9t0clhKC3u71yD/ambVYtsPgNKRCdi79hebBzciI2DG/H8wPPYOLgRmwY3YSAcmPb2WS+L03Kn4fTc6ZNCT3eqe87GZActwpe56kNEFEsadXQbc29KWiALBF7OA5p4zXoskhT4nT60VREeDsfL5JpcaGyUMDSrEPTBD34QmUzmmLe58847T2pARBRfA8GAW9WJVneKKzz78vumvX1CEliTXTMp6JzRfAaWpJfAyNyXLBx11Sc9ocMbV32IiGpXsdwtUEhKYDoMTLOBZKXu9/jMNUkK/K4oDB0KYQ9Z97exCQ3xt3BWIWj9+vWlltjTYeomagxjdgxbBreMr+4MPo+NAxvx4uiL095eIFiRWTEp6JyWOw2rMquO2aBgrhxz1WeRBzOPqz5ERDWrWO42BsADJBeVu2WFRxPMAUkJ/AU+tC1aGTpkoYfVHbh69Mv+2JtVCPr2t7/NxghEDSTUEDuHd05qUrBxcCO2DW9DqOG0n9OT6plSxrY2u3ZK6+lyK636/D6AHp5QD54G/EU+vEVc9SEiqlnTlbv1sNytnCQ9IQwdDGEPW2DEnYeH8j9fWXEzDkG8sxHVL1XFK6Ov4Nn+Z/H80PPYPLQZGwY3YPPgZozYkWk/p81vc/t2orBzes697kh2VHj0k9nBqMPbK0es+syP9vpw1YeIqHYVonI367q7mQ4D02IgGZa7VYqkBf5CH9o+IQwN118YYnc4ogZTsAVsHdqK3wz8xr30u9cHCgemvX3apHFq7tRSyCmGnoWphTUTJjRU2L3RXp/pVn0Weq4NKBE1hqh8SnwBPACN1xU5XqKfl+YV4gnL3WqENAn8RS4MBQejv68j7v31EIZmHILuuusutLa2lnMsRDTH+gp9eH7wefym/zf49cCv8ZuB32DjwMZpW1B74qEn2YNXt726VM52eu50nJI5BZ54VRj98dmhCXt9CtE7uepD1NgUsP0WpslAAwVG4VYVEAUiPzok00fDdMGqSRPL3cStPph5xpW7pVmBVEskI/CbfKAdCA5NCEMZmeXGmtoy46HfcMMNAIBvfvObuOeee7B161Ykk0n09vbixhtvxFVXXVW2QRLRsakqXh592QWdaGVn/cB67BzZOe3tm71mnNVyFs5sPhNnNZ+Fs5rPQm+mF48ffByv73w9fFMbj2qq6kojxrT0gjH3th20XPUhoskU0H6FaTHwF0ePY3lACwqbt9DRKBTl3UVcscpFfBkPRx4YjsopKneDBZAEzLyouxvL3WqaiABZwM/4QBvcylC/QlWPepB5rZvxlY61Fv/xP/5HfPOb30Rvby/Wrl0LAHj22WfxzW9+E+985zvxhS98AQcOHMBjjz2GP/mTPynboIkaWd7msXlwc6mcbf3Aevym/zc4FBya9vZL0kvGw07LWTi7+Wwsa1o2pQV1YINpP78cVNW1OT0i2Bz5gjHgeI+tpQ5v87nqQ9To7ICFyRj4PT4kET0eJFyHShPVxKl1T66g4MKQzVvoSPQ4lI8uzgGoceV0pVUjltSdOBvt88lHc5oTeK2eO8w0wcftOBERIAf4WR8YAoIDATB9NX3Nm3EI+sxnPoMf/ehHePDBB/HGN75x0scefPBB3HjjjVi5ciXuvvtuvO1tb5vzgRI1okOFQ1g/sB7rB9bj1/2unG3T4CYUtDDltr74ODV7Ks5qOau0unNm85kVb1Sg4VGCzejk9xcvNGYk6Vp4Fl+Qcm9787nqQ0SODikkIS4AHeNxQYx7DEHqiHAURE/O5N3Fuo4pdMSdR4MxjD9mGbhVI58ldcek0WN9HuPlbvNZ7lYvJoYhm7HATrhV1BiZ1Z6gf/qnf5oSgADgmmuuwT/+4z/ine98J/7oj/4I73nPe+ZyjER1T1Xxu5HfjTcriBoWHO3cnVa/tRR0imVtp+ZORcqkyjdGe+wVm9L/z2ZByceUYHPkC1LRRQsR0VHoiAIKF4CyJ/Z4USqJmxCgiqvWxVUjLSjsqHUldQWW1E1rmnI3ryVqa81yt7oj4lb2iv8fJzMOQdu2bcMVV1xx1I8XP/bAAw8c80BVokY3ZsewaXDTpM5s6wfWoy/om/b2y5qWTVrZObvlbCxNL53TBxtVhQ4oOvo6YMcsCvnClMCDqYtPR2eiC4ljBZsU67+JaA7kAQQuAJnWua1ZE4m6YCWiTeAAPHiTS+oKCjsW7TcamxAANCqp82R81aheS+rCCd3dEgJpjsrdsix3o9o14xDU1NSEw4cPY+nSpdN+vL+/Hy0tLQxARBMcyB9we3YmrO5sHtqMQKculyQkgdNyp5WCTjH0tCXayjpGHVUUNhZgD1icgTNgj1WnJph+1eaIwAMvfs8IEVEMBW4VyHQbSEflHnOOWlIX6viqUfFlxK0gHbOkLo6K5W7F7m5N4vZo5jw3L/wbQDVuxr96F110Eb7whS/gC1/4wrQf/9znPoeLLrpozgZGFDf9QT+e7XsWT/U9hSf7nsQz/c/g5dGXp71tu98+ae/OWS1nYU12DZKmck8iqCrCXSGCLYEr9zBAf6ofrdlWmLSZNuwgwT9sRFQjrDsc2Zvvwev0auKxqVgKN6WkLoTrUpd3e4zsyOSSOmtdOtJ+hUrUDUZw7MYwxS9xtNsc7/Oj25zoOZAibpVHOgVeswdkWLpM8TLjEHTrrbfi8ssvx4EDB3DLLbdg7dq1UFVs2rQJ//zP/4wHHngAP/nJT8o5VqKakbd5PD/wPJ7qewpP9T2Fp/uexuahzdO2iTyl6ZRS0Dmr2XVnW5ReVNU/2DoWrf7sd394pVVgTjV4bvi5mmqRTUQ0rWIr7HYDr9ur6YtvkWi1xz+ipC46AgD5qITuBcDr8eCZOdpdLhP/V476sZl8/rSM+35Y7kZxNeMrnYsvvhj33Xcf3vnOd+Jb3/rWpI+1t7fjnnvuwSWXXDLnAySqNlXFC8MvlFZ4nup7Cr8e+DXG7NiU2y5NL8X5refj/NbzcV7reTir+Sy0JmrnkGFVhd1jUdhccH98BfBX+vCWewg1BIarPUIiouOIApA0C/wFvispiyERAZJwzQOaXDmdmWfgeTFrsUUUU7N6uvdP/uRPcNVVV+EHP/gBtm3bBgBYvXo1rrrqKmQymbIMkKjS9o7tLa3wFFd5pjuDp91vx3mt500KPd2p7iqMeGY0ryhsKsDujVZ/mgWJMxIwuWinbjzPOiOiBmMHLUyTgb/QhyTjGYCIqPpmXfOSyWSmPQj15Zdfxh133IEvf/nLczIwokoYCobw3MBzpcDz5OEnp21LnTIpnN18dinwnN96PlZmVtZEDfpMhHtDFDZOWP05xYd3Sm2XkBARHUmH3WGbxzsLiIjoeOas8P/AgQP46le/yhBENSuwATYObpy0wrNhcMOUbmgCwdrs2kmrPGc0n1HRpgVzRQuKwuYC7O5o9ScnSJyegGmp1z6tRFSvdNQ1GPAX+6VzSYiIThR3P1NdUlW8OPpiaXXnqb6n8Gz/sxixI1NuuzC1cNIKz7mt56LFb6nCqOdWuC9a/cm7t73lHvyVPld/iCh+ogYCpsfAtPFJHCI6eQxBVBcO5A/g6b6n3SpPv1vl2ZffN+V2LX4Lzms5r7TKc17reViUXlSFEZePBopgS4DwlRCA696TOCMx54cIEhFVRADYYQuvy4M3j00DiGhuMATNkbFHx3DOtnNgUxaFvoI7NCxjIE3CQ8Pm2Eg4gt8M/GbSPp4XRl6YcruEJHBm85mTVnl6s70wUr9hIDwQrf6Mure9pR78Vb47u4KIKG6is4DMPFMzZwERlUvpTKkQrlkRrx/LasYh6E//9E+P+fHDhw+f7Fhize6xaBlpAUaA8HA4+YPGnaQsGZn8uvjC8qRjemnkJTx28DF8a9e38OEdH8bzg88j0GDK7VZnVk9pT5320lUYceVpoAi2BQhfjlZ/mqK9P+31G/iIqM4VzwJqNa4VNp/MoTqhoQs7xdel0CPRgbu+e1sHFcgxCJXLjENQa+uxzzppbW3F2972tpMeUFyl/jCFJ8MncUbhDBgx0BEtvcACOqTQoel7EEt6moBUfB3T8w9OlKpi+/B2rDu0Dj8/9HOsO7hu2m5tXcmuSSs857Weh/ZEexVGXH32kEVhQ8Hd1wB4iz34q+N7dgYRERTQAYXkok5wfDyjmCmt6gRR2LGAWoVAXNAxgPjuWg9JwCSMO1DXdyFIxxThnhA6oEAzg1A5zDgE3XXXXeUcR+yZeQYHWg9AjCDRmSi9X61CRxU6HIWiI17Duo43OnqUQ1qS46tIpslMCklIxP+XItQQGwY2uMATBZ+9+b2TbuOJh3Oaz8Gi1CL8ec+f48K2C7E4vTj23/vJ0lARbA8QvhitPKaBxGkJ1swTUezpoEKS4s4CSjX2Yz3VLlV3HTdlVQcYX9XxAJM2rrQt4Z7cLgYdeEe/jpMmARaAQaiMuCeozMREgWWas2RVFRgDdERhR+zUgBR1w9G8Qvt0SitneBPK7CaW2GXErS7V4C9LwRbwbP+zWHdoHdYdWocnDj2Bw8HhSbdJmRRe3fpqXNJ+CS5tvxQXtl2ItEnjx/t+jNd3vh6+4d3W9lkUni9Ah6PVn4Ue/DV8tpSI4k9HFPDc45o08TGNqk/teMAphZ3oueti0BE/uv5KSamkrRh2TnTbg0kbSLcg2BNA+xVoYRCaS7yarCIRAdKuHG66vRtamH71yI5Yt/E9dM+W6eA0q0iCyaHoyL1IFaqtHg6H8eThJ7Hu0Do8fuhx/LLvlxgOhyfdJufl8Jq21+DSjktxafulOK/1PKRMatJtAjt1D1AjUqsIdgQIfxs91ZSMVn86ufpDRPGnYwoEgL/Ih2nmnkaqnFL5mnX7bIv/D2Dyqk6TW9U5snytXNdVkhb4C3wGoTJgCKphkhBIQoBpjqzRUCftO5qyiqTuZG0dVuDANP94CjA5A3/13P6h6Sv04ReHf4HHDz2OdYfW4em+p1HQwqTbzEvMw8XtF+PS9ktxSfslOKv5LK7uzIDtj/b+RKHXLDBIrE24+wgRUdwVAIy6s4CkjY9rVB7FVZ1pmxKYKNAkBJITSHK8dK30ugoBRFITglBfFITYVOuk8cozpsRzv6DITf2YqgKjmLbETkfcs2wYA+yYRb4vj+Q5yRPuIrZ3bC8eP/R4aU/P+oH1U8r2FqYWusDTcQkuab8Ea7Nr67pN9VxTqwh3hgh2Bu6BOhGt/nRx9YcotooL+LyOcULADkVnAc1nK2yaO6pRBQ1cKbkm1F1DeQLJRk0J/Mqs6pwMSQr8bh8BAtgBC9NsGIROEkNQHRIRoAnwmjygY/LHVN1eIx1WFLYVoIcV+WfySJw1s5Kql0ZemtTEYMvQlim3WZlZWdrPc0n7JVjetJx/0E6QHYz2/gxEqz9dBolTE5Ak55ModkK3xxMFuBBUPAckLY0dhizcRV2HgdfFAERzQ+34E7/Figl/kQ8/7Y/v2YnZfU2S4ytCtt/CtDAInYyqhqDHHnsM//RP/4Snn34au3btwre//W388R//cTWHVPdE3LMekhQkz02i8JsC7H6Lwq8LwOmA1zMehGbarvqM3Bm4pN2t8lzcfjEWphdW8luqS6qK8Lchgh3R6o8PJNYmYBaY2D1oEzUsjRrb5F0HKTHRpuk2cd2iQiDcF0L7tdTxs+FErbBNq3GtsGvwGXiKFw3GtwVIRlzVRBLA84DJGreXJ8Yk4VaEQoQI+0MGoZNQ1RA0NDSEs88+G3/5l3953MNYae6JJ0icnUBhQwF2t8Xo+lFsHdyKJ5JPHLNd9ataXjUeetouRkey4yhfgU6EHYr2/vRFqz/zDRKnJdgmlqjWKdyZIMXVHhPtLWgVeFnPrfikJ9fyS04Q7gthD7uGN5JroFUhjVaAMtFhqOxuSSdB8y78iCcwOePCQcYdSC+F+rpvSULgdXuAgEHoJFQ1BL3hDW/AG97whmoOoaEV21X/LPszrEutwy9GfoH+F/on3Wa6dtU5f5qNSHTSVBXhSyGCbYHrSOMD/hofXg/LQ4hq1pElbomoM2enuJLkNI55cS8pgbfIg2kxLgz1Wff5DfCkhw4rTMK4s4DS9f/90twrHjViRy1MwsB0GLdXpkaPCZlLk4JQX+i+b66kzgr3BDWYg/mD+N6+7+HBvQ/ixwd+PLVdNXK4ABfgkrZLcNnqy3B+2/lT2lXT3LPDFoWNBeihaPWnwyBxeoIXBkS15jglbsXT32dzASYikBZ3jEF4MIQ9YKF96laF6rT/iY6Mn3EmGT7O0ewUD6JHPnoiocuDl/Ma4smDicSXUpMk22fdgaoMQjMWqxA0NjaGsbGx0tv9/W7VolAooFAoHO3TKqL49QMNYGxt1ZvuGt2Fh/Y9hH/d+6/42aGfIdSw9LGORAcubrsYF7dfjEvaL8Hph06H2W6AfkBeEZgWgwDVP6OneE5QvZ0XpKrQ3yvsduvadHqAWWUgiwShhDjyfNxyqtc5rhWc3/Iq2/wqxld7Arg2ugkBmt3+AqQATStggLB4VPyJ/t4KgHkAmoDwgNsrBL82GifM6fwW3Dl4Xo+HMBsiDMPjf06dC8Jg0muanoZR+AnciqvpNDAZAyTgrlWOcilYvEar9rViuWiHIrSurFaaK3cWZFHxflsIClUvPZzNz1hUdZqTNitPRI7bGOHDH/4wbr/99inv/5d/+RdkMpkyji5+Xhl7BU8cfgJP9D2BrcNbJ31seXo5Lmy9EBe2XojlTcuntKvuOtSFNS+tgUCwv2U/Ni3dBDU1cTepK6l8Cr0v96J9sB0AcDh7GFsXb8VoarTKIyMiIiKKn+HhYVx33XXo6+tDS8s0B21OEKsQNN1K0JIlS7B///7jfqPlVigU8MMf/hCXyqVIzk9W/OurKtYPrseDex7EQ/sewsbBjZM+fkHrBbim6xq8seuNWJFZcdx/z+6zsM9bV+7RLjBnmapuWg1sgMcOPIbL5l0W+4NVVRW6S2G3Rqs/BjArDWRJdWuY62mOaxHnt7xOan6jE+KnlLjlotcnUOI2VzSvsAct7EELhbpnvatQbDAn99/Qtf335nmu0yU3cpcEYYAfP/djvP6c18P3+PhQNLHZgWRddYqkZdb3neI12pVXXolEon7bMGqoCPdHK0I5qdh1W2GkgJ9s/Amu+MMrkExV/hp4ov7+fsyfP39GIShWv2mpVAqp1NT9KYlEombu1L74FbvACTXELw//Eg/ueRAP7H0Avxv53aRxvK7jdXhT15vwxq43zr5tdTcQJkIUnnP7VOwzFslzk1U/n8Y3lZvfctAxRWGja0sOANIqSJyecGU1NSLuc1zrOL/lNaP5ndjFbWKJWxtcF7cmcef31MJFehOgCxVoBYJ97pBESUrVSuRO+P6rrvOl3+G7VtjsBDct3/OR8GrjeqZaSs0OxiyMbyCdMmfNDmrperEsEu7xIky6vYWVCkLqufWUhF/9+Z3N16/qX+LBwUFs37699PbOnTvx3HPPoaOjA0uXLq3iyGpX3ubx6MFH8cCeB/DQ3ocmtbBuMk24Yv4VuLb7Wryh8w1oT7Sf1NfyOjzI+YL8M3nogCL/VN4FIW7WnzVVhd1tUdhcKF10+at8eMvY+Y2oIgK3D2VKF7fczLq4VZOIADnAb/IRHgph91t3tlBW4vFUpgLarzAthgGIjmpSs4O0wOv04DV7VX/yNW7ECLz5UbOEgxbIjB8WS5NV9eHzqaeewh/8wR+U3r755psBADfccAPuvvvuKo2q9gwFQ/jh/h/igb0P4Pv7vo++oK/0sVa/FVd3Xo03db8JV867Elk/O6df27QYJF+ddEFoSDH25BiS5yZrauWi1mleUdhUgN0brf40CxJnJGBynEOisrEu9Ezbxa3JPatcrRK3EyWewJ/vQ3OKYH8APewaMkim+o0TjsUOWpimKADxYoyOoGF0uGkYNTuYZ2By1S3Bj7tSEBLAHmAQOpqqhqDLL78cNbIlqeZMbGX9o/0/wqgd3yzfnezGm7rfhGu6rsFlHZchacpbf2myBqnzUy4IDSvyT7oVIdPCi/jjCfeEKGwquGefBfBP8eGd4tVGmQ1RPYn+lOiwQkMtlbiVDiqtpRK3kyRpgb/IhaFwvztbyGQMUN1S/GnpkEJ8cQGoglUExb0kMHAd9jy3alYPP/96oYFCh6NjIbIGpjU63JQtnufEpCC03wJN4KraEeKwkN4wXhl9Bf+691/x4J4H8dihxya1sj6l6RRc030Nru26Fhe0XTClo1u5SZOMrwgVS+POScJ0MAhNR/OKwpYC7O5o9ScX7f1hcCSaOxMOKlXrLqYk7TZP13qJ28kScatakhWEB0JX9jLqHmuq0ThhOjrqVuH8hb4bV6W+buACkJln3H1kTEuvi/cThqPqmdjswLQY1+wgU/+Hm1aDiMCbFwWhfdH1CINQCUNQlW0f2o4H9z6IB/c8iF/1/WrSx87InYFruq/BNV3X4MzmM6v+ACFJQfL8JArPFWAPWeSfzSNxZqJ0UBe5vT/h70ME28fPK/BO8eCv8PlHluhkKVzgiS5mJ5a4eQkP2AF4yzz4fuP8aZOEwF/gQ5sVwb7AnS2UrIGzhfLuxevxYNoql8o0VOigC0DefLfnUjUKQYGW9oZpXl04CsbvTwp3n4IXhWePB0/OFVU33zqqMEkD0xGFnxTDT7mJCLyOaI/QfgahiRrnL0WNUFX8ZuA3eGDPA/jXvf+KDYMbJn38wrYLcU2XCz4rsyurNMqjE1+QeFUChfUF2H0Whd8UgNPcqd+Nzva5xgfaHz0jnRMkTkvAtNbI07JEcVRsaJCHu6hPuH11Xs6DaTJutceIK4FDvPb4zCXJCvy0D+1ThHtDaF/UOKEajZoCV5ZoFhi3GlMhqgo7YOG1evDmjTedEYlWe45YFSyGIwTR6lEQXahH4Qh5wIaW4egkHNnswHQZNjuogilBSAFJ8WfAEFQBZW1lXQXiCRJnJVyr510WhQ0FaKDwlzbm3UkLimB7gPDlqHzRizq/LebeH6JZ06hcJh9tlPaj1Z72qKFBk3CD71GIJ5COqERufwh7aEKJXKWmzI6fBeR1Vq77papCBxQmZ9zXnUFIKYYj+IAcMUGlVaNiOMpHF/MMRzMyqdlBxu1NkWzlzq2hqUpBqFgap2j4br+NedVaAZVsZV0NYtwelyARIHwxRLAlgBbUlX01yDOxqorwlRDBtvHSN9NjkFid4DMsRDN15Jk9Jgo+bVFDg7S41Z4GeVyZC5ISeAs9mGaDcJ9rnCBpKf8Fj8IFkVYDb0FlnwTSYYUkBX7n3HSgE1+mD0dhtAftWOFI3X6XRgxHWojCjwAmEzU7yM7+cFMqDxGB137EHqEGDkIMQXNoNBzFA30P4N92/Rv+bd+/VbSVdTWICPxe9wcn2BEgfCEECoC/pv6DkO2PSt/6ohKcrCBxagKmnaVvRMdlo30YBYVodLGZFlfmVucNDSpFRCAtAskIwoPu4ETtU7cqVI7q5SgASVZcI4QK/vzsqIWowOvyyn5BVwo304Wj4n6jYMKeowKmXzmKGjLAxD/gq7pyNx2Nmh20Rvt9mrjfpxaJCPx2H6GErnQWCpNuzGsXhqA58tff+2t8/fmvI6/50vsq3cq6GkQE/gofSADB5gDhSyG0oEicnqjLZ360oC7wvTSh9G2lD28JS9+IjqrY0CDv2leLEUgyOg8kE53Zk4r/xWAtEl/gd01onNCnLnQ2zW2JnA4qJBUFoAru99C8uwD3FnhVPb+uFI5SMwxHAYAR93ELC6vuWXk7bGE9G/2jE7/AEcFLMPU2J/N+zP73TzVaBRtD6ffZay5/EKW54bW5ctVwbwg74s7yajQMQXNEIMhrHsuTy3Htwmur1sq6Wvwl7pm/wgbXFroQFJA4K1E3JQCqinBXiGDrhNK3BVHpGx/wiaaa0L4aCtfQIHtEQ4M6eXyIA2kS+It9aItrnGD7rAsNc9A4QYfVPSG00HfhqkKK58yYLgNpqc370nHDUeBWRO2ICz4mZWA841ZXFKXzr4762k78R494feT/F99VPJ/xiNvpdDee9M1M/fckJZBu93vNZgfxY1oNIO5MQzscnTfWQBiC5sh7LnwPTh85HW/PvB2prlS1h1MVXo8H+EDhNwXY/Rb5Z9xZQnHfxGwHLAqbJpe++Wv9UqcVIkKpoQHy7gJPvPH21aWGBrxIqiox0dlCmehsoQPWtSzOmhM+W0hH3eZ3f3GFzwKyUSvsdgOvvXINGObKkeHIz7rLMX+hDz8x/aXZpMPljxV4ZvA+hZ70vwHjwjVLV+OteH5hIwYhhqA50juvF9ubtsfugXiueZ0e5FxB/rk89LAi/3QeyVclY9koYNrStxU+vKUsfSMqNTQoRKs9xfbVLQI/5483NODvSs2RpMBb4MFr9hDsC2AHLCQpsz9bKNrvYnpMZc8CKnaCa47OAmqQ+9ik64uT/JaP3NNEjc20TFgRGrJVLS2tJIYgmnOm3SB5fhL5Z/LQAUX+qTyS5yYrWiZxMlTVtf7eVnBnkwAw3QaJXpa+UYOz0b6eggI2asmcFsi8Cas9fFY4FkQEyAF+k4/wUAi730L7o7OFZnJlEAB2yMLrdmfyVJIOKqQpaoTA+xvRnDDNjReEGIKoLEzzhCA0rBh7cgzJc5Mwudr+pbIDUde3wxNK39b4Ff8jT1QTjmxfLdEG6DYDk3Whhw0N4k08gT/fh+YUwf7APfaJe+w76mJBdBaQmWcqehYQ4BoHiCfu67K8kmhOmdyEIDRoa/6a7WQxBFHZmKxB6tUpF4SGohWhVyXdRrwaowVF8EJU+qZg6Rs1DgUQwq3yhFp6LRrd7xOu7t80G9dGle2r65KkBf4iF4bC/dHZQk0ytZRZ3UqMaTXwF/gVbW6hY27/kdfjNdS+BaJKMlkDdEdBaMC6FaI6xRBEZSVpcStCz+ah/W6PUOKcRM00FVBV181uK0vfqA4pXPeoCQEHodtUXtoTIHCb4otnl6QBkzRuj4gvbrUnydWeRiASNU7ITWicMKaTVoV0UCEZgd9T2bOANHDtmE2XqeuLMqJaYLIGWDAehCRXn2c+MQRR2UlSkDwvicKvC7AHLQrPFICzAK+rukFoSulbJur6xtI3iotjrOIooqBTDDgmKl1LCpAAjG/ceTHFQxv96HZ1+IeOZkd8gd8dlcjtC6D9Ck1Ej5PJ6CygCja7KXWCm+c6wRFR+ZnMeBDSQQVy9ff3gSGIKkJ8QeJVCRTWF2D3WhR+XYCepvAXVf4uqMGErm9Rm09/hQ9vGUvfqIYcZRUH6lYwBdGz8x5cq1o/Cjcp41Zw/KgFb/QaPju10exIVuA3+dDD7mwhwJ2PJpkKBiBV2H4Lr9U1YKi3izCiWmaaxkvjdECB5voKQgxBVDFiBIkzEwg2BQhfCRFsDIAA8JdV5m44belbV1T6FpPOdVRHolWcKWVqxVUcGQ854k1YxUlGqzhHBByu4lA5iBFIh8BLe8DzqHgpmg64c4y8To+H6xJVgWkykAWCYI9bFUZL/fytYQiiihIj8E/zgQQQ/i5EsDWAFhT+Sr+sv1R2MCp9OzSh9G2ND28+SyuoAiygI+6+Z/stVHQ84JioHC0T7cWJVnGmBByu4lAVVeOsNzvkzi/yu/zYH7pNFGeSFvgL/LoLQgxBVHEiAn+1+6MWbA8Q7gyBAuCvnfsgpEHU9e1Flr5RFQSAHbEQlVKjDX+RDz/pj+/F8cBVHKIj6KhbFfU6PTapIaoBknJ7BespCDEEUVWICPxT3IpQsClA+HIILSgSZyTmJJyoKuyeqPRtzL2PpW9UERqdqzMKwLhzF7x2D2gCcMgdJmw8drdqVBoqUHBP0ABuVZpPyEymeYXmFV63V/fnlBDFiaSOWBFqjneVAkMQVZW/2LVZLTxfcKElKCBxduKkar/toEWwOYA9ZAG4M078tSx9ozJTV/KmeYWkBKbTwGvxgEzUejiM7x8KOjGq7pBZLUSHzdpoH5fv9tZoQd2FRBYs94poqNBhhek0NXmmHFGjk+T4ilDczxFiCKKq8xZ4gA/XQvuARf5pd6jqbC8Kpi19OyUqfeOGWiqXYsmbdWfqeJ0eTLOpyh4Kqi4Nx0OPhq65hfgCSQikVVznvkR07pIRaKgID4awBy20oO5w0piXl5wMtQo7YOG1e/A62AmOqFZJ0q0IhXtChP0hkKj2iE4MQxDVBG++BzlP3KGqfYr8U3kkz03O6EJy2tK3TgN/je/aOxLNtelK3to8d6BcBQ+QpOoprfIErryt+MSLJAQmZyBpcQfOJt3Kz3QX9OIJvPkeTNog3B/WRXnJiVJV6IDCa/bc34MGnAOiOJGEwOt2FTbBoaDKozkxDEFUM0ybQfL8JPLP5KGDivyTeSTOTbgDu47CDkWlbwcnlL6t8eF1svSNykDdhm0di0re5kclb9n4bxClY1M7YS9P9Pe+tMrT7FZ5kHQXBrNZeRZxny9JQXgghO2zroQy2Vj3Jx3U0koqn0ggigdJCLwFHozG8wlnhiCqKabZIPnqJArPFKAjLgglz0sCmcm30zAqffvdhNK35T685Sx9ozIIohbX1gVts8jAa/ZY8lanVNWd2zRhL0/xQFrT5A4LlUS0ypOYmwAsKXcxgRSgBxU2byHZxiiPs8PWrYp18neKKG7El/E91zHLQgxBVHNMxgWhiStC3jnuF0xVEe4JXenbaHT7+VHp2zFWjIhmTeGe+R9RdwGcE5a81Sm1RzQwgCtVk6RAchNWeZKzW+WZLTECf54Pm7YI97tVIZMzdX1/07wLnN4Cj4/hRDFVfIyK25M2DEFUkyQlrjQu2iMUPhOie2E37MsW4cHQ3SYddX1j6RvNpYklb0mBmWfgtbLkrZ4US9q04Fb3INFenrQBmjC5gUEVfuYma8bL4w5baErd2OqMBgodiTrBtdTf90dEtY0hiGqWJATJ85KlrnFrXl4DhXtW3lvuwV/us/SN5s7Ekre0wCyMSt54UGOsqU7Yy1Nw7yseVGtaJjcwqKUVl+KmY0kL7AEL22/dKmSdNAxQq9BBdU8ydPCJLCKqPIYgqmniCRLnJJBfn4fuVcg8QWLtsZslEM1YseRtVN1qAEveYk9DdS2qA/ciJmpTnRRIm8AkzfhenhoPFCLR/TEl493j6uBMIdWoFXarB28eW2ETUXUwBFHNEyMwZxis27UOl/ZcCuMxANFJOrLkrYMlb3GlVoE8YEddh0gdViDt2pZL04QGBkdpUx0HpslAFkblcYfif6aQDihMxrhGCFzNJ6IqYQiiWBARFBKF2P7RpxoRRiVvIUve4kwLWnoRcSGnuKfEX+LDz/g1v8ozW8XuaaYp3mcK2SELSQq8Li/2K1pEFG8MQURU//ITSt6yAq+dJW9xUlzt0UIUYBPizmlqNzBp49oqu4Ug19QgZsFgpuJ+ppCOKkSjMFeHjR6IKF4YgoioPh1Z8tZu4LV57qKxTi+S64kGCs1PXe0xGdfMYMr5PLZ6Y620OJ4ppAX38/S6PZgcAxARVR9DEBHVl4klbymB6THwWljyVuumrPb44koWJ6z2cP/IuDidKaShQoeiVtitDEBEVBsYgoioPhQPNsWELm/NLHmrZcdc7UlV75yeOKn1M4XURp3g2jx4HewER0S1gyGIiOLriJI3aRf4bT5L3mqU2ujMnvwRqz1txnVA42rPCanVM4VU1XWCa446wfF3kohqCEMQEcWLjUqm8u6i2qQMS95qWHG1BwW4xhRc7SmLWjxTSIcUkhb4nT5XZImo5jAEEVFt02jlIAo9Iu7sF7QCfs5nyVuNmbTaY11rZ0lHB5VytafsauVMITtiISZqhZ3iz5uIag9DEBHVFsWkkikIXCewZoGXjVrrpsHgU0O42lNbqn2mkOYVCABvgQeTqZ39SUREEzEEEVF1FUNPwV04lUJPVmByhqGnBk272pPiak8tqdaZQhoodFhhugykmfcBIqpdDEFEVFkKIBh/trgUejITQk+qunsZaCqu9sRTJc8UUqvQQYWZZ9gJjohqHkMQEZVXMfQUxi+g4cPtU8hGKwdpYeipIapuhacUVo9c7UlHPzOu9sRCJc4UUo1aYbd68OYxABFR7WMIIqK5dWToAdxKT0og88YvoJHgykE1qY32XIUT/t9GH5Ro/4gPrvbUkXKeKaQDCpOJWmEzHBNRDDAEEdHJKYaeIAo9CrfSkxRIx4TQwwvoilIbreaEgIbj/19ioqDjRT+r4osXvc+LgiovaOtKOc4UskPW/btdHld0iSg2GIKIaPailR7NKwQyHnomlEohxdBTTqrjqzcTQ45q9DOZsJojiajcMAkY3wAe3PuLgYeHWDaUuTxTSEcVoi4AzdWqEhFRJTAEEdHxhW5viBYU0PH9IabVlPb0MPTMrdK+nCODjh2/zcRVG2mKVm78aDXHn/xxoiOZJgPpEYQHx88Umk1L6+ITIV63B5NjACKieGEIIqKpiqEncKsNpdAzcX9IiisIJ+uY+3IQhRcDwLgLViSiVR1/Qrjx3ccZQOlEiB+dKZQ2pVba0nz88jgNFTqkMJ0GppUBiIjihyGIiJzAvbL9Fuqpa4HcHoWeNEPPyVBV6JgCAGyfhfWipDPdvpzpQo7HkEPlIyKQFvdEx0zOFFIbdYJr89gKm4hiiyGIiNwen2F3ke4v8pHIJFzoYRnVSVFV6KgCY3D7cACY+QZ+2ue+HKo5MzlTSDU6CygXdYLj/ZaIYoohiKjRWcAOWph2A+wGTLth+DlJGkbhJ4A7T6db4Kd8YBPgd/gwCZYPUW062plCiB4SdEghaYHf5c/pOUNERJXGEETUyDQ636PVQLsV2F3tAcWbFqLwo4BkopbBWdeooFAoHP8fIKoRU84U8t1KsUh0v04xABFRvDEEETUwHVRIk8Bf4CPwg2oPJ5ZUFchHrYI9gWk2MC0GkhHulaBYm3imULDXPT54nd6sOsgREdUqhiCiBqUjCniA3+O7xgfh8T+HxqmNmh2Mub0UZp6BaXad8xh+qF4UzxTyPA/YBEiO920iqg8MQUSNKA8gAPyFPi9qZmm6/T5es3dCB00SxUXxIFQGfCKqFwxBRI0mBOyIdWUu7bygmSktqFs9w9T9PkRERBQvDEFEjUQBO2Bh5hl483m+x/EU9/vYUQvjub0+3O9DREQUfwxBRI1CAe1XmBYDv9vnCsYxqI1K3vLR2SnzPZicKZUEERERUbwxBBE1CB1SSMp1guP+lelpGJW8hYA0CUyHgckZzhcREVGdYQgiagA6ooAA3kIP0sQL+iNN2u+TFXgt3O9DRERUzxiCiOpdwb14Cz2YZpZzFU3Z79Matbjmfh8iIqK6xxBEVM9CwA65TnCmgwEImH6/j5dzB0ISERFRY2AIIqpXCuiAwrQbeJ3sBDdlv888A5Plfh8iIqJGxBBEVI+iACS5qBFCA+9tmbjfx2Rd2ZtkuN+HiIiokTEEEdUhHVZIQuD3+JBk413sqyowFu338bnfh4iIiCZjCCKqMzqqgAW8RR4k01gX/FP2+3R58LLc70NERESTMQQR1ZMCgDxgetzqR6Pgfh8iIiKaDYYgonpho05wnR68eV61R1MRpf0+ApgM9/sQERHRzDAEEdUDBbRfYdoMvK767gSngULHFFpQmES036fFQJq434eIiIhmhiGIqA7YAQuTNa4TnF9/QUALCs0rEADiCSTtzvcxTQaSqr/vl4iIiMqLIYgo5iZ1gquTQKCqQAHjwSchMGkDyQlMkwGS4KoPERERnTCGIKIY0zHXDMBf7EOy8Q4FpeATfU+SEHeuT9aVuiHB4ENERERzgyGIKK4CAKNRJ7i2eHaCU3XtrDXv2npLQmCao+CTloY844iIiIjKjyGIKI4sYActvPnx6wSndsKKjwKSFJiWCcGHba2JiIiozBiCiOJGAR1QmFYDr9uDmNoPDWonrPioO8jUtBuYTBR86rCZAxEREdUuhiCimLGDFiZT+53g1EYd3cYASLTi02FcRzcGHyIiIqoihiCiGNERhXjiAlC69kKEhlHwyaMUfGS+jAcfHmJKRERENYAhiCgu8gACwF/kQ3K1EyY0HD+8VIxAUgLpnBB8YlCuR0RERI2FIYgoDgK3CmS6DaSt+qFCA7fio3mF8d2BpaYt2uOTYvAhIiKi2sYQRFTrok5wZp6B1+lV7awcDdyKDwqA+AJJC7wOz632pIVn+BAREVFsMAQR1bIJneD8BX7FV1i0EO3xCQDxXNgx8w1M2gApHl5KRERE8cQQRFTDdFAhaaloJzjNK2xogdCt+Ji0gWl2+3uQZPAhIiKi+GMIIqpROqKAB/gLK9MJzo5Y9z8BYHLR4aVNAiQYfIiIiKi+MAQR1aICXCe4hZXpBKcFt9cHcN3n/CwfGoiIiKh+mWoPgIiOEAJ2yMLMN5D2CgQgVeiQwrS5hwNJctWHiIiI6htDEFEtUcAOWJiOynWC0yGFZAVeu1f2r0VERERUCxiCiGpFsRNcS9QJzqtAAMorBAJvnlexxgtERERE1cYQRFQjdEghyagTXKICAcgqdFghHQKT5UMBERERNQ5e+RDVAB1VAIDX47mObJX4moMKaRZ4bSyDIyIiosbCEERUbQUAecDr9mBaKvMrqaMK8aIyuAqU3RERERHVEoagOaZ2vNUw0XGFgB12neDMvAoFoFChY1EZXBMfAoiIiKjx8Apojpl5Bjqq7qBLomMpNkJoM/C6KtMJDnBlcKbFsAyOiIiIGhZD0Bzz5nnwF/qlVsdgFqLpaLQnJycV6wQHAHbEQpJRGZxhGRwRERE1JoaguSaA6TBILEnApA20XwFb7UFRrdFhhfgCv8ev2OGkGqjbezTPg6QYgIiIiKhxMQSVieQE/hIf0iKw/Zb7hKhEx1ww9no8SKZCAUgVdtDCtBlICwMQERERNTaGoDKSlMBf7MPr8qAjWmqDTA2sAGAUMN0GprVyv346rDBNBl5H5fYeEREREdUqhqAyE1/gLfDg9XhA6DbCc59Qg7KAHbIw8wy8eZVrSqAFBcKoDK5CpXdEREREtYwhqAJEBN58z5XHJYX7hBqRAtqvMK0GXnflmhKoqgte7QaSYwAiIiIiAhiCKso0G/hLJ+wTCqo9IqoUO2Ah2agRgl+5MKJDCpNlGRwRERHRRAxBFSbpaJ/QfA865A6tpPqmwwpJRAGogl3ZNK8QRO2wKxi8iIiIiGodQ1AViC/wejyYHgPk3bP13CdUn3TM7cfxF/iQbAUDkFUXvjoEJstfcyIiIqKJeHVUJWIEfqfv9gkZYcOEehTAdYLrMpDWyq7E6KBCmgVeW+UaMBARERHFhV/tATQ602ogSUGwO4DtszA5w59K3FlXAgfrDs715ld2P46OKsSLyuA8lsERERERHYmX2zVAmtw+oWBPAD2g7gDNZLVHRbOmUfgJAMlGIaRFKtYJDgA0dPvMTJeBaeJCLxEREdF0GIJqhCQE/kIfYTKE3es6x0mGz+LHggI6okDB/cy8hR6kWaqyCqODCtNiWAZHREREdAwMQTVEjMDr9GCSxpXH9VuYZgMwC9WmKPxoPmpD3R2t/FSpE5sdsZBktAJVwdUnIiIiorhhCKoxIgJpEyQSCQS7Amif2+AOPrFfO9Ttu9ExhckY+F0+pLV64QcANFAgD3g9XkXbcBMRERHFEUNQjZKswF/quxWhQ9a1OU5Ue1QNbmL4SRt4izyYNlP1M3hUFXbIwmtzK1FEREREdGwMQTVMkgJ/UbRPaH+0T6iJF7kVp+68Hx1VmJSBWej23EiiNn4WOhKFso7KdqEjIiIiiquaaB/1uc99DsuXL0c6ncaFF16IX/3qV9UeUs0QT+B1e/AX+oACdsDyPKFKiVZ+bJ+bc6/Hg7/Ch9/p104AKrhudN48D5KsjTERERER1bqqh6D77rsPN998M2677TY888wzOPvss3HVVVdh79691R5azRARmA6DxJIETNpA+90ZNFQ+OqbQPneArbfAQ+KUhNv7U0NBo1gGZ9oNJFc74yIiIiKqdVUPQXfeeSfe8Y534MYbb8Rpp52GL37xi8hkMvja175W7aHVHMkJ/CU+pEVg+y1QqPaI6lAe0MMKhIDpNkgsT8Dv9muy2YAORV3pWAZHRERENCtV3ROUz+fx9NNP4wMf+EDpfcYYXHHFFfjFL34x5fZjY2MYGxsrvd3f3w8AKBQKKBSqmwiKXz8IA5iwjNnSB7RHoQlF4UABSACSrv8L4MAGk17P/ReIDjpNAGa+gWkz0LQiQACE5fmSJ0PzLqh5rR4CDeYkEBfvw9X+XapXnN/y4vyWF+e3vDi/5cX5La9amt/ZjEFUtWo7TF555RUsWrQIjz/+OC666KLS+//u7/4Ojz76KH75y19Ouv2HP/xh3H777VP+nX/5l39BJpMp+3iJiIiIiKg2DQ8P47rrrkNfXx9aWlqOedtYdYf7wAc+gJtvvrn0dn9/P5YsWYI/+qM/Ou43Wm6FQgE//OEPcfmqy5FsTVbs69oBC7vHQkfV7QupeoFjeQQ2wGMHHsNl8y6Db+bgbhu4rmoQwLQat68mE48VNTtgIU0Cf4EP8eZuzMX78JVXXolEgv3Y5xrnt7w4v+XF+S0vzm95cX7Lq5bmt1glNhNVDUHz58+H53nYs2fPpPfv2bMHCxYsmHL7VCqFVCo15f2JRKLqk17kez4SXgXH0gZXtrU7gO2zMDkTs2g7O77xTy4EFcMPAOkQ+O0+kEFs9tTomCvZ87o9mHR5Em8t/T7VI85veXF+y4vzW16c3/Li/JZXLczvbL5+VdcNkskkzjvvPDzyyCOl91lr8cgjj0wqj6Njk7TAX+zDm+9Bh9xhnnSEELD9FjqikBZxDQ8W+ZCsxCcAWXXj7xCYpjpd8iMiIiKqgKqvGdx888244YYbcP755+OCCy7Apz/9aQwNDeHGG2+s9tBiRXyB1+MBSbjyuEBdeVc8ru/LJwTssIVAYJqNO08nF5/gM5EOKEyrO6iViIiIiE5c1UPQm9/8Zuzbtw8f+tCHsHv3bpxzzjn4/ve/j+7u7moPLXbECPxOHzZpEe4OoQMKaW7QIGRdC2koXPjp8CDN8Qw/AGBHLCQp7vsw8fweiIiIiGpF1UMQALz73e/Gu9/97moPo26YVgNJSsPsE5rERq2urTtXqRR+YhwcNFAgD3g9XkO0QyciIiIqt0a5NG440uT2CQV7AuhBhTQJULmmdZWnUfgJAMmKK3triXf4AQBVhR2y8Frd90NEREREJ48hqI5JQuAv9BEmQ9i91gWEmLSBnjGNur0V3PfmLYxWfuawdXQ16YjCpKK9TDEt5SMiIiKqNQxBdU6MwOv0YJIGwe7A7RPK1cE+oSj8aF5hsgZed7Ty48f9GxungVvZ8hZ6kGT9fF9ERERE1cYQ1ABEBNImSCQTCHYF0L6oYUIcm4wVw8+YwmQM/C4f0lpf4QeIyuAGbambHRERERHNHYagBiIZgb/Edw0TDlmYrAFO5kyrI48j0uO8PuJ2qjqj20HHb2v7LfwmH94iD6bN1F34KdIhF/K8DpbBEREREc01hqAGI0mBvyjaJ7TfAsNwpXFHBBA94h0yXf3cke+a7m054v0y8X8nlOUVb2em+VwBRAUYcqVhfrsPSdRvMNC8QiDw5nt1G/KIiIiIqokhqAGJJ/C6PZi0cftOJn0Q4ysP0wWYCe+btEJxZOCZ7vXRAlEx6BxrxSMEsA+uPKxOmh5MR61ChxWm07iVOiIiIiKacwxBDaq4T4hqiw65xhVeexw3bBERERHFA59qJqoROqaum1+dr3YRERERVRtDEFENUKvQEYV0CEyGv5ZERERE5cSrLaIaoAMK02rgtbEMjoiIiKjcGIKIqsyOWEhSXDtswzI4IiIionJjCCKqIg0VyAOmw0DSDEBERERElcAQRFQlqgo7aGFaDUwrfxWJiIiIKoVXXkRVoiMKkzKuG9yxzkgiIiIiojnFEERUBRooEESHvyYZgIiIiIgqiSGIqMJKZXDtBtLMAERERERUaQxBRBWmwwqTMfDaWQZHREREVA0MQXMtjEqdiKaheQVsVAaXYAAiIiIiqgaGoDlm2gx0UF3rY6IJ1KpbBeowMDn+6hERERFVC6/E5pg3z4NpNbADFmoZhGicDikkJ/DavWoPhYiIiKihMQTNMfEEXpcHr9lzQUgZhAjQMYWIuDI4j2VwRERERNXEEFQG4rsgZDIG2q8MQg1OrUJHFNIhMBn+yhERERFVG6/IykSSAr/Lh6QFOsgQ1Mh0QGFaDMvgiIiIiGoEQ1AZSdqtCIkvsEO22sOhKrCjFpKIyuAMy+CIiIiIagFDUJmZjHFBSAV2hEGokWiowBhg5hlImgGIiIiIqFYwBFWAybkghMBtkKf6pqqww9aVwbUamBb+mhERERHVEr/aA2gUptXAhAZ2nwXE7Rmi+qJWoaMK5AFpEpgedx4Qy+CIiIiIagtDUAV57R6gcEHIuC5yFH/F7m8oAJIVePM9SE7YCpuIiIioRjEEVZBIdFBmCNgDFmgGL5RjTMMo/FhAMlETjCzDDxEREVGtYwiqMDFupQAhEB4OYVpYLhU3GkThRwGTNTCtxoUf/hyJiIiIYoEhqArECLzOKAgNREFIeAFd6zRQ6LAC4ppdmFYDyQh/dkREREQxwxBUJeILvG4PqgrtV6AFvJiuUZp3Kz/iCUxLFH6aGH6IiIiI4oohqIokIfC7fAS7A+igQpp5UV1LJoWfdtfqWtIMP0RERERxxxBUZZJyG+rD3SHskIXJ8kyZalJ1La7tiIVJGpgOA6/F42GnRERERHWEIagGmCYDdAHhrhB22MJkGIQqTVXdQbaj48HUy3mQFMMPERERUb1hCKoRJhsFoT0hdFS58lAhqtEBp2NwpW5d4lZ+Epx/IiIionrFEFRDTIuBWkW4J3SHqSZ5IV4uaqPwkwekSWB6DEzO8ABbIiIiogbAEFRjTKuBhgq717ogxIvyOaU2OuMncOHHmx8dcMp5JiIiImoYDEE1RkTgdbgzhOwBCzQD4vEC/WRpGIWfEJCs2/MjWeHcEhERETUghqAaJOJWKGABe8i6M4QML9ZPhAZR+FG378q0Ghd+OJ9EREREDYshqEaJEXidLgiFfaE7o4YX7jOmgUKHFRDA5KIzfrI844eIiIiIGIJqmnguCGmo0AF1K0K8iD8mLbjwI57AtEQrP00MP0REREQ0jiGoxklC4Hf7CHYF0AGFtPBifjqad2Vv4glMWxR+0gw/RERERDQVQ1AMSFLgdXvuMNVBC5PjYaqAO+MHecCOWJiEgekw7owfnrFERERERMfAEBQTJh0dpro7hB22MJnGDUKq7nBTHVVIynV683IeJMXwQ0RERETHxxAUIyY7HoR0VBtyxcOOWGjgvnfTZeA1ezxUloiIiIhmhSEoZkyzO0w13BO6w1QbIACoVdghC8A1hjDdUfhJ1P/3TkRERERzjyEohkyrC0J2nwUEdRsGJp7xUyx18xf58Jt4tyUiIiKiE8eryRgSEXgd0WGq+y2QA8SvjyBUbHago1Gnt+iMHyQA/Lp+vk8iIiIiqh6GoJgSEXjzPCAE7CHrzhCK8WGqahU66gKQJAVmnoHJjbe5lkJ8vzciIiIiqi0MQTEmxh2mCguEfSFMi4ldENIwKnkL4Q41bRfX6a0B9joRERERUXUwBMWceK5FtFqF9ivQilgcEKqFKPzAdb0zLQaSFYhX+2MnIiIionhjCKoD4gv8Lh+BDaADCjTXZhAqnu9jRy2Mb2BaDUyzgWSkJsdLRERERPWJIahOSDIKQrsD6JBCcrUTKibt90nzcFMiIiIiqi6GoDpSDBjhrhB22MJkTFXHU2pxbQHJRM0OsqZuW3oTERERUTwwBNUZkzFAN1wQGrEwTZUPQpqPwo+Ba3HdHO33iVnTBiIiIiKqTwxBdcjkDNAFBHsC6JhWpOxM1ZW86ZjCJA1MRxR+0tzvQ0RERES1hSGoTkmLwIQGdq8FDMpWgqY2WvUpuHI802XgNbPFNRERERHVLoagOiUi8NrdGUJ2vwWyrovcXNFC1OxA3X4fr9NzJW9z+DWIiIiIiMqBIaiOiQi8Dg8IAXvQutbZJ3EOj6rr8KajCvEEpjk636eJ+32IiIiIKD4YguqcGIE3360IhYdDF1pmGVgmtbhORV3emg0kxf0+RERERBQ/DEENQDxXroYQCAeiIDSD8KJhtN8niFpcdxiYHFtcExEREVG8MQQ1CPHdGUJqFdqvQAuOGoQ0H638iGu5bVoNJCMnVUpHRERERFQrGIIaiCQFfrePYFcAHVRI83ioUVVgDLCjFibhgk9pvw9L3oiIiIiojjAENRhJCbxuzx2mOmQhTeJWfQrRx7o8eDmvImcLERERERFVA0NQAzJN7jDVcHfoVoSaXPMEtrgmIiIiokbAENSgTM4APSid88MW10RERETUKBiCGpjJmmoPgYiIiIio4ngVTEREREREDYUhiIiIiIiIGgpDEBERERERNRSGICIiIiIiaigMQURERERE1FAYgoiIiIiIqKEwBBERERERUUNhCCIiIiIioobCEERERERERA2FIYiIiIiIiBoKQxARERERETUUhiAiIiIiImooDEFERERERNRQGIKIiIiIiKihMAQREREREVFDYQgiIiIiIqKGwhBEREREREQNhSGIiIiIiIgaCkMQERERERE1FIYgIiIiIiJqKAxBRERERETUUBiCiIiIiIiooTAEERERERFRQ/GrPYCToaoAgP7+/iqPBCgUChgeHkZ/fz8SiUS1h1N3OL/lxzkuL85veXF+y4vzW16c3/Li/JZXLc1vMRMUM8KxxDoEDQwMAACWLFlS5ZEQEREREVEtGBgYQGtr6zFvIzqTqFSjrLV45ZVX0NzcDBGp6lj6+/uxZMkSvPTSS2hpaanqWOoR57f8OMflxfktL85veXF+y4vzW16c3/KqpflVVQwMDGDhwoUw5ti7fmK9EmSMweLFi6s9jElaWlqqfgeoZ5zf8uMclxfnt7w4v+XF+S0vzm95cX7Lq1bm93grQEVsjEBERERERA2FIYiIiIiIiBoKQ9AcSaVSuO2225BKpao9lLrE+S0/znF5cX7Li/NbXpzf8uL8lhfnt7ziOr+xboxAREREREQ0W1wJIiIiIiKihsIQREREREREDYUhiIiIiIiIGgpDEBERERERNRSGoFn43Oc+h+XLlyOdTuPCCy/Er371q6PedsOGDfizP/szLF++HCKCT3/605UbaEzNZn6/8pWv4LWvfS3a29vR3t6OK6644pi3p9nN7/3334/zzz8fbW1tyGazOOecc/D1r3+9gqONp9nM8UT33nsvRAR//Md/XN4Bxtxs5vfuu++GiEx6SafTFRxt/Mz2/nv48GHcdNNN6OnpQSqVQm9vL773ve9VaLTxM5v5vfzyy6fcf0UE//7f//sKjjheZnv//fSnP401a9agqakJS5YswXvf+16Mjo5WaLTxM5v5LRQKuOOOO7By5Uqk02mcffbZ+P73v1/B0c6Q0ozce++9mkwm9Wtf+5pu2LBB3/GOd2hbW5vu2bNn2tv/6le/0ltuuUXvueceXbBggX7qU5+q7IBjZrbze9111+nnPvc5ffbZZ3XTpk369re/XVtbW/Xll1+u8MjjYbbz+5Of/ETvv/9+3bhxo27fvl0//elPq+d5+v3vf7/CI4+P2c5x0c6dO3XRokX62te+Vq+99trKDDaGZju/d911l7a0tOiuXbtKL7t3767wqONjtvM7Njam559/vl599dW6bt063blzp/70pz/V5557rsIjj4fZzu+BAwcm3Xeff/559TxP77rrrsoOPCZmO7/f+MY3NJVK6Te+8Q3duXOn/uAHP9Cenh5973vfW+GRx8Ns5/fv/u7vdOHChfrd735Xd+zYoZ///Oc1nU7rM888U+GRHxtD0AxdcMEFetNNN5XeDsNQFy5cqP/wD/9w3M9dtmwZQ9BxnMz8qqoGQaDNzc36v//3/y7XEGPtZOdXVfVVr3qV/v3f/305hlcXTmSOgyDQiy++WP/X//pfesMNNzAEHcNs5/euu+7S1tbWCo0u/mY7v1/4whd0xYoVms/nKzXEWDvZx+BPfepT2tzcrIODg+UaYqzNdn5vuukmff3rXz/pfTfffLNecsklZR1nXM12fnt6evSzn/3spPf96Z/+qV5//fVlHedssRxuBvL5PJ5++mlcccUVpfcZY3DFFVfgF7/4RRVHVh/mYn6Hh4dRKBTQ0dFRrmHG1snOr6rikUcewZYtW3DZZZeVc6ixdaJzfMcdd6Crqwv/+T//50oMM7ZOdH4HBwexbNkyLFmyBNdeey02bNhQieHGzonM74MPPoiLLroIN910E7q7u3HGGWfg4x//OMIwrNSwY2Mu/sZ99atfxVve8hZks9lyDTO2TmR+L774Yjz99NOlkq4XXngB3/ve93D11VdXZMxxciLzOzY2NqX8uKmpCevWrSvrWGfLr/YA4mD//v0IwxDd3d2T3t/d3Y3NmzdXaVT1Yy7m933vex8WLlw46ZeUnBOd376+PixatAhjY2PwPA+f//znceWVV5Z7uLF0InO8bt06fPWrX8Vzzz1XgRHG24nM75o1a/C1r30NZ511Fvr6+vDJT34SF198MTZs2IDFixdXYtixcSLz+8ILL+DHP/4xrr/+enzve9/D9u3b8a53vQuFQgG33XZbJYYdGyf7N+5Xv/oVnn/+eXz1q18t1xBj7UTm97rrrsP+/ftx6aWXQlURBAH+6q/+Cv/9v//3Sgw5Vk5kfq+66irceeeduOyyy7By5Uo88sgjuP/++2vuSRKuBFHsfeITn8C9996Lb3/729z4PIeam5vx3HPP4cknn8THPvYx3HzzzfjpT39a7WHVhYGBAbz1rW/FV77yFcyfP7/aw6lLF110Ed72trfhnHPOwete9zrcf//96OzsxJe+9KVqD60uWGvR1dWFL3/5yzjvvPPw5je/Gbfeeiu++MUvVntodeerX/0qzjzzTFxwwQXVHkrd+OlPf4qPf/zj+PznP49nnnkG999/P7773e/iIx/5SLWHVhc+85nPYPXq1Vi7di2SySTe/e5348Ybb4QxtRU7uBI0A/Pnz4fnedizZ8+k9+/ZswcLFiyo0qjqx8nM7yc/+Ul84hOfwI9+9COcddZZ5RxmbJ3o/BpjsGrVKgDAOeecg02bNuEf/uEfcPnll5dzuLE02znesWMHfvvb3+JNb3pT6X3WWgCA7/vYsmULVq5cWd5Bx8hcPAYnEgm86lWvwvbt28sxxFg7kfnt6elBIpGA53ml95166qnYvXs38vk8kslkWcccJydz/x0aGsK9996LO+64o5xDjLUTmd8PfvCDeOtb34r/8l/+CwDgzDPPxNDQEN75znfi1ltvrbmL9Wo6kfnt7OzEd77zHYyOjuLAgQNYuHAh3v/+92PFihWVGPKM8ac8A8lkEueddx4eeeSR0vustXjkkUdw0UUXVXFk9eFE5/cf//Ef8ZGPfATf//73cf7551diqLE0V/dfay3GxsbKMcTYm+0cr127FuvXr8dzzz1XernmmmvwB3/wB3juueewZMmSSg6/5s3FfTgMQ6xfvx49PT3lGmZsncj8XnLJJdi+fXspvAPA1q1b0dPTwwB0hJO5/37zm9/E2NgY/tN/+k/lHmZsncj8Dg8PTwk6xUCvquUbbAydzP03nU5j0aJFCIIA3/rWt3DttdeWe7izU+XGDLFx7733aiqV0rvvvls3btyo73znO7Wtra3UcvWtb32rvv/97y/dfmxsTJ999ll99tlntaenR2+55RZ99tlnddu2bdX6FmrabOf3E5/4hCaTSf1//+//TWojOjAwUK1voabNdn4//vGP68MPP6w7duzQjRs36ic/+Un1fV+/8pWvVOtbqHmzneMjsTvcsc12fm+//Xb9wQ9+oDt27NCnn35a3/KWt2g6ndYNGzZU61uoabOd3xdffFGbm5v13e9+t27ZskUfeugh7erq0o9+9KPV+hZq2ok+Plx66aX65je/udLDjZ3Zzu9tt92mzc3Nes899+gLL7ygDz/8sK5cuVL/4i/+olrfQk2b7fw+8cQT+q1vfUt37Nihjz32mL7+9a/XU045RQ8dOlSl72B6DEGz8D//5//UpUuXajKZ1AsuuECfeOKJ0sde97rX6Q033FB6e+fOnQpgysvrXve6yg88JmYzv8uWLZt2fm+77bbKDzwmZjO/t956q65atUrT6bS2t7frRRddpPfee28VRh0vs5njIzEEHd9s5vc973lP6bbd3d169dVX19wZFbVmtvffxx9/XC+88EJNpVK6YsUK/djHPqZBEFR41PEx2/ndvHmzAtCHH364wiONp9nMb6FQ0A9/+MO6cuVKTafTumTJEn3Xu95VcxfptWQ28/vTn/5UTz31VE2lUjpv3jx961vfqr///e+rMOpjE1Wu+xERERERUePgniAiIiIiImooDEFERERERNRQGIKIiIiIiKihMAQREREREVFDYQgiIiIiIqKGwhBEREREREQNhSGIiIiIiIgaCkMQERERERE1FIYgIiJqWD/96U8hIjh8+DAA4O6770ZbW1tVx0REROXHEERERGXz0ksv4S//8i+xcOFCJJNJLFu2DH/7t3+LAwcOVHwsl19+Od7znvdMet/FF1+MXbt2obW1teLjISKi6mEIIiKisnjhhRdw/vnnY9u2bbjnnnuwfft2fPGLX8QjjzyCiy66CAcPHqz2EJFMJrFgwQKISLWHQkREFcQQREREZXHTTTchmUzi4Ycfxute9zosXboUb3jDG/CjH/0Iv//973HrrbcCAEQE3/nOdyZ9bltbG+6+++7S2+973/vQ29uLTCaDFStW4IMf/CAKhULp4x/+8Idxzjnn4Otf/zqWL1+O1tZWvOUtb8HAwAAA4O1vfzseffRRfOYzn4GIQETw29/+dko53HQeeOABnHvuuUin01ixYgVuv/12BEEwZ/NERESVxxBERERz7uDBg/jBD36Ad73rXWhqapr0sQULFuD666/HfffdB1Wd0b/X3NyMu+++Gxs3bsRnPvMZfOUrX8GnPvWpSbfZsWMHvvOd7+Chhx7CQw89hEcffRSf+MQnAACf+cxncNFFF+Ed73gHdu3ahV27dmHJkiXH/bo/+9nP8La3vQ1/+7d/i40bN+JLX/oS7r77bnzsYx+b4UwQEVEtYggiIqI5t23bNqgqTj311Gk/fuqpp+LQoUPYt2/fjP69v//7v8fFF1+M5cuX401vehNuueUW/N//+38n3cZai7vvvhtnnHEGXvva1+Ktb30rHnnkEQBAa2srkskkMpkMFixYgAULFsDzvON+3dtvvx3vf//7ccMNN2DFihW48sor8ZGPfARf+tKXZjRuIiKqTX61B0BERPXreCs9yWRyRv/Offfdh//xP/4HduzYgcHBQQRBgJaWlkm3Wb58OZqbm0tv9/T0YO/evbMf9AS//vWv8fOf/3zSyk8YhhgdHcXw8DAymcxJ/ftERFQdXAkiIqI5t2rVKogINm3aNO3HN23ahM7OTrS1tUFEpoSlift9fvGLX+D666/H1VdfjYceegjPPvssbr31VuTz+Umfk0gkJr0tIrDWntT3MTg4iNtvvx3PPfdc6WX9+vXYtm0b0un0Sf3bRERUPVwJIiKiOTdv3jxceeWV+PznP4/3vve9k/YF7d69G9/4xjdw0003AQA6Ozuxa9eu0se3bduG4eHh0tuPP/44li1bVmqkAAC/+93vZj2mZDKJMAxn9TnnnnsutmzZglWrVs366xERUe1iCCIiorL47Gc/i4svvhhXXXUVPvrRj+KUU07Bhg0b8N/+239Db28vPvShDwEAXv/61+Ozn/0sLrroIoRhiPe9732TVnVWr16NF198Effeey9e/epX47vf/S6+/e1vz3o8y5cvxy9/+Uv89re/RS6XQ0dHx3E/50Mf+hDe+MY3YunSpfjzP/9zGGPw61//Gs8//zw++tGPznoMRERUG1gOR0REZbF69Wo8+eSTWLFiBf7iL/4Cy5Ytwxve8Ab09vbi5z//OXK5HADgn//5n7FkyRK89rWvxXXXXYdbbrll0l6ba665Bu9973vx7ne/G+eccw4ef/xxfPCDH5z1eG655RZ4nofTTjsNnZ2dePHFF4/7OVdddRUeeughPPzww3j1q1+N17zmNfjUpz6FZcuWzfrrExFR7RCdaX9SIiKik3TbbbfhzjvvxA9/+EO85jWvqfZwiIioQTEEERFRRd11113o6+vD3/zN38AYFiQQEVHlMQQREREREVFD4VNwRERERETUUBiCiIiIiIiooTAEERERERFRQ2EIIiIiIiKihsIQREREREREDYUhiIiIiIiIGgpDEBERERERNRSGICIiIiIiaigMQURERERE1FD+P0ERrX21FCRcAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['figure.figsize'] = 10., 7.5\n", "fig, ax = plt.subplots()\n", "ax.grid()\n", "\n", "ax.plot(df['Quantile'],df['DML LQTE'], color='violet', label='Estimated LQTE')\n", "ax.plot(df['Quantile'],df['LQTE'], color='green', label='True LQTE')\n", "ax.fill_between(df['Quantile'], df['DML LQTE pointwise lower'], df['DML LQTE pointwise upper'], color='violet', alpha=.3, label='Pointwise Confidence Interval')\n", "ax.fill_between(df['Quantile'], df['DML LQTE joint lower'], df['DML LQTE joint upper'], color='violet', alpha=.2, label='Joint Confidence Interval')\n", "\n", "plt.legend()\n", "plt.title('Local Quantile Treatment Effects', fontsize=16)\n", "plt.xlabel('Quantile')\n", "_ = plt.ylabel('LQTE and 95%-CI')" ] } ], "metadata": { "kernelspec": { "display_name": "dml_dev", "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": "3.12.8" } }, "nbformat": 4, "nbformat_minor": 0 }