{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-654bd336ff003835", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# You must run this cell, but you can ignore its contents.\n", "\n", "import hashlib\n", "\n", "def ads_hash(ty):\n", " \"\"\"Return a unique string for input\"\"\"\n", " ty_str = str(ty).encode()\n", " m = hashlib.sha256()\n", " m.update(ty_str)\n", " return m.hexdigest()[:10]" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-7f7c9f7f55486e9b", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "# numpy" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-e630f56f923f6053", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-da0a17e5d1fbe62c", "locked": true, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "source": [ "## Q1 Create a 1 dimensional numpy array named `x` with 20 elements from 0 to 19.\n", "\n", "Hint: use the `arange` function of numpy." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-1502b7b220c53c56", "locked": false, "schema_version": 3, "solution": true, "task": false }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n", " 17, 18, 19])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(20)\n", "x" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-f67a7b35435b525c", "locked": true, "points": 0, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(x)=='7a150607a7'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-64d2210f9a71e242", "locked": true, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "source": [ "## Q2 Create a 1 dimensional numpy array named `x` with 20 elements from 0 to 38 with an increment of 2.\n", "\n", "Hint: use the `arange` function of numpy." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-cff3a5e50b06014a", "locked": false, "schema_version": 3, "solution": true, "task": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38]\n" ] }, { "data": { "text/plain": [ "'8bc0a3b6c4'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(0, 40, 2)\n", "print(x)\n", "ads_hash(x)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-64723536c462a2d1", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false }, "tags": [] }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(x)=='8bc0a3b6c4'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-f439954fdaef75da", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Q3 Create a 2 dimensional numpy array named `x` of shape 5,6." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-15f8e74190eecaf8", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "# Type your answer here and then run this and the following cell.\n", "x = np.arange(30)\n", "x.shape = (5,6)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-38a672b790c362eb", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(x.shape)=='6510380315'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-681527231f43677f", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Q4 Consider the first 100 integers starting with 0. What is their mean value? Put this in a variable `mean100`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-34f66fe8d8b001fb", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "mean100 = np.mean(np.arange(100))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-9ef246cf9714c370", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(round(mean100*1000))=='bc8d4a4d1e'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-b4c0d9ce0a0e3ec7", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Q5 Create an array named `x` with every 4th value of the first 100000 integers." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-6b4ee0992aa506c5", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "data": { "text/plain": [ "array([ 0, 4, 8, ..., 99988, 99992, 99996])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.arange(100000)[::4]\n", "x" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-6833bfed10c7430c", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(len(x))=='0812a4ef4e' \n", "assert ads_hash(x)=='17e101b2ef'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-34c0aed2a2c992a4", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Q6 set the shape of `x` to be (5, 5000)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-e07f245d339e6328", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "x.shape = (5, 5000)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-190e11601c41f920", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(len(x))=='ef2d127de3' \n", "assert ads_hash(x)=='3331e0302b'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-1802e3087f27de21", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Q7 Create the variable `y` from the first 3 rows and the first 8 columns of `x`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-ef33bd87060e2253", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [ { "data": { "text/plain": [ "array([[ 0, 4, 8, 12, 16, 20, 24, 28],\n", " [20000, 20004, 20008, 20012, 20016, 20020, 20024, 20028],\n", " [40000, 40004, 40008, 40012, 40016, 40020, 40024, 40028]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = x[:3,:8]\n", "y" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-38599e5840dfe50f", "locked": true, "points": 0, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(len(y))=='4e07408562' \n", "assert ads_hash(y)=='b39d640ca0'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-c760334b1b549d83", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Plotting numpy arrays with matplotlib\n", "\n", "Let's start plotting numpy arrays. The `plot()` function in matplotlib's `pyplot` module works differently depending on the number of arguments it was called with. Like your `get_2D_coordinates()` function above, if only one argument is used, it is treated as the `y` coordinates. The `x` coordinates are the integers starting with `0`. If two arguments are used, they are treated as the `x` and `y` coordinates.\n", "\n", "Here we are also going to label our axes to keep track of what we are plotting. We will use the `plt.xlabel()` and `plt.ylabel()` functions." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-022fd8735aa958ba", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-efcd917dafccd54c", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "We are going to generate some \"toy\" (artificial, false, fake, made-up) positions of a bee over time. First, we need the times where we have tracking data. We are going to track the bee for 10 seconds. Over these 10 seconds, we have 100 measurements." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-3acd919556155f8f", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "time_values = np.linspace(0,10,100)\n", "plt.plot(time_values)\n", "plt.ylabel('time (seconds)');" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-54aa9b963835b42f", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Now we will get the `y` coordinate of the bee over these 10 seconds. Instead of real data, we generate toy data by using the `np.sin()` function. We take the sine of the time value as the `y` coordinate of the bee." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-d8eefc7a088ab564", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "y = np.sin(time_values)" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-44143dbd92ac071d", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Now let's plot the `y` coordinate of the bee over time." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-e1a3bbf4c1b1618c", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(time_values,y)\n", "plt.xlabel('time (seconds)');\n", "plt.ylabel('y (meters)');" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-de601837bd41103a", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "So our bee was oscillating in `y` over time.\n", "\n", "In addition to a `y` coordinate, our tracking system also gives us `x` coordinates. Let's make some toy data for this, too." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-9a46e6754a91f190", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "x = 0.5*np.sin(time_values*3.2)+1.1" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-153b385adbe59ec2", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(time_values,x)\n", "plt.xlabel('time (seconds)');\n", "plt.ylabel('x (meters)');" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-d5670c1510ca17ff", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "So the bee was also oscillating in `x`, and even faster than in `y`." ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-65c1964e62b88a11", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Storing multiple dimensions data in single array\n", "\n", "We might want to store our coordinates in a single multi-dimensional numpy array where the columns are time and the rows are the different coordinates (`x`, and `y`).\n", "\n", "```\n", "x0, x1, x2, ..., xn\n", "y0, y1, y2, ..., yn\n", "```\n", "\n", "We can use the `np.vstack()` (\"vertical stack\") function for this. It will return a new array whose rows are the given in its input argument." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-6e94a8443b27f2bf", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "bee_coords = np.vstack((x,y))" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-de3884dcd3459ef6", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "By doing these, we can keep the different measurements together. Of course, we can still plot just a single dimension. Here is `x` over time again. In this case, the x axis is simply the index of the array passed to `plot()`:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-5f3a313fa88f20be", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bee_coords[0])\n", "plt.ylabel('x (meters)');" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-765b4dd8839b4076", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Of course, we can also plot the time value as the x axis and label it correctly:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-b2b1ea1720798043", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(time_values, bee_coords[0])\n", "plt.xlabel('time (seconds)');\n", "plt.ylabel('x (meters)');" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-c92b5b7235df5958", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "If `x` and `y` are horizontal positions, we can plot a \"top view\" of the bee position by plotting x against y:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-8a08ee9348762caf", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "outputs": [ { "data": { "image/png": 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o+nCyz+14xIj4o+LJR1x5hZVGKkEiCSuCuCI69RYcb9HhWLMOx1oGuQkTvsxJlmJZngrLctVYmqtCojR43oSXA4mlIBIuYolhGFQ2DeKZjxtwvFU37j5iAR85mjjkaRJGo0QeYTRdbbczjcnqQI9htPi82zCCbuMItEYrtJ6i9KlGMFTxYqTIYtio1BghNROWCcSVMWxzYmDIhn6zjb0dsmPAbEP/kA0Dnm0DQ3b0m22X3YwQI+L7R6Y80SrfSJX3cRJWBBFIr8mKYy06HGsexLEWXYBZMgDkJcazkSdP6i5VHhuClQZCYimIhFosjSeSxEI+Fmcp/QRRviYB6crYqG8TZxjWx8QblerxiiiDFVqTJ1p1GR1+Sk/KL9Un5ZciG/05lQRVWOEVVl5x5RVWvmLriwqrgDSgT6SKfVw8YzYVBBHuDAzZcKJFh2MtOhxtHsSFXnNAvWqWKg7LclVYmqvC8jw1MpSxIfl7IbEUREIllhiGQWWzRyS1jIqk25dm4f6r85EijwnaWiINhmFgGnFyEakAYXWZlgnKOBFSPDVUKZ7U36jAYsVVrJgEVbjhL6zsYyJVXnFlx8CQ7bKL2n2FlV8aUCoZE8kiYUVENwaLHSda9TjWPIjjrTrUdRkDZgimyWOwLI9N2S3LVSFXEx+UvwkSS0EkFGLpSNMAiaQZxiuoenyiUVqjt45qVFxN9SSq8I1QcbVUvgIrlgRVGDNWWPlHqkajVVcirCRC/xqrRKl4TKTKK65IWBGRj9nqwMk2PY4163C8ZRCnO40Bg8XzE+Ox63urZrzLjsRSEAmmWGLTbRdxzCuSBHx8Y2kmvntNAYmkEMAwDExWJxeN4qJUhhFoTaNF6ZcjqNgaKrZeKlXGRqZSfaJVJKjCH4vdiQGzHf1DVk5YjU0Deu9fibDySwN6OgRT5DFIV8Sy/5SxZK9BRAwWuxPVbQYcbxnE0RYdatsNsLvc+OWW+bh9WdaMvjaJpSASDLE0kUi6/5r8sCmUI8aHYRiYbU70eCwSWGE1JkplGJmSvwnAGkF6xVO6kh09k66I9Zh7jo6dISKDUWE1NlLlnwYcMNum/BkBWOHt/Vz4iijv/cux/SCIYPL850341YfnsSJPjZ33LZ/R1yKxFERmUiwdbWZF0tFmEknRjsnq8ItMja2jmqptgoDP4zr80nxEVLoiBumKOKQpYiCNiUwDudmOr7Dy6w402zi7jS79yJTG34iFfI+YivET2xme21RFDCRCimISwadDZ8Hq33wGHg84tm0dkmQzlzWZ6vmb4rRhzCfnenH3X04CYEXS15dm4rskkqIWWYwIshgRipKlE+5j9giqbq8PlWEEXR77hC7DCHqMI3C4GHR57gP6cY8jjRH6nCBHRZR3WxJFp8KSOLEQWWohstRxk+5nsjrQpR9hbTUMI+g0sF5lXXoLug1W9JqtsDvdaPEMuZ6IRKnEPyolj0G6kv2sZCjiaDQRMSNkquJQlqVATbsBu8/0YOuq3FAvicRSOKNJkEAs4MPucmNRpgKPbpobNfN4iCtDGiOCNEaEwgkEldvNYGDIhi7vydFg8dyOnjj1FgfMVifOa804rzWPexxvdMobeUhT+Kf70pWxSKDPYtgiixFBlirCvNTxr5TtTtZNv9MjqLyfjy6fn60ON2u9YLahtsMw7nHixQIutTdeuo9SwsSV8pUFaahpN+Cfp8NDLFEabhqYyTTcoYYB3Pf6SVjsLpRlKfDK1iURYx5JhCcWu9NzYrRyAqrLk77xWiaM7UwZD1mMMKBeKl3JpvvY6FRM1Ht6RSsMw0A3bOcEd5fBykWqvGJqcAqjicaKbt+aKe/nhi4AifHoNVmx/KlPwDDAkceuRZpiZjIqVLMURGa6wLumXY+tr5yAccSBOclSvH730hnN4RKzG5dPdMo3leOb7jOOOC55HCGfh2TPiZI9SQZGqCg6FbmM2F3oNo5wdVKj6T5PSniKolsRJ0Ka3Cu0/aOXaYoYaOIl4JPonpXc9kIljrXo8P+un4d71+TNyGuQWAoiweiGu6A1486XjqHPbEOWKg5v3L3sknULBDFTDNmcnnqp0bl9vikcrXFqJ0p5rIgrPvcvRmf/JUolFJ2KUFxuBv1mm9/nwiusvNvM1qkVoqd5Oz/loyk+KkSPbi5ozXj83TM40arHkhwl/n7/yhl5HRJLQSRYPksdOgu++dIxtA1akCSV4PW7l2FOysTFwAQRKkZPlBa/dF+3YYSrkzFN4UQp5PPYOX1jUje+xeiUxolcTFbHGBE1Wl/XpR9Br9kaMCZjPBKlEh8BxX42stRxKEySIl0RS5GpCIFhGBxpGsSOA834/GI/t33ryhz87IaSGXlNEktBJJimlH0mK/7t5eM4rzVDHivCK3ctQXmWckZfkyBmArOVnd83Nt3nLUjXmqxwTTE6NeonxAqrTFUcslRxyFLHQUY2CRGLw+WG1ugvoLqNI36F6Zcamh0nFqAgKQFFyVIUJSegMFmKomQp0uQx1MkXJjhcbuw+04MdB5pR320CAPB5wObSVNyzOhdlM3iOI7EURII97sRoceCuV4+jut2AWJEAO/5tMVYXJs746xJEMHG63Ogz23yKiq0Y27k1lTSOMk7kEU7xyFLFIlsVz4opdRxSZFSEHskwDAO9xcGl9nybFVoHh9HcPwy7a3wxlSARekQUK6QKPWIqRUYiKliYrQ68daIDLx9qQbfRCgCIFQlwa0UG7r4qLyilJiSWgkgoZsNZ7E585/UqHGwYgEjAw6Ob5uKGhWlU+E3MKkxWB3p8Ora80Yd2nQUdOsslO7bEAj4ylGzKJkvl889zn0aGRDZOlxutgxY09JpxsXcIF/vMaOg1o7l/eMKaOmmMEIVJ/gKqKFmKJKmERNQ0oTVa8crhFvztWDtnoKpJkGDrymzcsSwbyvjgdXyTWAoioRBLAOuV8vBbtfjXmR4AAI8HlGcpsbEkGRtLUpCtjg/aWggiHDFbHejQjaBdN4x2nQVtgxZOSHXqRy5ZhK5JkCB7HCGVrYpDIp08Ixa7043WwWFc9IgoVkyZ0TpomTD1K4sRBgiowuQEJCbQ52CqnOsx4c8Hm/F+bTf3t5efGI/71uThxkXpiBEFv1CfxFIQCZVYAthC2tcqW/FebXeAcdzcFCk2lKRgY0kyilNl9AdNED44XW70GK1o11k4IdXB/Tx8yQL0GBHfR0SxKT42IhWPDGVsSL74iS+GzelCy8Cwn4Bq6B1C6+AwJtLVijgRipJY4eQVUHOSpVAnSIK7+DCFYRgcahzAjgPNONgwwG1flqvCfWvysHZOUkgL8EksBZFQiiVftEYr9p3VYk+9FkebdX5XSJmqWGwoTsHGkhQszlZSnQZBXAKjxYE2T0SqXWdB++CoqOoxjkx48gTYKG+KLAaZKjYK5Zvay1LFQRVPQ2wjCavDheb+YTT0mf2iUW06y4Tdeup4sY+AkqLIk9oLZooplDhcbvzzdDd2HGjBuZ7Rou3r56fi3tV5WJipCO0CPZBYCiLhIpZ8MVjs+ORcHz6q1+JAQ79fx4gmQYzripOxoSQFK/PV5FFCEJeJ3elGl2HER0j5p/ksdtekz0+QCEeFlDrOT1SlK2MhohEhEYHV4UJj35BHRA1xtVEd+olFlCZB4pfGK0qWoihJCnlcdHRtmqwOvHm8Ha8cbkWPp2g7TizArRWZuPuqXGSqwssfkMRSEAlHseTLiN2Fzy/2Y2+9Fh+f6/VLLyRIhFg7NwkbS5JxzZwkclQmiC8IwzAYHLZztVFeAeWNTGlN1kmfz+cBaYpYrlaKFVLxXHRKHhsdJ9VoxmJ3oqnPUxPVZ8ZFLSui2OHW45MklfgLKI/NQSRYX3ToLKhsGsSRpgF8fK4PQ56i7USpBFtX5uCOZVlhO6aLxFIQCXex5IvD5caxZh0+qtfio3ot+sw27jGxkI+rCjTYWJKM9fOSKedOEDOA1eFCp97iF4nyCql2nQU25+S+QfJYEbLHRKO8QipVHksp9jBm2OZEQ9+QpxZqNBrlbZsfjxRZTICAKkxKgDSEIqrXZOXEUWXzIDp0/iKwMCkB967Jw42L0sI+c0FiKYhEkljyxe1mUNtpwEf1Wuyt70XLwDD3GJ8HLMlRYWNJCjaUJCNDGV6hU4KIRtxuBv1DtjFCarRuamBocisEkYCHDKW/kMpUxXFRKnI7D0/MVgca+kbTeN7C8smikGnyGBSnyXFVgRprihKRq4mfsTo43bAdR5sHOYHU1D/s97iQz8OiTAVW5quxskCDpTmqiHFNJ7EURCJVLPnCMAwa+obwUZ0WH53Voq7L5Pd4aboMG4tTsLE0BYVJCVScShAhYNjmRId+tHOvzSci1am3wOG6lBWCGJmqOORq4lGYJOX8hDKUNBIkHDGOONDY5y+gLvaa/TICXtIVsVhTlIg1hRqszNd8oRook9WBEy06HGkaxJGmQa5A2wuPB8xPl2NFnhor8tVYkqOKWCFOYimIRINYGkun3oK99b3YU6/FyVadX+dPriYeG0qSsakkBQszFPQlSxBhgMvNQGuyom1w2E9IdegsaNNZYLA4JnxujIiP/ERWOBUkJXAiKlMVR2m9MMRgsaOhbwgnW/U42NCPk616P6dyPg9YlKnA6sJErCnSYGGGAsJJmgZG7C6cbBsVR2c6DQHdnnOSpViRr8bKfDWW5aqjpyCdxFLwiEax5MvgkA0fn+vFR/W9ONQw4PdHmSyT4LriZCzLVWNxthJpitgQrpQgiIkwjjg4EdXcP8TVzkw2EkQs9IooVkAVJLF1M1mquElPvkRwsdidONasw4GGfhxsGEBj35Df49IYIVbla7CmKBGrCzVIkklQ227AkaZBVDYPoqZdHxCVzNXEc+JoeZ4amiitYSWxFESiXSz5MmRzYv+FPnxU34vPzo92PXhJkcWgPFuB8iwlyrOVKEmThX2BH0HMZpwuNzr0I7jYa2bb4D11M039QxMWm4sFfOQlxnPFxkXJrJDKVseR7UEY0GUYwaGGfhy4OIBDjQMwjkwcVfSSJo/BinwNVuazqbXZcuEbtWJp+/bt+O1vf4uenh6UlJTgmWeewerVq8fdd+vWrfjLX/4SsL24uBj19fUAgFdffRV33XVXwD4jIyOIiZnanLXZJJZ8sTldONI0iP3n+1DdbsDZHlPAqACxgI/SdBnKs5RYnM0KqGSaX0cQYY/LzaBTb2E7tvrYepmGPlZQ+fq2+SIS8JCnSUBB8mgqrzApAdnqeIiFJKKChdvN4LzWjCNNrFjaf6F/0v1T5TH45vJsrClMREmabFaVVkSlWHrrrbdw5513Yvv27Vi1ahVeeOEFvPjiizh79iyysrIC9jcajRgZGW1pdDqdWLhwIX74wx/iZz/7GQBWLD344IO4cOGC33NTUlKmvK7ZKpbGYrE7cbrTiOp2ParbDKhp1487yDRdEYuyLAUrnrKUKE6T0dUoQUQIbjeDTv0IK6A8qTw2IjWEEcf4ZpxCPo8tKk8eTeUVJkmRqyERNR0wDIOm/mFUNg3gSNMgjjYPQj+mRk0WI8SKfDXmp8sBAD1GKw42DKBdZ/HbTxknwlWFbKH46sJEpMij++I2KsXSsmXLUF5ejueee47bNm/ePNx000146qmnLvn8d999FzfffDNaWlqQnZ0NgBVLDz30EAwGwxWvi8TS+DAMg7ZBCyue2vWoajPggtYUUDgoEfKxIEOOco94Ks9SIlEanflxgohW3G4GXYYRf0frviE09poxPIGjuYDPQ446DoUeAVXgiUTlJcZT+v4SdOgsOOIRR5VNgwEdcnFiAZbmqth2/nwN5qXKxi3WbxscxoGGARy42I/KpsGA0oqi5ASsKUzE6qJELMtVRd3Mw6gTS3a7HXFxcfj73/+OLVu2cNsffPBB1NbW4vPPP7/kMb7yla/AZrNh79693LZXX30V99xzD9LT0+FyubBo0SL84he/QFlZ2YTHsdlssNlGP5gmkwmZmZkklqbAkM2J0x0Gj3jSo6bDMG6XTpYqDuVZCk5AzU2RUkEpQUQgDMOg22hFQ+9oKq/BE4kae2L2wucBOep4FCSNjgUpSEpAfmJC1J2sp4rWaMXR5kFOIHXq/Y0gxUI+KrKVnpojDRZkyC87Yu9wuVHbYcCBi/040DCA050Gv7EtYiEfy3JVWF3IFovPSZZGvI1M1Iml7u5upKen4/Dhw1i5ciW3/Ze//CX+8pe/BKTRxtLT04PMzEz87W9/w6233sptP3r0KBobGzF//nyYTCY8++yz2L17N06dOoXCwsJxj/Wzn/0MTzzxRMB2EkuXD8MwaB4YRnWbnkvfXewzB8xVihUJsDBTztU+lWUpoZolAykJIhphGNbqwOsdxEak2J/N1olFVJYqbjSV50nn5ScmIFYcPSJKN2zH6U4DTncaPf8MAZEjrxHkCk9BdnmWctqFpH7YjsNNAzh4cQAHGvq5WW9ekqQSXFWowdVFiVhVoInIjrmoFUtHjhzBihUruO1PPvkkXn/9dZw/f37S5z/11FP43e9+h+7ubojFE59k3W43ysvLsWbNGvzhD38Ydx+KLM0sJqsDpzoMqGrTo7qdrX0a78szVxOPsiwFJ6CKkqXkCUMQEQ7DMOgz2zgR1dA3xBkzTtTVxeMBmco4rivPW1yenxSPOHF4myWarA7UdRpxusvICaSxUSOAFYrFaTKsyteExAiSrYsawgGPcDraPBhQ6F+SJuPsCSqyVRFRjxZ1YumLpOEYhkFRURG+/OUv4+mnn77ka917773o7OzEhx9+OKW1Uc3SzOJ2M2jsHxqNPrUbAnxEAHYo8MJMORZnKVGWrUR5pjJqjNMIYrbDMOwomEYfEeUdETK2mNmXDGUs15XnTesVJCWExHF6xO5CfbcRpzqNOOMRRs0Dw+Pum6eJx/wMORZkKLAgQ46SNFlYCT+b04WTrXrW2+niAM6OcfmOEwuwoTgZ//mleUiShm+ReNSJJYAt8F68eDG2b9/ObSsuLsaNN944aYH3/v37sXbtWpw5cwalpaWTvgbDMFi6dCnmz5+Pl19+eUrrIrEUfAwWO2o6DKhp06OqXY/adsO4RaT5ifFc1115thIFiQmzqi2WIKIdhmEwOGz368rz/jxeN66XdEUsCpMTMDdF5vmOUEzr8HCb04ULWrOfMLrYaw5ocPGuZWGmHPPTFViYIUdJuhzy2Mi60OszW3G40ZuyG8DAEJt9kceK8NOvFGNLWXpY1jdFpVjyWgc8//zzWLFiBXbs2IE///nPqK+vR3Z2NrZt24auri689tprfs+788470dDQgKNHjwYc84knnsDy5ctRWFgIk8mEP/zhD3j99ddx+PBhLF26dErrIrEUelxuBhd7zaOF4+0Gv8HAXqQxQpRlsV+M5VlKLMpSQBbC6d0EQcwcg0M2LgLV2Dvaoec9kY/Fm9pfnM2m9guTppbad7rcaOgbwplOI051GnCmy4jzPeZxndETpRIszGCF0YJMORaky6dVpIUD3iHtP32vHme6jACAa+cm4cktpUiVh5fZZVSKJYA1pfzNb36Dnp4elJaW4umnn8aaNWsAsCaUra2t2L9/P7e/0WhEamoqnn32Wdx7770Bx3v44Yexa9cuaLVayOVylJWV4Wc/+5lfXdSlILEUnuiG7ajxiKfqdj1OdRgDfGB4PKAoSYrybAUnonI1CVT7RBBRjH7YzhWTn+k0oqpdP2Fq31sXWZ6tRFmWAgliIVoGh/0KsOu7jeMadSriRJifLsfCDAXmZ7C3yTJJWEZYZgKny40XDjTj2Y8bYHe5IZUI8fiX5+HWisyw+R1ErVgKR0gsRQZOlxvntWZP1x1b+zTWkA1gc+3FqTKUpstRnCZDaZochckJZJxJEFGMN7Vf3cZeYNV2GGCZwB9qPBIkQpSmy7gaowXpCmSqYsNGFISSxj4z/v0fp1HTbgAArC7U4Kmb5yNDGRfahYHEUlAhsRS59JttnGlmdZsedV2mcV2IxQI+5qZKUZLGFlqWpssxN0U6az1fCCIa6TVZuVb9mnYDDjUOTOl5Oeo4fHVxBipyVFiYoYgqG4PpwuVm8PKhFvzP3guwOd2IFwvw2PXzcMfSrJDWkZJYCiIklqIHl5tBy8AQ6rpMqOsyoq7biPpu07jWBQI+D4VJCVz0yRuJSghBlw1BEJeH18uIrTMy4kyXAb2mwFomkYCHuSkyzM+QI10RC4fLDbPViVMdBpzuMsI+ZtiwgM9DcaqMm4VZnqVAuoIiTF5aBobx6D9O43irDgCwPE+FX9+yANnq+JCsh8RSECGxFN0wDIMO3Qjquo0eAWVCfZdx3E4bHg/IVcezAipdjlJPJEpJBpoEETJMVgfqutj6Im8R9kReRoVJUk99kRzzMxSTRpDtTjfqu41cXWRVm35cwZUsk/h15ZakyWb1OBe3m8Frla349Z4LGHG4ECsS4N83zsHWlTlBjzKRWAoiJJZmHwzDoNdk46JPdV0m1HcbAxxuvaQrYrn0XWk6G4lKkoWv9whBRCpeLyNvOu10lxHN/eN7GeVq4rEgQ84WYWcqUJwq+0L+S97RLlVtes4Xrr7bBNcYvwCxkI8F6T7zMLMVYe1FNFO0D1rw2K7TONI0CACoyFbiN19dgLzEhKCtgcRSECGxRHgZHLKhvtvEpu+62Nu2wcAicgDQJEg44VSaLkNJmhwZSgrXE8RU8XoZccLoEl5GC3xMHkuD5GVksTtxutPI1UVWtenHNdHMVMVisc84p9kyD5NhGPzteDue2n0eQzYnJEI+HrmuCPeszgtKVzKJpSBCYomYDJPVgbPdbA1Uvee2qX9o3C90eayIi0B5b3PV8WSkScx6jCPs39HZHjaKe7bbhMa+ITjH+UMa62U0P10eNnPLGIZB66AFVW1eTzg9LvQGzsOMEwuwKFPhMw9TAUVc9Kbzuwwj2LbrDA5c7AcALMxU4H++ugCFydIZfV0SS0GExBJxuYzYXTinZWuf6rpMqO8x4oLWDIcr8M/R18rAK6AKksjKgIhOvAN267tGhVF9t2ncGiNg1MvIN2qUIouJqAityepAbbuBq3uqbTfAbAtsKilISkC5j2lmnia6JhIwDIO/V3XiF/88C7PVCbGAjwfXF+K+NXkz9n1HYimIkFgipgO7042LvWbU+9RAne0xjWt2JxbyMTeFtTLwpvLmkJUBEWF4u0/ru004221ib3tM0E0wpiRdEYviNBlK0ti0dXGaDGnyyBJGU8HlZtDQZ0Z1m4ErHh9vIoEsRojybCWXvluYqQjJzLvpRmu04v+9cwafnO8DwA7ofeHOxTPiy0RiKYiQWCJmCpebQXP/kF8ReX2XadyrTq+VQYlPDRRZGRDhgtXB1hfVd49eCJzvMY/raybg85CfGM/5mhWnylCcJovqNNSlGByyoabdgCpP9Ol0pyHgQorPw+isu2wF1hQmRuwoFYZh8F5tN37ybh3MNid+sLYAP944Z9pfh8RSECGxRAQTt5tBh97CekF1j9ZBjXc17rUyKEmXo9RzNU5WBsRMY7DYuUiRVxg19Q8HdIUBQKxI4DF8laE4lf18UpT00jhcbpzrMXkiT6zzeJfBP1Up5PNwdVEibi7PwLp5SRH3O3W43Fj2y0+gG7bjL99eiquLEqf9NUgsBRESS0So8dZ5eM00vSepyawMvOm7ErIyIK4Qb6t8veczd7aHTaeNPWl7UcWLWVHkiRaVpMmRq4mnWYzThNZo5eqejjYPor7bxD0mjRHiywvScHN5OiqylRGRuvz0fC++/epJaBLEOLpt3Yx0B5JYCiIklohwZcBrZdDFdg9NZmWQKJVw0SdvXUiWKi4ivlSJmcfpcqN5YJjrRPOKI8M4bfAA2wpfkir3qzGaTUNkw4HGPjN2VXfh3ZoudPtcOGWp4rClLB03l6eHzDl7Kjywswbvn+rG1pU5+NkNJTPyGiSWggiJJSKS8LZg13df2spAKhFinqdexHvCK0hKgFhInXjRjLdbkxNF3Uac15phcwY2Gwj5PBR4auW8n5N5qbKgeBgRU8PtZnC0ZRC7qrvw4ZkeDPsMCF6crcTN5en48vw0yOPC5/9s2OZExX9/jBGHC+9+fxUWZSpm5HVILAUREktEpGOxO3Gux8wVkJ/tMeGC1gy7a5xOPAEfhckJXOFtSboc81KpkDxS0Q3b/cTz2R4TmicQz/FiwbjiOdJqYWYzFrsT+8724u3qLhxq6Of+n8UCPtbNS8LN5Rm4uigx5BdE79Z04aG3apGtjsP+H18zYxFJEktBhMQSEY04XG409g0FFOqON1QYYCeve6ML3pPpbBzhEK4wDINO/QgXKfIKo4nq2jQJEq6+yCuMslVxUeXrM9vpNVnxXm0XdlV34bzWzG1XxYtxw8I0bClLx4IMeUhSp1tfOY79F/rxwLpCPHJd0Yy9DomlIEJiiZgtjJ5wjX7eOFrT+CfcRKlkNALl6cTLohPujONwudHUP4T6Lm9tEVtnZJpE6Bb71qulyqjgf5ZxttuEXdWdeLe2GwNDo8OA8xPjcXN5Bm4qS0e6IjYoaxkYsmHZLz+By83g0x9dPaOz4kgsBRESS8RsZ3DI5nFbHq1xaR4YDhjhAAAJEiHmpbKGml7/nKJkacjD/pHKsM2J81qTn3i90GuGfZz6IpGAh8IkqSdSJENxmhzzUqWQxoRPrQoRWpwuNw41DmBXdRc+qtdydWo8HrA8V42by9OxeX7qjKbd/3KkFT99vx4LMuR4/wdXzdjrACSWggqJJYIIxFsHxbaTs5Go89qJT+IFvidxj4iik7g/3u5Gb0fa2W4TWgYnFqXFPvVFxWkyFCaRKCWmjtnqwId1Wuyq7sTRZh23PUbEx8aSFNxcnoGrCjTTbv2wZfth1LQb8JMvF+Puq3Kn9dhjIbEUREgsEcTUcLrcaOr3bz+v7zZOmB7KVscFpPESpdHffs4wDHqMVtR1GVHX7Zkh2G1Er8k27v5JnnSnb0dappLSncT00am34N0atr6p2Wf0SpJUgpvK0rGlLB3zUr/4+a9tcBhX/3Y/+Dzg6LZ1M54OJrEUREgsEcSVwzAMugwjPik8NhLVPWHhsRjFPmMwStJkyFHHR6wwcLsZtOssfm7s9d3jz0fzOrKPFtGzqcxEaWSOtCAiD4ZhcKrTiF3VnXj/VLefz9a8VBluKU/HDYvSrri54w+fNOD3+y7iqgIN3rhn2XQte0JILAUREksEMf14W9rP9hg5ITVRS3ucp6W9xKdzqzA5ARJheLW0+xo7et3Wz3ZPPuuv1DOqptRj0RANg1KJ6MDudGP/hT7squ7CJ+d74XCxf5x8HrC6MBE3l6djQ3EKYsVT+ztkGAbrfv85mvuH8duvLsDXKjJncvkASCwFFRJLBBEcRuyu0WJmT0H5+R7TlM0Si9NkkAWpDsrudONir3lUGHUbca7HFDD8FADEQj7mpUg9M/zYQchFyTQfjYgc9MN2/PNMD96p7kR1u4HbniARYnMpW9+0LFc1YQS4rsuI3+29gM8u9EMi5OPk4+uDUrNIYimIkFgiiNDhdLnRMjDs5wVV333pMRwlPqmsLzqGw+t4Xd81Kowu9pq5K21f4sQCLvpV4okYFSQlQDQDc68IIhS0DAzjnepO7KrpQqd+dE5ghjIWv/3qQqzIV3PbGvuG8PS+i/jXmR4AbET1sU1zce+avKCslcRSECGxRBDhhXfAq58z9SQDXtXxYr86IG8d1HhdPiYrOy7GW1s02bgYeawIpen+wmii4xJEtOF2MzjZpseu6k7863QPzDYnJEI+XrhzMfITE/DsJw3YVd0JN8PW431lQRoevq4IuZrgzasjsRRESCwRRGRgsNj9hsDWdxvR1D8M1zhKJ04sQLIsBgzDcELI4XJP6nhdmi7j0mglaXJkKGOjvnOPIKbCiN2FH+6sxsfn+gIeWz8vGT/aUDQt3XSXy1TP31QpSBDErEERJ8bKAg1WFmi4bVaHC+e1Znx6vg//d6KDcyO32F1o8WmRHo/ripNxS3kGyrMU5HhNEJNgdbiQpQqMGL3zvZUoy1KGYEWXB4klgiBmFd6RLXUe76K6LjbCNDAU2Kp/Kfad7cXnF/oxN1WKBRlyLEhXYH6GHIVJCRBSDRJBwGx14KVDLXjxYAuGxnR98nlsfROJJYIgiBDicjOe4m8jK466JjbB5PPAjgLxpNBK0/xdxL1GkWe6jDjdacDpTiPOdBlhsDhwutOI051GAO0AWIfjkjQ55qfLsTBTjvnpCuRpItcLiiAuF6vDhdcqW/Hc/iboPc0W81Jl+PeNRbi6KAn/uesM3jrZgR/9/RRsTje+sTQrxCueHKpZmgaoZokgQo/D5UZD7xBr7uhxvj7XY4LF7grYVyzgoyglAaVpck+7vgxzU2RT9oPxwjAMOnQjON1lwBmPYKrrMo7rm5QgEaIkTYaFmQrMT5djQYYcWao4qmkiogq70423Tnbgj580oM/MOs7nJcbjkeuKcH1pKnfB4HYz+On79Xj9aBsA4IkbSvCtlTlBXy8VeAcREksEEVy8dUZsR5pn7lyPGXZXoIdRjIiP4lSZx9xRjpL0mZ2R5nYzaBkc5sTT6U4D6rtNGHEEijZ5rAgLMuSceJqfoUCaPIYEFBFxuNwM3qnpwrOfXESHju06TVfE4sH1hbi5LH3ctDTDMPjl7nP488EWAMB/Xj8X963JD+q6SSwFERJLBDFzDNmcfq369d1GNPQNjdvBJpUIUcJ1pLFdabmahJC36ntn4nnTd6e7jDjXbRpX3GkSxJifzgqnBelyLMiUX/HoCIKYadxuBnvqtfj9voto7BsCACRKJfjB2gJ8fWnmJV30GYbB7/ddxB8/bQQA/Oi6IvxwXeGMr9tL1Iql7du347e//S16enpQUlKCZ555BqtXrx533/3792Pt2rUB28+dO4e5c+dy999++2385Cc/QVNTE/Lz8/Hkk09iy5YtU14TiSWCmB70w3ZOEHkHyLYMDmO8bylVvJgbBVLiadePpOGxXodvtvaJFVEXtGY4xxGBKbIYzM+Qe8QTm8ZTxYtDsGqCYGEYBvsv9uN3ey+grssEgI2UfveafHxrRc5lp7T/9GkD/mfvRQDA99fm48cb5gQlwhqV1gFvvfUWHnroIWzfvh2rVq3CCy+8gM2bN+Ps2bPIypq4OOzChQt+v4TExETu58rKStx22234xS9+gS1btuCdd97BrbfeikOHDmHZspkf4kcQsxWb04Wz3SbUtBtQ02FATbvez+3Xl1R5DOd67Y0YpcgiO10lFvI970UOgP3+sjpcONdj8hSRsym8xr4haE1WaM9ase9sL/f8DGUsFmYoOBFVmiEP2igXYnZzrHkQ/7P3Ak606gEA8WIB7l6dh3tW517xZ/AH1xYiRiTAf//rHP73syZYHW48/qV5YfM3HlGRpWXLlqG8vBzPPfcct23evHm46aab8NRTTwXs740s6fV6KBSKcY952223wWQy4cMPP+S2bdq0CUqlEjt37pzSuiiyRBCTwzAMugwjrDBqN6CmQ4/6rvHTUFmqOM7UsTSddb7WJEhCsOrwYNjmxNkeEyeeznQa0TyB/1OeJh7zuRooBUrSaPAuMX2c7jTgf/ZexIGL/QAAiZCPb63Mwf1X509bpPP1ylb85L16AMA3l2fh5zeUzmi0OOoiS3a7HVVVVXjsscf8tm/YsAFHjhyZ9LllZWWwWq0oLi7G448/7peaq6ysxMMPP+y3/8aNG/HMM89MeDybzQabzcbdN5lMl/FOCCL6sdidON1p9IgjPWo6DOg32wL2U8aJUJalRFmmAmVZSszPkEMeS9ERX+IlQizJUWFJjorbZrI6UOeJPp3pNOJ0lwEduhE0DwyjeWAY79V2A2DtEAqSEjA/XcH6QGXIMS9VRgN6icviYq8Zv9t7AR/Vs5FNIZ+Hry/NxA/WFiJFPr31dHeuyIFYyMdju87gjaPtsDnc+NUtC0JedxgxYmlgYAAulwvJycl+25OTk6HVasd9TmpqKnbs2IHFixfDZrPh9ddfx7p167B//36sWbMGAKDVai/rmADw1FNP4YknnviC74ggogNv9xcnjNoNOK81BcxKE/J5KE6TYVGmAmVZCpRlKpGtptb5K0EWI8LKfA1W5o86keuH7TjdZcQZHw+oHqMVF3uHcLF3CG9XdwJg/x+Kkj0mmhmsiCpKnrnuQCIy6TfbsO9sL/bUa3GwoR8Mw4rvm8rS8dC6ImSp42bstW9bkgWxkI8f/d8p/L2qE3aXG7/72sKQGr1GjFjyMvaLlWGYCb9s58yZgzlz5nD3V6xYgY6ODvzP//wPJ5Yu95gAsG3bNjzyyCPcfZPJhMzMzMt6HwQRqRgtDtR2jgqj2g4DjCOOgP1S5TGcKCrLUqA0XU4RjRlEGS/G1UWJuLpotCazz2T1q3863WnE4LAdZ3vY2XhvnugAwPpOzUuVYoG3BipDjoJEciGfbXTqLfiovhcf1Wlxok3n11ixuTQFj1xXhMJkaVDWsqUsAxKhAA/srMF7td2wOdz44+1lEIXoMxkxYkmj0UAgEAREfPr6+gIiQ5OxfPlyvPHGG9z9lJSUyz6mRCKBRDJ7ayiI2YPT5cbF3iHUdOi5yFFTf2C9jETIx4IMOZdSW5SlQKo8NgQrJnxJksVgnSwG6+ax32deF3Ku/skjpIwjDpzqNOJUp5F7bqxIgJI0GSeeFmQokKsmF/Joo7HPjI/qe7GnToszXUa/xxZmyLGxNAWbSlKQl5gQ9LVdPz8VYgEf97x2Envqtdh/oR/XFU/9fD+dRIxYEovFWLx4Mfbt2+fX1r9v3z7ceOONUz5OTU0NUlNTufsrVqzAvn37/OqW9u7di5UrV07PwgkigugzW1Hr0512utM4rgN2jjqOFUaeyNHcVGnIrviIqcPj8ZCmiEWaIhabSlMAjLqQn+LEkwF1XSYM2Zw42abHyTY99/wEiRCl6TIufbcgXYFMVSylUiMIhmFQ12XCnvoe7KnT+l388HnAkhwVNpWmYENJCtIVob/gcbrZJpAYEXtBFioiRiwBwCOPPII777wTFRUVWLFiBXbs2IH29nbcf//9ANj0WFdXF1577TUAwDPPPIOcnByUlJTAbrfjjTfewNtvv423336bO+aDDz6INWvW4Ne//jVuvPFGvPfee/j4449x6NChkLxHgggWU23dT5AIR+uMshRYmKGAehZ3p0UbPB4PWeo4ZKnj8JWFaQDYOrTmgWHO/+lMJzt0eMjmxNFmHY4267jnK+JEnAP5wgwFluSooCQPqLDC5WZQ1abHnjotPqrXossw+ncuEvCwqkCDTSUpWF+cHFadp3anG7/68DwA4N7VeUiWhc6cNaLE0m233YbBwUH8/Oc/R09PD0pLS7F7925kZ2cDAHp6etDe3s7tb7fb8eMf/xhdXV2IjY1FSUkJ/vWvf+H666/n9lm5ciXefPNNPP744/jJT36C/Px8vPXWW+SxREQVDMOgUz+Cmg6DJ3I0fus+jwcUJUk5YVSWpUR+YugdsIngwufzUJCUgIKkBGwpywDApmQb+4dGO/A6DTjXY4bB4sDBhgEcbBjgnl+UnICluSoszVVjaY5q2jumiEtjd7pR2TyIPXVa7DurxcCQnXssViTA2rmJ2FiSgrVzk8LWn+tvx9rQOmiBJkGM71wd3DEoY4kon6VwhXyWiHBj2OZp3edqjQwYGAps3VfFiz1t+6wwWpAhhzRMvziJ8MPXhfx0pwFVbXo0eEZe+JKlivOIJxWW5qioC3KGGLG78PnFfnxUr8XH53phto4OdJbFCLG+OBmbSlKwpigx7JstTFYHrv7NZ9BbHPjvm0rxzeXZM/M60TruJBwhsUSEEm/KxOtnVNNuwIVJWve9nkZlWQqaek9MO4NDNpxo1eNEqw7HW3So7zYGfBaTpBIszVVhWa4KS3JVKEqSUuH4FWIcceDT82yB9ucX+2F1jEaLNQkSbCxJxqbSFCzPU0dUXeGvPjyP5z9vQn5iPD56aM2MdWaSWAoiJJaIYGKw2FHbYeBqjWrb9TD5XEF6odZ9IhwwWx2oahsVT6c6jAHpX3msCEtyWPG0NFeFkjQZ2RZMgq8H0pHGAb95ghnKWGwqScGm0hSUZSkjMoXeZRjB2v/ZD7vTjRf/rQLrZ7ADLuocvAliNuJ0uXGh1+w3JqR5nNb9GBEf89OpdZ8IP6QxIlwzJwnXzEkCwM6/q+0w4ESLDsdbdahq08M44sDH53rx8TnWITpOLMDibCWW5rCRp0WZilkv9Dt0FnxUr8Xe+t4AD6Si5ARsKknBxtIUFKfKIj5a/LuPLsDudGNZrgrr5iWFejkASCwRRFjRZ7aOCiNP6/6II7B1P1cT71drNCeFWveJyCBGJMDyPDWW56kBAA6XG/XdJpxo0eFYiw4nWnUwjvgXjYsFfCzMlGNpLjv2ZXG2clbU1jX2mbGnTos99VrUdfmP1fJ6IG0sSUF+CDyQZoq6LiN21XQBAP4fDdKNLigNR1wJNqcL9d7WfY8btm9LrxepRIhFWQqu1mhhpmLahlYSRLjhdjO42GfmxNPxFh36xswV5POAkrRR8bQ0VxUVfxOR5oE03TAMgztePIYjTYO4cVEanv162Yy/JtUsBRESS8RUcLkZnOky4uDFfhxsGEBth2Hc1v05yVK/WqP8xAQqfiVmLQzDoG3QguOemqfjLTq06ywB+xUmJYx23OWqIiYNfSkPpKsKNNhUmoL185Kj3t/ss/N9uOvVExAL+PjkR1cjUzVz8+e8UM0SQYQBXYYRThwdahwImKGmjhdzqbSyTHYu12xILxDEVOHxeMjRxCNHE49bK9gZnFqj1SOeBnG8RYeLvUNo6GP//fUY67WXqYrF0hw1luYqsTRXjZwwsiuwO9040jSAj+p7I9YDabpxutz45e5zAIC7VuUERShdDhRZmgYoskR4GbY5cbR5EAcbBnCgoT+gGFsaI8SqfA1WF2mwKl9DfjMEMQ3ohu046Y08tepQ1xVoV5AolWBpzmjkaU5ycO0KLHYnDlzsx546LT453xfRHkgzwc7j7di26wwUcSJ8/u9rIY8NjkikNFwQIbE0e3G5GdR3G1lxdLEf1e16OFyjf1ICPg+LMhVYXajB6sJELMyQU0s0QcwwQzYna1fgSduNl/KWxQi5eqeluSqUpsuntUnC7WZwodeMyqZBHGkaxKFGfw+kRKkEG4oj0wNpuhm2OXHN/+xHv9mG//pyMb59VW7QXpvScAQxQ3QbRnCwoR8HGgZwpHEAeot/ai1TFYs1hYlYXZiIFfnqoF0hEQTBkiAR4uqiRFxdlAiAtSs43WnE8ZZBHGth7QpMVic+Od+HT873AWDTX+XZCk/qToWyrMuzK2AYBo19Q6hsHkRl0yCONg8GfDdkKGOxudTjgZSppFpED38+2Ix+sw3Z6rgZc+r+olBkaRqgyFJ0M2xz4ljLIA5cHMDBhn6/DhWA7VZbka/G6qJErCnUIFsdH6KVEgQxFZwuN872mHDcx67AMEbYiAQ8LMhQcJGnxdlKv/ohhmHQMjDsI450ASOF4sQCVOSosCJPjdWFGpSkRb4H0nTSZ7biuf1NeONoGxwuBv97ezm+tCA1qGugNFwQIbEUXbjdDOq7TTjQ0I+DDf2oavNPrfF5wMJMBVYXsuJoYaZiVofQCSLScbsZNPQN+XTcDaLXFGhXkCARwuVm4GIYuNyM3/cCAEiEfFTkKLEiT40V+WosyKDvhvHQD9vxwoFm/OVIK+cj96X5qfjT7WVBF5MkloIIiaXIp8c4wpngHWroHzd87hVHK/M1kMdRao0gohWGYdCus+Ddmm48+8nFgGLxseRq4vHgukJsnp8CiXD2FWdPFZPVgZcOtuClQy0YsrEF7osyFfjxhjlYVaAOSdSNxFIQIbEUeVjsThxr0eGgJ7U2dlJ6gie1tsZTmE1dawQR/fSarKhsYtNqlc2D4/o5TUaeJh4bS1OwuTQF89Pl9J3hYdjmxKtHWrHjQDNnn1KcKsOPNhTh2rlJIf09kVgKIiSWwh+3m8HZHpMnetSPk616v+4YPg9YkKFgxVFRIhZRao0gop5+sw1Hm1lhdLRpEM0D/vWIAj4P89PlWJGvxoo8NSpylIgTs31R+mE7TrbpOa+num4TXD4hqHRFLDZ5irkXZ83OYm6rw4U3jrbhuf1NGBxmvaQKkhLwyHVF2FSSEha/ExJLQYTEUniiNVpxsIE1hDzcOMD9sXpJV8RiTREbOVqZr4YiLvLHJRAEMTH6YTuOtYxGji72+keUeTygNM1fHE3VJNZsdeCzC/34qE6LT8/3+c10TJRKsLEkGZtLU7EsVxX19iF2pxtvnezA/37aCK3JCgDIVsfhofWFuGFhOgRhIJK8kFgKIiSWwoMRuwvHWga56NHYL8J4sYDtWitMxOpCDXI18RQmJ4goxjjiwPEWHSeOzmtNGHvGm5si5cTRslz1tNQjWh0ufO4xoPz4XK+fAaUyToTrilnhtLJAHVU1Tk6XG7tquvCHTxrQqWfHtqTJY/DAukLcsjgjLKP1JJaCCIml0OB2MzinHU2tnWjxT63xeMCCdDknjsqylBALw++PlSCI6WHI5sSJFh3Xzl/fHejkXZiUMCqO8tQzPoDX7nTjcNMA9pzRYu9ZrV/ziFQixLXzkrC5NAVXFyUhVhyZwsntZvDB6W4883EDWjypzESpBD9YW4CvL80Ma0FIYimIkFgKHn0mKyeODjUO+M1UAtirmNWFidw4EWUUTCInCGJ8LHYnTrbqOXF0psvoVzcEsEXXyz3iaHmeGonS0A2jdbrcON6qw546LfbUadFnHrUniBUJcM2cRGwqTcG1c5MiYkYkwzD4qL4XT++7iAu9ZgCAKl6M716dj28uz44I8UdiKYiQWJo5rA4XjrfouNqj81qz3+NxYgGWewzfVhcmIj+RUmsEEa1YHS5Ut42Ko1OdhgCvoyxVHOdztDxPjRR5TIhWOzluN4OaDj0+PKPFh3VadBlGuMfEAj6uKtRgU2kKrpuXHHYXfQzDYP/Ffvx+70Wc6TICYOdefmdNHrauykWCJHKGg5BYCiIklqYPhmHnKR24yIqjYy062J3+qbX56XJOHJVTao0gohab04XadgMnjmo6DH7fBwDbqLHcI45W5KuRrogN0WqvHIZhjXA/rOvBh3VavwHcAj4PK/LU2FSagg0lyUiShlb8HWkawO/2XkRVmx4AWwv67atycc9VeRHpP0diKYiQWPrimK0OvFfbjb8da8fZHpPfYymyGK5rbVWBZsZrDAiCCA0OlxunO4042jyII00DqGrT+w2fBYBkmYSLHK3I0yBTFRtV0WSGYd3E2YhTj180nccDlmSrsNFjSRBMYVjVpsPv9l7EkaZBAKxb+bdW5uA7a/KgTghdavOLQmIpiJBYunJOdxqw83g73qvthsXOttqKhXys8nStrSnSID8xIaq+DAmCYHG63KjvNnGRoxOtOu57wIsmQTwaOcpTz7ou1taBYeypZ1N1pzoMfo8tzJBjU2kqNpemIEczMzMpz3Qa8ft9F/DZhX4A7My825dm4XtrC5AsC88U5+VAYimIkFi6PIZsTrxf242/HW9DXddoFCkvMR63L83CLeUZYZejJwjii+M1hz3qEUfHW3Qw25x++yjjRH7iqCCJLpa8dBtGuOLwE206PxuEuSlSbC5NxabSFBQlf/Hf2QWtGU/vu4g99VoAbDrwa4sz8INrC5ChjPtCxw4nSCwFERJLU6Ouy4i/HW/HezVdGPZGkQR8bCpNwe3LsrAsV0VfigQRRVgdLpzuNOJkmw5VrXqcbNNz4y68SGOEWJbLiqOV+WrMSZaGhbNzuNNntmJvfS/21GlR2Tzo1wWYp4nHptIUbC5NRWm67LK+V5v7h/DsJw14/1Q3GIZN/d20KB0PriucsehVKCGxFERILE2Mxe7EB6fYWqRTnUZue54mHt9YmoVbFmdQDRJBRAkDQzacbNWjqk2Hk2161HUZA7rVEiRCLMlRcjVHxWmysHJ0jkT0w3Z8fI4VTgcbBvz85jKUsdhUwtY4lU8ydqV1YBjb9zfi7eouTnhdPz8FD60vQlGyNCjvIxSQWAoiJJYCOdttwt+Ot+Hdmm5uurRIwMPGEjaKtCIvNBOmCYKYHhiGQVP/EE56IkZVbXrOkNCXRKkEFdlKLM5WoiJHhdI0WdSP+wglZqsDn57vw546LfZf6Pcbu5IklWBjCTvod2muCh36Eew+04MP63r8SiLWzU3Cw9cVoTRdHoq3EFRILAUREkssI3YXPjjNRpFqfQoRs9Vx+MbSLHx1cQY0Edw1QRCzmbEptap2PQwW/5QajwcUJUmxOEeJimwlKrJVUdetFkmM2L1jV3rwybm+gPowX/g8YHVhIh5cX4jyLGUQVxlapnr+jhznKCJsuaA142/H2rCrpoubgSTk+0eRqAaBICKLwSEbFzE62apDXZfJL70DADEiPhZmKFCRwwqj8ixlRHrtRCuxYgE2liQjWx2HZHkMXvi8ecJ9VxVo8I2lWZiXMnsv+CeDxBJxRVgdLvzzdA92Hm/nzMkA1j3360sz8bXFmSEdK0AQxNRhU2rDONmqu6yUWkmaLCyHo852vCaXbIpN6/d/KRLwsCJfA3W8GE43g6PNg+g32zxjpAYQJxbgzhXZ+N41BZDHkvD1QmKJuCwaes3467F27KruhMkninRdcTJuX5aFVfkaiiIRRJhjdbhwpsvIFWNXten9Brx6KUpOwOJsFZtSy1EiSxVHKbUwhWEY1HYYsKdOi911PejQ+YxPEfJxdVEirp+fgmvnJvuJILebQXW7Hh96LAm6DCN44fNmvHWiAz+8thDfXJ4V1oNwg0XE1Sxt374dv/3tb9HT04OSkhI888wzWL169bj77tq1C8899xxqa2ths9lQUlKCn/3sZ9i4cSO3z6uvvoq77ror4LkjIyOIiZma4Va01yxZHS7sPsNGkU60jkaRMpSx+MbSLHxtcQaSosCcjCCilcEhG6o8EaMTE6TUJEI+FmVSSi2S8Aqd3We02FPXg26jlXssRsTHtXOTsLk0FWvnJk1pXhvDMPjkXB9+tec8GvuGAACZqlj8x8a5+PKC1KgUylFZs/TWW2/hoYcewvbt27Fq1Sq88MIL2Lx5M86ePYusrKyA/Q8cOIDrrrsOv/zlL6FQKPDKK6/gK1/5Co4dO4aysjJuP5lMhgsXLvg9d6pCKZpp7BvC34614+3qTs4bRcDnYd3cJNy+LAtrChMpikQQYYY3pVbVpvNEjvRoHielpkmQcBGjxdlKlKTJac5iBOByMzjRqsOHnhRbn9nGPRYvFuDaecm4vjQFV89JRJz48k7xPB4P64uTcc2cRPy9qhO/33cRHboR/HBnDV482Ixt18/D8jz1dL+liCCiIkvLli1DeXk5nnvuOW7bvHnzcNNNN+Gpp56a0jFKSkpw22234b/+678AsJGlhx56CAaD4YrXFU2RJZvThT11Wvz1WDuOt+i47emKWHx9SSZuXZIZFRb3BBEtWB0u1HUZcbJNz6XVxkupFSYloCKHUmqRiNPlxtFmHXbX9WBvvRYDQ3buMalEiOuKk7GpNAVrihIRI5q+lJnF7sSLB1vwwudNnJHw+nlJeHTTXBRGifdS1EWW7HY7qqqq8Nhjj/lt37BhA44cOTKlY7jdbpjNZqhUKr/tQ0NDyM7OhsvlwqJFi/CLX/zCL/I0FpvNBpttVM2bTKYJ940UmvqH8ObxdvyjqpP7ouXzgGvnJuOOZVlYU5RIxnEEEQb4ptROtulxptM4bkptYaaCE0blWUoo4sj8NZKwO9040jSAD89osfes1k8Ay2NF2FCcjOvnp2JlgXrGaorixEI8sK4Q31iahWc/uYidxzvw8bk+fHq+D7ctycTD64tmTQlGxIilgYEBuFwuJCcn+21PTk6GVqud0jF+97vfYXh4GLfeeiu3be7cuXj11Vcxf/58mEwmPPvss1i1ahVOnTqFwsLCcY/z1FNP4YknnrjyNxMm2JwufFTfi78da8PR5tEoUqo8BrctycRtSzKRKg/eVGuCIPxhGAbNA8OeUSFsp1pz/3gpNTEWZyuxJEdFKbUIxupw4VDDAHbX9eDjs71cEw0AqOLF2FiSjM2lqViRrw5qF2KiVIL/vmk+7lqVi19/eB57z/Zi5/EOvFvTjXvX5OG+NXlTqomKZCImDdfd3Y309HQcOXIEK1as4LY/+eSTeP3113H+/PlJn79z507cc889eO+997B+/foJ93O73SgvL8eaNWvwhz/8Ydx9xossZWZmRkwarnVgGDuPt+PvVZ3QDbPhXD4PWDuHrUW6Zk4SRZEIIgTYnGxK7UQrm1Krbtdzf6O+sCk1Jdeplq2mlFqkYnW4sP9CPz70GEcO+RhHahIk2Fyags3zU7A0RxU2zucnWnX45e5zqGk3AGDF+oPri/D1JZkRZyURdWk4jUYDgUAQEEXq6+sLiDaN5a233sLdd9+Nv//975MKJQDg8/lYsmQJGhoaJtxHIpFAIok8D6EhmxOP/uM0/nWmh9uWLJPgtiVZuG1JJtIVFEUiiGCiG7Z70mmsK/bpLiPsznFSahkKLM5RYgml1KKCYZsT+y/0Y3ddDz473weLfXQkSYosBptKU3D9/FQszlaG5YXrkhwVdn13JfbUafHrPefROmjBT96twyuHW/DoprnYUJwcdeI9YsSSWCzG4sWLsW/fPmzZsoXbvm/fPtx4440TPm/nzp349re/jZ07d+JLX/rSJV+HYRjU1tZi/vz507LucEFrtOKuV0/gXI8JPB5wTVEivrE0C9fOTQqbqxWCiGYYhkHLwLBnltqlU2oV2SoszlGilFJqUYF3ZtvuMz3Yf6EfNh9RnK6IxfXzU7CpNBVlmYqI6DLm8XjYPD8V64uT8bdj7Xj2kwY09w/jO69XYUmOEtuunxdVY1MiRiwBwCOPPII777wTFRUVWLFiBXbs2IH29nbcf//9AIBt27ahq6sLr732GgBWKP3bv/0bnn32WSxfvpyLSsXGxkIuZwcEPvHEE1i+fDkKCwthMpnwhz/8AbW1tfjf//3f0LzJGeBstwnffvUEtCYrNAlivPitJViUqQj1sggiqvGm1LyDZqvb9BgcJ6VWkJTAuWIvyVFRSi2KMFoc+PhcLz6s68GBiwN+hfjZ6jhsLk3F9fNTMD9dHrH/5yIBH99amYOby9PxwufNePFQM0606nHz9iO4fn4K/n3jXORq4kO9zC9MRIml2267DYODg/j5z3+Onp4elJaWYvfu3cjOzgYA9PT0oL29ndv/hRdegNPpxPe//318//vf57Z/61vfwquvvgoAMBgMuO+++6DVaiGXy1FWVoYDBw5g6dKlQX1vM8VnF/rwg79WY9juQmFSAl7eugSZqrhQL4sgog49l1JjZ6mNl1ITC/lY5EmpVWSzKTVlPKXUogndsB37zmrxYZ0WhxsH4HCNlgXnJcbj+tJUbJ6fguJUWcQKpPGQxojw441zcMfyLDy97yL+XtWJ3We02FvfizuWZeGBdYVQR/Ag9Ygp8A5nwtVn6Y2jbfjp+/VwuRmszFfjuW8uplk/BDENMAyDtkELJ4xOtuk5x2Nf1PGelJqnGLs0XUajI6KQfrMNe89q8eEZLSqbB+Fyj55W5yRLsXk+W4NUmJQQVQJpMs5rTfj1h+fx2YV+AECCRIjvXpOPb6/KRaw4fP4Gpnr+JrE0DYSbWHK7Gfxqz3nsOMBOmP7q4gz8cst8qnsgiCvE4XKjvtvECiNPWm1gyBawX35iPCqyVezIkBwVciilFpU4XG6c6jDgcOMgDjcO4GSbDj76CMWpMq4GqSApIXQLDQOONA7glx+eQ10X60eYLJPgR9fNwS2LM8KieH1GxZLD4YBWq4XFYkFiYmKAyeNsI5zEktXhwsNv1eLDOrY+60fXFeEH1xbQFzZBXAbGEQeq2/Wcv1FthwFWx5iUmoCPBRlyT0qN9TdSUUotKnG7GZzTmnCkcRCHmwZwvEXn18EGAAsz5Ng8PxWbS1OQrY78Gp3pxO1m8MHpbvxmzwV0GdgBv3OSpXjs+rm4pigxpOenaRdLQ0ND+Otf/4qdO3fi+PHjfj5DGRkZ2LBhA+677z4sWbLki68+wggXsTQwZMO9r51ETbsBYgEfv/nqAtxUlh6y9RBEJMAwDLoMI6Ndaq16XOg1Y+w3oyJO5CnEVmFJjhKl6fJpHS1BhA8Mw6B10IIjTQM40jiIyubBAL8rZZwIK/M1WFmgxprCRKoFnQJWhwuvV7bhj582cIabK/PV+M/r56E0XR6SNU2rWHr66afx5JNPIicnBzfccAOWLl2K9PR0xMbGQqfToa6uDgcPHsQ777yD5cuX449//OOE7tfRSDiIpca+Idz16nF06EYgjxVhx52LsWyWDjwkiMlwutw4rzXjZKsOJ9rY6JHWZA3YL0cdxwmjihwl8jQJEdHSTVwZvSYrjjQN4HDjII40DqDb6P+ZiBMLsCxXhVUFGqzM12BuipQ+D1eIwWLH9v1NePVwK9cheNOiNDz+5WJoglwEPq1i6Wtf+xr+67/+65LeQzabDS+99BLEYjHuueeey191hBJqsXS0eRD3vXYSJqsTWao4vHLXEuQnzu48OUF4GbI5UdtuwIlWHara9Khp13NDQb0I+TyUpMuxxKcYO1EauZ07xKUxWhyobB5EZdMADjcNBhToiwQ8lGUpsSpfg1UFaizMVEScO3W406Gz4Hd7L+Dd2m4AwNo5iXjlruB2olOBdxAJpVjaVd2JR98+DYeLQVmWAi/+W0VEt2cSxBdFa7Ry6bSTbTqc7Tb5Fd8CgDRGiPIs1hF7cbYKizIVYdWhQ0w/I3YXTrbp2MhR0wDquox+nwseDyhNk2NlgRqr8jWoyFEiThxR7joRS1WbHl97/gjcDPDRQ2swJ0UatNcO2rgTk8mETz/9FHPmzMG8efO+6OGIKcIwDJ79pAHPfMyOZfnS/FT87taFVENBzCrcbgYX+8w40apHVasOJ1r1XAGpL+mKWFYY5bCz1IqSpWHRiUPMHA6XG6c7DVxRdnWbwc8UEmB9j7yRo+V5ahojEyIWZyuxqTQFu89o8eLBZvz2awtDvaQALlss3XrrrVizZg1+8IMfYGRkBBUVFWhtbQXDMHjzzTdxyy23zMQ6CR/sTjce23Uau6q7AAD3X52P/9g4h/LnRNQzYnehtsOAqjZWGFW362H2mcwOsEOhi9NkXIdaRY4SqXKaexjtuN0MLvSacbhxAEeaBnG8Rec3lBYAUuUxWOkRRyvzNUiRx4RotcRY7lmdh91ntHi3tgv/vnEOkmTh9X9z2WLpwIED+H//7/8BAN555x0wDAODwYC//OUv+O///m8SSzOM0eLAd944iaPNOgj4PPzixlLcviwr1MsiiBmh32xDlSeldqJNj/ouI5xjcmpxYgHKs0bHhSzKUiBBQumTaIdhGLTrLFxarbJpMGCcjCJOhBV5aqws0GBVvhq5mniyUQlTyrNYV/uTbXr8pbIV/75xbqiX5Mdlf6MYjUbOV2nPnj245ZZbEBcXhy996Uv493//92lfIDFKh86Cra8cR1P/MOLFAvzvHeW4Zk5SqJdFENMCwzBo6h9ihVGrHlVtOrQOWgL2S5ZJUJGj8hRjqzA3RUrDoGcJfWYrKptYI8jDjYMBKddYkQBLc1Vc5Kg4VUYR9wji3jV5OPl6Fd442o7vXVOA+DC66LnslWRmZqKyshIqlQp79uzBm2++CQDQ6/WIiQmvsFk0UdOuxz1/OYnBYTtSZDF4eesSFKeF3i2cIK4Uq4MdNOsVRifb9DBYHH778HiseZ03arQ4W4kMZSxFB2YJJqsDR5sGcaSJjR5d7PXvWBPyeSjLUnhSaxosylTQpIIIZv28ZOSo49A6aMHfT3Zg66rcUC+J47LF0kMPPYQ77rgDCQkJyM7OxjXXXAOATc9dylqAuDI+PNODh96qhc3pRnGqDC9vXUK5diLi8A6aPdGmQ1WrHqc7jQEFtzEiPhZmKFhhlMMOmqV5hrMHq8OFqjY9GzlqGsSZTkNAx1pxqgyrCjRYka/G0hxVWEUfiC+GgM/D3avz8JN36/DS4RbcuSInbBoxrsg6oKqqCu3t7bjuuuuQkMD6+fzrX/+CQqHAqlWrpn2R4c5MWgc09Jqx4ZkDYBjWg+KPt5dTPQYR9ngHzXq9jU606tDUPxywnyZBzM1SW5ytREmanCIDswiny43TXUYc8RRln2zTw+70F9C5mniszFezAilPDSWNlIlqRuwurPzVJ9BbHNh+Rzmun586o683I9YBDocDc+bMwT//+U9s2bLF77EvfelLV7ZSYlIYAAzDhpuf++ZisgYgwhK70436biOq2vScv9HAkD1gv4KkBM/IEDatlk2DZmcVDMPgYu+Qp2NtAMeadTCP6VhLlkmwKp+NHK0q0CBNQZ2Ms4lYsQB3Ls/GHz5txI4DzdhcmhIW3xGXJZZEIhFsNltYLHy2UJCYAFmMECarExd7zViQoQj1kgjCb9DsiVYdTnVOPmh2iaeNn6ICs48OnYVLq1U2DQSIaFmMkBNGK/M1yE+kjrXZzp0rcvD8gWaPTYgeFTmqUC/p8muWfvjDH+LXv/41XnzxRQiFlA6aafh8HhZnK/HZhX6cbNWTWCJCgsFix9FmHSqbBnCsRTfpoNkKj/EjDZqdnQwM2diC7MYBHG4aQIfOv2MtRsTHkhwV53dUkiYPm7oUIjxIlEpwS3k6dh7vwI4DzZEplo4dO4ZPPvkEe/fuxfz58xEfH+/3+K5du6ZtcQRLRY4Kn13oR1WbHt++Kny6A4joZcjmxIlWHSo9XUj13aYAcUSDZgkAMFsdONasw2GP19F5rdnvcQGfh0WZCqzKZ/2OyrIUkAhJRBOTc/dVedh5vAP7zvWiZWAYuZr4Sz9pBrlssaRQKMh4MsgszlYCAE626cAwDIWoiWnH6nChuk3PtWif6jTCNcb8sSApASvz2bEQS3Jo0Oxspd9sw8lWHY63smah9d3GgNl781JlnqJsNZbmqqkphbhsCpISsG5uEj4534eXDjXjv28Kbbf9ZX+CX3nllZlYBzEJCzMUEPJ56DXZ0KkfQaYqLtRLIiIch8uNUx0GHGkaRGXTIKraA7uQslRxHvdjNVbkqcNu/AAx8zAMg1ZPV+OJFtYLq2UgsKsxWx3HpdVW5KlpmDcxLdy7Jg+fnO/D30924uH1RSH9XF2R3Hc6ndi/fz+amppw++23QyqVoru7GzKZjLMSIKaPWLEAJelynOow4GSbjsQScdm43AzOdptwpIlt0T7RqoPF7vLbJ1kmwUpPF9KKPDV9zmYhTpcb57VmHG/R4aRn/l6/2ea3j9codEkOa/mwJEdFHWvEjLAsV4X56XKc6TLijaPteHB9YcjWctliqa2tDZs2bUJ7eztsNhuuu+46SKVS/OY3v4HVasXzzz8/E+uc9VRkK1mx1KrHlrKMUC+HCHO8LdpecXSseRCmMQNnVfFirMhTs+IoX408mps16/AOJj7RqsOJVh2q2/QYHiOivV2NS3LZ+rTFWSrI48golJh5eDwe7l2Thwd21uC1ylZ85+q8kDWNXLZYevDBB1FRUYFTp05BrVZz27ds2YJ77rlnWhdHjLIkR4mXDrWgqk0f6qUQYYg3XeIdKHq0eTCgRVsqEWJZngor8jVYma/GnGQpFWTPMvTDdpxs03M1R3VdRjhc/gVHUomQtXvIUWFJjgoLMqirkQgdCRL2szc4bEdj3xBK0+UhWcdli6VDhw7h8OHDEIv9/VKys7PR1dU1bQsj/FmczbZOXug1wzjioBEQBLoMI1y3WmXTIHqMVr/HfVu0V+arUZImo4Gzs4xOvafeqFWPEy06NPQNBeyTLJNwwmhJjgpzUqTUyk+EBVVtOnz/rzUAgC8vSEVxaujmoV62WHK73XC5XAHbOzs7IZVKp2VRRCCJUgmy1XFoG7Sgpl2Pa+YkhXpJRJDpN9tQ2cwa+x1pGkTboMXvcbGAj7IsBVbksxPXF2bKqUV7FuF2M7jYZ+aE0clWHbrHCGgAyE+M54TR0lwVDSYmwpJzPSbc9coJjDhcuLooEb+/dVFII+GXLZauu+46PPPMM9ixYwcANqc4NDSEn/70p7j++uunfYHEKIuzlWgbtKCqjcTSbMBrBHm0efyJ63wesCBDgZUecbQ4W4lYMYmj2YLN6cKZTiMrjlpZcTS2Lk3A56E0XY4l2UosyWXNQqlTjQh32gaH8W8vH4fJ6sTibCWe+2Z5yGdGXrZYevrpp7F27VoUFxfDarXi9ttvR0NDAzQaDXbu3DkTayQ8VGSrsKu6CydbqW4pGpmKEWSxx79mZQHrdSSNoXTsbMFkdaDaM5T4RKsepzoMsI2xe4gVCVCereAiR2VZCsSJyeOIiBz6TFbc+dJx9JttmJsixcvfWhIWn+HLXkFaWhpqa2vx5ptvoqqqCm63G3fffTfuuOMOxMZS++hMUpHDmlPWdhjgcLkhovqTiGaqRpAr8tRYma/Gsjw1VDRbbdbQZ7LiuMff6ESrHue1pgDzR3W8mGvfX5KjQnGajL4XiIjFYLHjzpeOo11nQZYqDq99e2nYdF5etlg6cOAAVq5cibvuugt33XUXt93pdOLAgQNYs2bNtC6QGMV3qO7ZbhMWZipCvSTiMvAaQbKRo/GNIDNVsViZpyEjyFkGwzBoHhjmhNGJVh3adZaA/bJUcZ5aI3YGH9k9ENGCxe7Et189gQu9ZiRJJXjj7mVh9f132WJp7dq16OnpQVKSf82M0WjE2rVrxy3+JqYHv6G6bXoSS2HOZRlBevyOyAhyduBwuXG228T5G51s1WNw2N/qgccD5qXIsDR31PwxOYxOHgQxXdidbnzn9SpUtxsgixHitbuXIksdXt+Fly2WJppNNjg4GDBUl5h+vEN1X/i8CVmqOFxXnBzqJREepmIEqYwTeUwg2XZ+igzMDix2J2raR80fa9oNAcJZLORjUaYCSzzCqDxbCRnVpBFRjsvN4OH/q8XBhgHEigR45a6lmJsSOouAiZiyWLr55psBsN1vW7duhUQy2lHhcrlw+vRprFy5cvpXSPhxa0Um/u9kB9oGLbj3tZO4fn4KfvaVkrAKV84WvEaQ3oJsMoIkvAwO2XCilTV/PNGqQ123KaAeTRYj9IwMYdNqpelk9UDMLhiGwePv1uFfp3sgEvDwwp2LucHx4caUxZJczrpmMgwDqVTqV8wtFouxfPly3HvvvdO/wjFs374dv/3tb9HT04OSkhI888wzWL169YT7f/7553jkkUdQX1+PtLQ0/Md//Afuv/9+v33efvtt/OQnP0FTUxPy8/Px5JNPYsuWLTP9Vq6IRKkEex5cg2c/acCfDzZj9xktDjYMYNvmefj6kkw6Ec8wl2MEuSJfjVIygox6GIZBh26EixqdaNWhqT9w2GyaPIZt389RYWmOCoVJCfT3SsxqfvvRBew83g4eD3j6tkVYU5QY6iVNyJTF0iuvvAIAyMnJwY9//OOQpNzeeustPPTQQ9i+fTtWrVqFF154AZs3b8bZs2eRlZUVsH9LSwuuv/563HvvvXjjjTdw+PBhfO9730NiYiJuueUWAEBlZSVuu+02/OIXv8CWLVvwzjvv4NZbb8WhQ4ewbNmyYL/FKRErFuCxzXPxlYWp2LbrDE53GvGf75zBuzVd+OXN81GQRMOMpwu3m0FVux576rT45FwvWscYQYoEPJRlKTmvIzKCjH5cbgYXtGac8IwMOdmqQ6/JFrBfUXICJ4wqcpTIUIZXDQZBhJI/H2jG9v1NAIAnb5qPLy9IC/GKJofHMGOdXC6N0+nE/v370dTUhNtvvx1SqRTd3d2QyWRISJi5E/WyZctQXl6O5557jts2b9483HTTTXjqqacC9n/00Ufx/vvv49y5c9y2+++/H6dOnUJlZSUA4LbbboPJZMKHH37I7bNp0yYolcop+0aZTCbI5XIYjUbIZMHNtbrcDF490orf7b0Ai90FsYCP763Nx3evyaeT9hXicLlxtHkQe+q02Hu212/qOhlBzj6sDhdOdxq5qFFVmx7mMbVoIgFr/rjU08K/OFsJJdk8EMS4/N+JDvzH26cBAP+xaQ6+d01ByNYy1fP3ZRd4t7W1YdOmTWhvb4fNZsN1110HqVSK3/zmN7BarXj++ee/0MInwm63o6qqCo899pjf9g0bNuDIkSPjPqeyshIbNmzw27Zx40a89NJLcDgcEIlEqKysxMMPPxywzzPPPDPhWmw2G2y20ROoyWS6zHczfQj4PNx9VS42liTjJ+/W4bML/Xjm4wb883QPfnXzfFTkqEK2tkjC6nDhYMMAPqzrwSfn+mAccXCPSWOEWD8vGRtLkrGyQENFt7OIDp0F6373Oewuf4uHeLEA5dlKT9RIhUWZChLNBDEF9tT14LFdrFC6b00evnt1fohXNDUuWyw9+OCDqKiowKlTp6BWq7ntW7ZswT333DOti/NlYGAALpcLycn+3V/JycnQarXjPker1Y67v9PpxMDAAFJTUyfcZ6JjAsBTTz2FJ5544grfycyQoYzDy1uX4J+ne/DEB/Vo7BvCV5+vxB3LsvDo5rl0gh+HIZsTn53vw546LT670OfXnaRJEOO64hRsKk3Bijx1yK32idCQoYxFvEQAGV+Epbmj5o9zU6RUi0YQl8nhxgE8sLMWbga4rSIT2zbPjZhu4MsWS4cOHcLhw4chFvuHmLOzs9HV1TVtC5uIsb/YiawMJtt/7PbLPea2bdvwyCOPcPdNJhMyMzMvvfgZhsfj4SsL07C6UINf7j6H/zvZib8ea8e+s734+Y0l2FSaGuolhhz9sB37zvXiozotDjYO+JlCpsljsLE0BZtKUlCRo6LJ6wR4PB72Pnw1NAniiPlSJ4hwpLbDgHtfOwm7y41NJSl4cktpRP1NXbZYcrvd4xpPdnZ2QiqVTsuixkOj0UAgEAREfPr6+gIiQ15SUlLG3V8oFHJRsYn2meiYACCRSPysE8INRZwYv/nqQtxUlo7/904dWgaGcf8b1dhQnIyf31iKFPnsshnoM1nxUb0We+q1ONqs82vhztXEY5NHIC3IkEfUHy8RHBKl4fu3ThDhjtvNYP/FPjzyf6dgsbuwqkCNZ7+xKOIis5ctlq677jo888wz2LFjBwD2ymtoaAg//elPcf3110/7Ar2IxWIsXrwY+/bt82vr37dvH2688cZxn7NixQp88MEHftv27t2LiooKiEQibp99+/b51S3t3bs3KjyjVuZr8OGDq/GnTxvx/OdN2Hu2F0eaBvHopjm4Y1l2VLctd+gs2FPHCqTqdr3fQNp5qTJsKmFTbEXJCSSQCIIgphmrw4X3arvw54MtaOwbAgAszFTghTsrIrL56LK74bq7u7F27VoIBAI0NDSgoqICDQ0N0Gg0OHDgQMAYlOnkrbfewp133onnn38eK1aswI4dO/DnP/8Z9fX1yM7OxrZt29DV1YXXXnsNAGsdUFpaiu985zu49957UVlZifvvvx87d+7krAOOHDmCNWvW4Mknn8SNN96I9957D48//vhlWQeEshtuqpzXmvDY22dQ22EAAJRnKfCrWxagKHnmooHBhGEYNPYNYU+dFh/WaXG2x7/ovixLwQmkbDU5zRMEQcwE+mE7/nqsDa8eacPAENsIlSAR4htLM/GDawshjw2v+tmpnr+vyDpgZGQEO3fuRHV1NdxuN8rLy3HHHXf4GVXOFNu3b8dvfvMb9PT0oLS0FE8//TQ3vHfr1q1obW3F/v37uf0///xzPPzww5wp5aOPPhpgSvmPf/wDjz/+OJqbmzlTSq9j+VSIBLEEsDYDfz3Wht/suYAhmxMiAQ/3X52P768tQIwo8pQ+wzCo6zLhw7oe7KnXotnHCJDPA5bnqbGpNAUbilNmXeqRIAgimLQNDuOlQy34+8lOjDjYUp1UeQy+vSoXty3NDNsmoxkVS4Q/kSKWvPQYR/CTd+vx8bleAECeJh6/vHk+luepL/HM0ONyM6hu1+PDM1p8VK9Fl2GEe0ws4OOqQg02laRgfXEyVORzQxAEMaNUt+vx5wPN2FOv5codilNluG9NHr60IBWiMK9NmlGx1NXVhcOHD6Ovrw9ut7//yAMPPHD5q41wIk0sAWxUZk+dFv/1fj1nuvj1JZnYtnke5HHhdQXgcLlR2TSIPfVa7K3v5UK7ABArEmDt3ERsLEnB2rlJYXv1QhAEES243Aw+PteLPx9oxsk2Pbf9mjmJuHd1HlbmqyOmFnTGxNIrr7yC+++/H2KxGGq1OqAFv7m5+cpXHaFEoljyYhxx4Nd7zuNvx9oBAJoECX52QzG+ND81pB92q8OFAxf7sadei4/P9sLk45gsjRHiunnJ2FiagquLEiMyhUgQBBFpjNhd+Ed1J14+1IKWAbbsQSTg4aZF6bhndR7mpEReDeyMiaXMzEzcf//92LZtG/j88A6vBYtIFktejrfosG3XaW4A6Lq5Sfj5TaVIV8x8HZoXs9WBzy7046NJTCI3l6ZgOZlEEgRBBI2BIRteq2zD65Wt0FvY6QbyWBG+uTwL31qRgyRZ5NaEzphYUqvVOH78OPLzI8OiPBhEg1gCAJvThef2N+F/P2uEw8UgTizAjzbMwR3LsmYseuM1idxTp8WhhgG/sRLpilhs9HSwLc5WkkkkQRBEEGnqH8KLB1vwdnUnZ+CbqYrF3aty8bWKTMRLLtt9KOyYMbH0H//xH1CpVAEz2mYz0SKWvDT2mfHY22e4XHSCRIjNpSnYUp6O5bnqL+zP1Os1iazT4liLv0lkntcksjQF89PJJJIgCCKYMAyD4y06/PlgMz4+18dtX5ipwH2r87CxJDniDCUnY8bEksvlwpe//GWMjIxg/vz5nLmjl9///vdXtuIIJtrEEsC6ru480Y7tnzX5dZylymNww6I0bClLx9yUqb/X9kEL9tT3YE+dFtXtBr/HilNlnEAqTCKTSIIgiGDjdLmxp16LPx9oxqlOIwCAxwPWz0vGfWvyUJGtjMrv5qmevy87hvbLX/4SH330EebMmQNg8hlrROTC5/Nwx7JsfGNJFqra9dhV3YV/ne5Gj9GKFz5vxgufN2NuihRbytJx46L0AB8jhmHQ4DGJ3DOOSWR5lsIzZiQVWeq4YL41giAIwsOwzYm3TnTg5cMt6NSzF8YSIR+3LM7A3VflIj8xIcQrDA8uO7KkVCrx9NNPY+vWrTO0pMgjGiNL42FzuvDZ+X68W9OFT8/3cfVFPB6wMl+Nmxal47riZOw924sdB5o5i3sAEPB5WJ6nwqaSFGwoSUFyBBcEEgRBRDq9JitePdKKvx5t47qNVfFi3Lk8G3euyIYmYXbMRJyxNFxKSgoOHjyIwsLCL7zIaGG2iCVfjBYHdtf14J3qLhxv1Y27D58HrJ2ThI2lKVg/j0wiCYIgQs0FrRl/PtiM92q74HCxp/9cTTzuWZ2LW8ozZp0Vy4yl4R588EH88Y9/xB/+8IcvtEAispHHifCNpVnYUpaOp/ddxAsHAv21hHw+MpSxKExKgDLMjC4JgiBmCwzD4HDjIHYcbMaBi/3c9iU5Sty7Og/r5yVH9WD16eCyI0tbtmzBp59+CrVajZKSkoAC7127dk3rAiOB2RhZsjpc+NuxdrxwoAm9JtZRO0kqwVWFGsSIBAFO27maeNy0KB03laXRIFuCIIgg4HC58c/T3dhxoAXnPHWjfB6wuTQV96zORVmWMsQrDD0zloa76667Jn38lVdeuZzDRQWzSSxZ7E789Wg7XjjQzImhVHkMvndNPr5WkcmFcJ0uNw43DeLdmi7sqdNygxUBtrh7S1k6vrQgjVJzBEEQ04zJ6sCbx9vx8qFWaE1WAOxoqNuWZOLbq3KpqcYHGqQbRGaDWBqyOfFaZStePNgC3bAdAGsa+f21BbhlcTokwonz3MM2J/ae1eKdmm4cauiH11ZJyOfhmjlJ2FKWjnXzkmZdrpwgCGK6sDldqGwaxL6zvXivthtDNrZoO1EqwdaVObhjWRYUcXRxOhYSS0EkmsWSyerAXw634qXDLTB4bO6z1XH4/toCbClLv+yJ0n1mKz441YN3a7pwpsvIbZdKhNg8PwU3lU2P8SVBEES0Mzhkw2cX+vHx2V4cbOjHsM+IqMKkBNy7Jg83Lkqb9GJ2tjOtYmnTpk34r//6L6xcuXLS/cxmM7Zv346EhAR8//vfv/xVRyjRKJaMFgdePtyClw+3wOxpK83TxOMH1xbghoVp0+Lg2tBrxru1XXi3pjvA+PLGRenYUpYekYMZCYIgZgKGYdDUP4R9Z/vwybleVLXr4XsGT5ZJsG5eMjaVpGB1oYa8D6fAtIqll156CT/96U8hlUpxww03oKKiAmlpaYiJiYFer8fZs2dx6NAh7N69G1/+8pfx29/+FpmZmdP6hsKZaBJLumE7XjrUjL8caePCuIVJCfjBtQX48oK0GZnP5nYzONmmxzs1rPGl1/MDAOalyrClLA03LAw0viQIgoh2HC43TrTq8Mm5Pnx8rhdtgxa/x0vSZFg/Lxnr5yWjNF1GAukymfY0nN1uxz/+8Q+89dZbOHjwIAwGA3sAHg/FxcXYuHEj7r33Xs7ZezYRDWJpYMiGPx9sxuuVbbB4QrlzU6R4YF0hNpWkBC0tZnW4sP9CH97xGF96fUB4PGBVvgY3laVjU2kKEqJggCNBEMR4GEcc2H+hD5+c68P+C31+F5BiAR8rC9RYNy8Z6+YmIU0RG8KVRj4zXrNkNBoxMjICtVodYB8w24hksdRnsuKFA83467E2WB2sI3dpugw/vLYQ14XYe8NgsWP3GS3erfE3vowR8XFdcQq2lKVhdWHiZddNEQRBhBttg8P4+FwfPj7bixOtOjh9Boyr48VYOzcJ6+clY3WhBvF0sThtUIF3EIlEsdRjHMELnzfjb8fbYXeyImlhpgIPrivA2jlJYRfK7dBZ8F5tF3bVdKG5f5jbro4X4ysL03BTWToWZsjDbt0EQRDj4XIzqO3Qc/VHDT7joQC2/GF9cTLWz0vCokzljJRAECSWgkokiaVOvQXP7W/C3092crPdFmcr8cC6QqyJgIJAhmFQ12XCOzVdeP9UNxlfEgQRMQzbnDjY0I+Pz/Xh0/N9nA0LwFqpLM1VYd08ViDRd1hwILEURCJBLLUPWrB9fyP+UdXJhXeX5arw4LpCrMhXh71IGg+ny41DjQN4t6YLH9X3Bhhf3rAwDVfPSUKOOi4i3x9BEJFPj3GES69VNg1yF6kAIIsR4po5SVhfnIyrixIhj53dJS2hgMRSEAlnsdTcP4T//awJ79Z2weURSasK1PjhtYVYnqcO8eqmD6/x5a7qLhxuHIBPuh/pilisKlDjqsJErMxXz5pp2gRBBB9v9HvfuV58cq4X9d0mv8ez1XFYPy8Z6+YlYUmOimouQwyJpSASjmKpsc+MP33aiPdPdXPC4eqiRDywrgCLs1WhXdwM02ey4v1T3fjkXB+q2vR+V3IAa0ewulCDVQUaLM1RIVZMhm0EQVw5VocLR5oG8PE5tv7IOy8TYGexlWcpufqj/MQEinSHETMmlrZu3Ypvf/vbWLNmzRdeZLQQTmLpvNaEP37aiN1nejizsnVzk/DDdYVYlKkI6dpCwYjdheOtOhxuHMDBhgFumKQXsYCPxdlKXFWowVUFGpSmy6mQkiCIS9JvtuHT8734+FwfDjUM+JUBxIsFWFOUiHXzkrF2TiLUFM0OW2ZMLN1yyy3417/+hczMTNx111341re+hfT09C+84EgmXMTS/53owH+8fZq7v6E4GQ+sK0Rpujxkawo3BoZsONI0iEMN/TjUMIBuo9XvcVmMECvzNZx4yqZ6J4IgwKbXLvSa8cm5Puw724tTnQY/9+w0eQxbnF2cjOV5KhoxEiHMaBpucHAQb7zxBl599VXU1dVh/fr1uPvuu3HjjTfOSs+lcBBL53pMuPF/D8PudGNDcTIevq4I81LDIyUYrjAMg9ZBCyucGgdwpGmQG+3iJV0Ry6XsVuar6QqRIGYRdqcbx1t0+PhcLz4+14tO/Yjf4wsy5Fz9UXEquWdHIkGrWaqpqcHLL7+MF198EQkJCfjmN7+J733veygsLPwih40oQi2WLHYnbvjTYTT2DWHtnES89K0lNIj2CnC63DjTZcThxgEcahxAVZuecxD3UpImw1UFbORpSY4KMSK6eiSIaMJgseOzC334+FwfDlzoh9k2egElEfJxVYEG64uTce3cJCTLaARTpBMUsdTT04PXXnsNL7/8Mrq6unDLLbegp6cHn332GX7zm9/g4YcfvtJDRxShFkuP/uM03jrZgSSpBB8+uJqiH9OExe7E8RYdDjWw4um81uz3uFjIR4VPvVNJGtU7EUQk0tw/xKbXzvWiqk3PdQ4DgCZBgvXzkrBuXjKuKtBQQ0iUMWNiyeFw4P3338crr7yCvXv3YsGCBbjnnntwxx13QCplJ8S/+eab+O53vwu9Xv/F3kWEEEqx9P6pbjywswY8HvDXe5ZhZb4mqK8/m+g323CkaYATTz1j6p0UcSKszFdjVYG33olM5QgiHHG63KhuN3DpNd+pAAA7F3O9p/5oQbqcIvVRzFTP35c9YCY1NRVutxvf+MY3cPz4cSxatChgn40bN0KhUFzuoYnLpH3Qgv/cdQYA8IO1BSSUZphEqQQ3LkrHjYvSwTAMmgeG2ZRdwwAqmwZhsDiw+4wWu89oAQCZqlg2ZVfA+jsp48UhfgcEMTtxuNyo7zbheMsgjrfocLxF5zecViTgYXmeGuvnsem1TFVcCFdLhCOXHVl6/fXX8bWvfQ0xMZSr9RKKyJLd6cbXnj+CU51GVGQr8eZ9yyEkc7OQ4XS5cbrLiMMNAzjYOICadv96Jx7PW++UiKsKNKjIUVK9E0HMEFaHC6c6DKwwatWhqk0Pi93lt48iToRr57DptTVFGkhjZl9zEhGFppR6vR4PPPAA3n//fQDADTfcgD/+8Y8TRrAcDgcef/xx7N69G83NzZDL5Vi/fj1+9atfIS0tjdvvmmuuweeff+733Ntuuw1vvvnmlNcWCrH0y93nsONAM+SxIux+cDXSFbFBeV1iagzbPPVOjQM4PE69k0TIx5IcFVYVaLC6UIPiVBmF+gniChm2OVHdrsexZjZqVNthCDCjVcSJsCRHhWW5KizNVaE4VUYXmET0iaXNmzejs7MTO3bsAADcd999yMnJwQcffDDu/kajEV/96ldx7733YuHChdDr9XjooYfgdDpx8uRJbr9rrrkGRUVF+PnPf85ti42NhVw+dW+iYIulzy704a5XTgAAXrhzMTaWpMz4axJfjD6zFUcaB3HIk7bTmvzrnZRxIj9/J0oDEMTEGC0OnGhlo0bHWnSo6zL6FWUDbNp8Wa5XHKlRmJRAFyREAFElls6dO4fi4mIcPXoUy5YtAwAcPXoUK1aswPnz5zFnzpwpHefEiRNYunQp2trakJWVBYAVS4sWLcIzzzxzxesLpljqNVmx+dmD0A3b8a0V2XjixtIZfT1i+mEYBk39w5yr+NHmQQzZ/P2dslRxnHBama+GIo7qnYjZS7/Z5qk1GsSxFh0u9Jox9syVoYzFUo84WparJkNZYkrMWIF3KKisrIRcLueEEgAsX74ccrkcR44cmbJYMhqN4PF4Aam7v/71r3jjjTeQnJyMzZs346c//SnX2TceNpsNNtvo7B+TyTThvtOJy83g4bdqoRu2Y16qDNuunxeU1yWmFx6Ph4KkBBQkJeBbK3PgdLlxqtOAQw2DONw4gOp2Pdp1FvztWDv+dqwdPB4wP13OpuwKNCjPpnonIrrpMoxwxdjHWnQB3WoAkJ8Yj6W5aizLVWFJropKEYgZJSLEklarRVJSUsD2pKQkaLXaKR3DarXisccew+233+6nHu+44w7k5uYiJSUFdXV12LZtG06dOoV9+/ZNeKynnnoKTzzxxOW/kS/I85834UjTIOLEAvzp9jI6YUYJQgEfi7NVWJytwoPrCzFkc+J4yyAONQziUGM/LvYO4XSnEac7jXhufxMkQj6W5qpwVQHrLE71TkQkwzAMWgaGuS61Yy06dBn8nbJ5PGBuioyrN1qSo0KilPzkiOARUrH0s5/97JKi48QJtjZnvHAqwzBTCrM6HA58/etfh9vtxvbt2/0eu/fee7mfS0tLUVhYiIqKClRXV6O8vHzc423btg2PPPIId99kMiEzM/OS6/ginGzV4ff7LgIAfn5jKfITE2b09YjQkSAR4tq5ybh2bjIAoM9kxeEmNmV3uHEAvSYbDjaw9wFAFS/Gynw1J56o3okIZ9xuBhf7zJwwOt6iQ7/Z5rePgM/D/HQ5J44qslWQx1G3GhE6QiqWfvCDH+DrX//6pPvk5OTg9OnT6O3tDXisv78fycnJkz7f4XDg1ltvRUtLCz799NNL1hSVl5dDJBKhoaFhQrEkkUggkQTvqsZgsePBN2vhcjPYUpaOW8pn9+Di2UaSLAZbyjKwpSzDU+80xAmnyqZB6Ibt+OfpHvzzdA8Att6pIluJ8mwlKnKUKEySkrM4ETKcnMcRK45OtOpgHHH47SMW8rEoU8GJo/IsJeIlEZH4IGYJIf00ajQaaDSXNlJcsWIFjEYjjh8/jqVLlwIAjh07BqPRiJUrV074PK9QamhowGeffQa1Wn3J16qvr4fD4UBqaurU38gMwjAMHn37NLoMI8hRx+EXN5VS0eIshq13kqIgSYq7VuXC4XLjVIeB67Kr6TCgXWdBu86CXTVdAACpRIhFWQoszlZicbYSizIV5ClDzBg2pwunO42cOKpq1WF4jMdRnFiAxdlKrlNtQYacygqIsCYiuuEA1jqgu7sbL7zwAgDWOiA7O9vPOmDu3Ll46qmnsGXLFjidTtxyyy2orq7GP//5T78IlEqlglgsRlNTE/7617/i+uuvh0ajwdmzZ/GjH/0IsbGxOHHiBASCqf3xzmQ33OuVrfjJe/UQCXjY9d1VmJ8xdUsDYvZhtjpQ025AVZseVW161LTrA05UfB4wJ0WGxdkeAZWlQqYqlkQ4cUVY7E5Utxm4TrWaDgPsTn+PI1mMEEs9UaOluWqUpMkgIo8jIgyIKusAANDpdAGmlH/605/8Ott4PB5eeeUVbN26Fa2trcjNzR33WJ999hmuueYadHR04Jvf/Cbq6uowNDSEzMxMfOlLX8JPf/pTqFSqKa9tpsTSea0JN/zpMOxON37y5WLcfdX474cgJsLlZnBBa0ZVux5VrTpUtevRoRsJ2C9RKsHiLDbyVJ6tRGm6DBIhXekTgRhHHDjZOlqMXddlhHOMx5EmQYxluWpOIM1JllITAhGWRJ1YCmdmSiz9z0cX8KfPGpEklaBy2zqqOyGmhT6TFdXtepxs1aOqXY+6LqPfaBaArSFZkC7nxFN5lpK6j2YpA0M2nPApxj6nNQV4HKXJY7Asb1Qc5WniKVJJRAQkloLITIml1oFhXP+Hg7DYXXhs81zcf3X+tB2bILxYHS7UdRlR1abHyTY9qtv0GBy2B+yXrY7j6p4WZ1PheLTSbRjx6VQbRNM4Hkd5mniftJoKGUrqwCQiExJLQWQma5beOtGOR98+A5GAh3e+twql6VSzRMwsDMOgbdDiJ54u9gU6JnsLxyuyVWzheJYCCdTBFFF4/685cdQ6OG6adm6KdFQc5aiQJKNB6kR0QGIpiMykWGIYBve/UYWP6nuRnxiPf/5wNWLFVEtCBBfjiAO1Hd7CcR1q2w2TFo57BVSGkgrHwwm3m0FD3xBXjH28RYe+cTyOStNkXDH2khwljdshohYSS0FkpmfD6Ybt2PTMAfSZbfi3Fdn4Oc2DI0KM0+XGhV4zqj1ddyfb9OjUT144vjhHiZI0KhwPJha7E2c6jajxCN2TrTroLWM8jgR8LMyUc+JocbaSIoTErIHEUhAJxiDdAxf78W8vHwcAvLJ1CdbODRz/QhChpNdk5cTTVArHvcXjmgQqHJ8O3G4GLYPDqGk3oKZdj5p2Ay70muEa06kWK2I9jrxptUWZCvI4ImYtJJaCSDDEEgA88UE9XjncCk2CGHseWkMnGSKssTpcOOMpHPf+041TOJ6jjkO5RzxVZKtQmJRAbeZTwGCxo7bDwIqjDgNq2/UwWZ0B+yXLJCjPYs1Il+SqUJomh1hIHkcEAZBYCirBEktWhws3/OkQLvYOYf28JPz53yqoHoSIGBiGQauncLxqssLxGCHKspRc+o4Kx9m053mtGTUdbNSotsOA5nG61CRCPhZkyFHmEUdlWQqkymNDsGKCiAxILAWRYIklADjbbcJN/3sYdpcbT24pxR3Lsmf09QhiJjGOOFDTzgqnKk/qyDJO4fjcFJmfbUG0F473mqxcKq2mw4AznUaMOFwB++Vq4lGWqcCiLAXKMpWYmyolZ2yCuAxILAWRYIolAPjzgWY8ufscYkR8/OuB1chPTJjx1ySIYOCNoFS3j6buxiscT5JK/OqeIrlw3OtzxaXU2vXoNloD9pPGCNloUaYCZVlKLMxUQBVPXWoE8UUgsRREgi2W3G4Gd758DIcbB7EgQ463v7uSriaJqKXXZPWre6rvHr9wfGGGnK19ygrfwnGGYdCus4wWYXcYcLbbFDAuxGvD4E2llWcpkKehWi6CmG5ILAWRYIslAOgxjmDTMwdhHHHg+2vz8e8b5wbldQki1Fgd7FR7rvapfeLC8cUevyfWcTz4YsNkdeB0h5ETRrUdhnHXqkmQoCyLFUZlmUosyJAjfpbXaRFEMCCxFERCIZYA4F+ne/D9v1WDxwPeum8FluZOffgvQUQLDMOgZWCYE05VbXpc7B0K2M83jbUoS4GFGQqopzH65HIzaOgz+7XuN/YPBRSwiwV8lKTLUJap5ARSuiK6a7AIIlwhsRREQiWWAOBH/3cKb1d3Il0Riw8fWg1ZjCior08Q4YjR4kB1h57zfartCCwcB4BMVSwWZbKdY4syFShJk03Zc6jfbPPUGbHC6HRnoKu59zVGhZES81KlEVtfRRDRBomlIBJKsWS2OnD9Hw6iQzeCLWXpePq2RUF9fYKIBHxb72vbDajt0I87IFbI52FeqowTT4uyFMhVx8PhduNst2nU06hDP+4MtXixAAszR9Npi7IUYVk7RRAEC4mlIBJKsQQAJ1t1uPWFSrgZ4PEvzcO3VuZQwTdBXALjiAOnO1nxdKqTrScaGAqsJ5oIHg8oTErgokaLshQoTJJCQEXYBBExkFgKIqEWSwDw+70X8IdPGwEAafIYfPuqXHx9adasN/MjiEsxbHPidKcR1e16/PN0D871mKb0vGvmJOKqAg3KshQoSZPTyBCCiEBILAWRcBBLTpcbLx5qwYsHWzAwxE4Rl8YIcceybNy1KgfJspiQrIsgwgm3m0HzwBCq20c9jS72mjGmcx9CPg8laTKUpMsRJxKAxwN0ww6c6jSgsS+weNw3fbfQk8LL08RTqz9BhDkkloJIOIglL1aHC+/WdGHHwWZuHIJIwMNNi9Jx35o8FCZLQ7o+gggm+mH7aBG2p3XfPM78tHRFLOdpdKlIkdcOoLaDLRyfKH0nixFywskroqh+iSDCCxJLQSScxJIXt5vBx+d6seNAM0626bnt6+Ym4b41eViaq6JWZSKqcLjcON9jRk2HnosatQ5aAvaLFQkwP0POFWGXZSm+UOSVYRh0GUZY4dTOiqczXUbYnO6AfTOUsZx4ovQdQYQeEktBJBzFki9VbXrsONCEvWd7Oc+XhZkK3L8mDxtKUqgglYg4Lkeg5CXG+3kazUmWQjjDDRAOlxsXtGYu8lTbMXH6bm6q1COglJS+I4ggQ2IpiIS7WPLS3D+EPx9swdvVnbB7Tio56jjcvToPX1ucQVe4RNgyZHPidIeBS6XVtBu42jxf5LEin3SaEosyFJDHhYf3mDd9d6qTXT+bvgt8D9IYIRZmKPzsCyh9RxAzA4mlIBIpYslLv9mG1ypb8VplG4wjDgCAKl6Mb63IwZ0rsmk4JxFSnC43LvYOeSIybF1QQ1+gE/ZEnkiREpXxjY6d6hiNjlkdk6fvFmUqUJpO6TuCmA5ILAWRSBNLXoZtTvzfyQ68eLAFXQbWYC9GxMetFZm456o8ZKnjQrxCYjagNVpR28EWYNe0G1DXZRzXbTtdEYtFWZ5xJVEqGMam7051jD8yxTd9tzCDjaTRoF2CuHxILAWRSBVLXpwuN3bXabHjQBPquliPGT4P2Dw/Fd9Zk4cFGYrQLpCIGix21tPIt9ZIa7IG7JcgEWKBpwh7UaYSCzPlSJLOTvsLk9WBM97fmedfv3ny9J23Cy9RSuk7gpgMEktBJNLFkheGYVDZNIjnDzTjwMV+bvvyPBW+syYf18xJpA46Ysq43Qwa+4dQ2z5aa3RBawrwNOLzgDkpMr8ht/mJCdR4MAEMw6DbaOXGtkyWvhsbjStJkyNWHF3ROIL4IpBYCiLRIpZ8Oddjwp8PNOP9U91wes5uRckJuHd1Hm5clA6xkMapEP70ma1ctKi2w4DTnUYM2QI9jVJkMVwR9qJMBeZnyBEnJqf5L4LT5caFXrNfxG689J2Az8PcFKmffQGl74jZDImlIBKNYslLt2EErxxuwd+OtXMT1VNkMbhrVQ6+sSwLspjw6DQigovV4UJdF5sa8g6n9da9+RInFmB+utwnuqFEinx2ptOCjdmTvqu5VPpOIsSCTDlnX1CepYCauu+IWQKJpSASzWLJi3HEgb8da8crh1vQ5/nCTZAIcfuyLNy1Kgep8tgQr5CYKdxuBi2Dw552dzbtc77HzEUcvXgHy3pPumVZChQmJcy4pxExNbzpO2/nXW07m74bcQQW02er41CWqUB5thLlWUrMTZl5byqCCAUkloLIbBBLXmxOF96r7caOA82cyZ5IwMMNC9lxKnNSaJxKpKMbtrOiyFNrdKrDANM4I0I0CRIulVbmSadJKdIYUXjTd6c841tq2lmbhrHEigSegns28lSerSTvJyIqILEURGaTWPLidjP47EIfXjjQjOMtOm77NXMScfdVuViep4aIrkTDHpvThfpuk1+tUbsucESIRMhn02meAuxFmQqkK2Kp4D8KMY44UNthQHWb185BP+48vSxVHMo95p/lWUrMTZXS3zwRcZBYCiKzUSz5UtOux44DzdhTr+UKShMkQqzIV2NNUSKuLkwkz6YwgGEYtA1aOFFU02HA2W4jHK7Ar4C8xHhPAbASZZkKzEmhE+Fsxe1m0NQ/hOp2ParbDKjp0I9rEhoj4mNBhgLlWWwKtjxLSdYFRNgTdWJJr9fjgQcewPvvvw8AuOGGG/DHP/4RCoViwuds3boVf/nLX/y2LVu2DEePHuXu22w2/PjHP8bOnTsxMjKCdevWYfv27cjIyJjy2ma7WPLSOjCMlw614F9neqAb9p/CnqOOw9VFiVhTlIjleWrES6j7aaYxWOx+3jynOgzQWxwB+6nixX7u0AvDaEQIEZ4YRxw41WFgBVS7AbXt+nFTtZmqWJR7Ik9lWQrMS5WR6CbCiqgTS5s3b0ZnZyd27NgBALjvvvuQk5ODDz74YMLnbN26Fb29vXjllVe4bWKxGCqVirv/3e9+Fx988AFeffVVqNVq/OhHP4JOp0NVVRUEgqn5kZBY8sftZlDXbcSBi/04cHEAVe16uHyKgUUCHiqyVVhTlIg1RRoUp8oonfMFsTvdOK81+bWONw8MB+wnFvBRnCYbnZ+WqUSmitJpxBfD7WbQPDCE6javgJok+pSuQFk2+9krz1bMWrNRIjyIKrF07tw5FBcX4+jRo1i2bBkA4OjRo1ixYgXOnz+POXPmjPu8rVu3wmAw4N133x33caPRiMTERLz++uu47bbbAADd3d3IzMzE7t27sXHjximtj8TS5JisDhxpHMSBhn4cuNiPTr1/i3miVILVhRpcXZSIqwo01LZ8CRiGQad+hBsoW9uhR123iRuO7EuOOs5ndpoS81KlkAjJlJCYeUxWT/TJI6BqJog+ZSi90Sc27VucRtEnInhM9fwdEbmQyspKyOVyTigBwPLlyyGXy3HkyJEJxRIA7N+/H0lJSVAoFLj66qvx5JNPIikpCQBQVVUFh8OBDRs2cPunpaWhtLQUR44cmVAs2Ww22GyjfiUmk+mLvsWoRhYjwqbSFGwqTQHDMGgZGGajTg0DqGwaRL/Zhl3VXdhV3QUeDyhNk3Mpu7Isxaz/4rQ73ajrNuJkqw4nWtmTzsCQPWA/eayIG3NR5hl5QUORiVAhixFhdWEiVhcmAvBGn4Y54VTdZsDFPjM69SPo1I/g/VPdANhmggUZck/qjhVRSTKKPhGhJSLEklar5QSOL0lJSdBqtRM+b/Pmzfja176G7OxstLS04Cc/+QmuvfZaVFVVQSKRQKvVQiwWQ6lU+j0vOTl50uM+9dRTeOKJJ678Dc1ieDwe8hITkJeYgK2rcmFzunCyVY8DF/vx+cV+nNeacabLiDNdRvzps0ZIfQvFixKRqYr+QnGz1YHqdgNOtupwvEWH2g4DbGOiRkI+j0unef/lauIpnUaELXw+DwVJCShISsCtFZkA2M/6qQ4jl7qraTfAOOLAiVY9TrTqueemK2I9nk9s4fi8VBlNESCCSkjF0s9+9rNLio4TJ04AwLgnAYZhJj05eFNrAFBaWoqKigpkZ2fjX//6F26++eYJn3ep427btg2PPPIId99kMiEzM3PS90GMj0QowKoCDVYVaLDt+nnoM1lxoGEABy7242BDP/QWB/ae7cXes70AgDxNPFfrtDxPHRVjMnpNVpxo1eFkqx4nWnU41xM4P00ZJ0JFjgpLcpRYnK1ESZocMSJKpxGRjTRGhKsKNbiqUANg1AC1uo0tHK9p1+NCrxldhhF0GUbwgU/06f+3d+fRUdb3/sDfsyeZzEz2yUpCIBsEKBDZ/FFlEXGv1oKVVulFrl6Pl1JFr9Z7BNpeObW3Xve23oPitQjUWmtdC4KyiCJgICAQ1iSE7Nssmcz+/P54ZiYzmUlIMJnJJO/XOZxknnmeme88JJn3fL+f7/eZlKXzBaipYxKhZ+8TDaGIvtM89NBDuOuuu/rcJy8vDxUVFWhsbAy6r7m5GXq9vt/Pl5GRgdzcXJw5cwYAkJ6eDrvdjvb29oDepaamJsyZM6fXx1GpVFCpWFczFNK0MbhzejbunJ7tKxTfXdmMPWea8U2NWLR8vqUTm/ZXQSmToiwvUQxPBakoydAM+54VQRCnYR/0BKODVW242BZ8mZCcpFhclZfk+ZfI63fRqCCVSjAuNR7jUuPxI7/ep4pagydAiWs/dVgcOFTdjkPVgb1P3iULpuUmYgJ7n2gQRVWB94EDBzBjxgwAwIEDBzBr1qw+C7x7am1tRVZWFl599VXcc889vgLvP//5z1iyZAkAoL6+HtnZ2SzwHoYuVyieplFhboHY6zS3IHVY1Ov0rDc6VNUWNH1fIgFK0rWYMTYJZXmJKMtN4vXTiHrhrXv8psYz8666HacbTUG9sUpv75NfgGLvE/U0ombDAWL9UV1dHf70pz8BEJcOyM3NDVg6oLi4GBs2bMDtt98Os9mMdevW4Yc//CEyMjJQVVWFX/7yl6ipqcHJkyeh0YiX5fi3f/s3fPDBB9i0aROSkpKwZs0atLa2cumAYU4QxGJRcXmCZnx1vi3gGlcSCTApy69QPCchLNe2MlkdKK/p8PUaHbnYAasjsN5IJZfiezkJnnCUhKljEnhBYqLvwGxzosJv3afymvaQa4pl6mIw1XO9u2ljEjAxU8fep1FuxIWltra2oEUpX3rppYBFKSUSCV5//XUsX74cXV1d+MEPfoDy8nJ0dHQgIyMD8+bNw69//euA+iKr1YpHH30Ub731VsCilAOpQWJYirxQheL+NCo55oxP9g3ZDVaheJPRGjCkFqreKCFOgbLcJMwYm4iyvCSU8g800ZDy9j6V13QHqMqG4N9NpVyK0kytr+dp2phE9uqOMiMuLA1nDEvDT6PR6lueYJ+nUNxffqoa3y8QZ9jNzE/qV6G4WG/U6RtSO1jVFvI6ajlJsbgqNwlXjWW9EdFw4e19Kvdc9+6bXnqfMnQxmDYmET+cnoX5xf2viaXoxLAURgxLw5vLLeD4Jc+K4p5Ccf8VxZUyKa4amyiGp6JUFOnFQnGHy43jlwy+WWqHqtuDLuPirTe6Ki8RV41NYr0RUZQQBAFVrRbPBYPFdZ9O+fU+/edNJbhvbn5kG0lDjmEpjBiWootYKN6C3afFJQoudQTPRuuNt97oqjyx54j1RkQjR6fNiaO14sr4iyboUaDXRLpJNMQYlsKIYSk6eeuN/nLoInafbr7s/j+dlYu7Z45BkV7DYTUiohGAYSmMGJaGP+/sOXFV7HYcqm5DdWtwvVGaRlw/q8vuQpfDBWfPilCIxeKlWTpMztZhcnYCJmfrkJ3Ii9ESEUWbEXVtOKKBcrjc+LbO6CnGFlfHbu2j3qgsT1zjKEMXG7BPo9GKr8634uhFAypqO3C8zgCTzYkvz7fiy/Otvv2S1UpM8oSnKZ6vqRouXEpENBKwZ2kQsGcp8sw2J8pr2n0LP5bXdASsuwQE1huV5YlThQdab+R0uXGmyYyK2g4crTXgWK0BpxqMcLiCf40ydTFiz1OODlOyE1CapYMulvVNRETDBYfhwohhKfysDhe+Ot+K3aebcaiqHSfqjQEz3IDu9Y28PUelWVqo5IN/PTWrw4VTDSYxQHl6oM42mxHqNys/RR3QAzUxU4dYJa/xRkQUCQxLYcSwFB6NRis+O9WEnaeasO9MS1DPUXZiLGbkJfkuODsuNXLrG5ltThy/ZPD1QFXUdoS8BpxMKkFBWjym+PVAFaVroAjDauNERKMdw1IYMSwNDe+FdHeebMKuU004dskQcH+6NgbzitMwZ1xyyHqj4aat046K2g4cqzX4AlSTyRa0n1IuxYQMra/2aUqOjgtbEhENAYalMGJYGjydNif2nW3BrpNN2FXZhGa/MCGRAFOyE7CgOA3zS9IwIUMb9TPQGgxWHK3tQEVtBypqDaioNcDQFbyqsFopQ2mWDlNyxNl3U7ITOAOPiOg7YlgKI4al7+ZimwW7PMNrX51rhd3VfeFZtVKG7xemYn5xGq4tShvxM8wEQUB1q8UToDwz8C4Zg4YcASBJrcSkLJ2vB2pyjg5pGq4eTkTUXwxLYcSwNDBOlxvlFzs8w2uNON1oDrh/TFIcFpSkYUGxHjPGJo36i846XW6cbTaj4qIBR2s7cOySASfrQ8/Ay9DFBKz/NDkrAbo4zsAjIgqFYSmMGJYuz2BxYPeZZuw62YjPTzejw+8CljKpBGW5iVhQkob5xXqMS1VzeOkybE4XTtWbAgrIzzSFnoGXlxznC09TchIwMVPbrwsHExGNdAxLYcSwFEwQBJxr7sSuU43YebIJh6rbA6b2J8QpcG1hKuaX6HFNQSp7PwZBp28GnsE3jFfTFrxKuVQCFOo1vh4o7wy80d6DR0SjD8NSGDEsiexON76+0Iadpxqx61RT0OVECvXxmF+sx4KSNEzNSYCc0+OHXHunHRWXDKi42NH3DDyZFCWZ3TPwJmfrMC41HjLOwCOiEYxhKYxGc1hqMdvw2Slxav/eMy0w25y++5QyKWaNSxZnrxWnIScpLoItJa9GoxVHL3YE9ED1NgNvol8BeWmWDmOS4higiGjEYFgKo9EUlgRBwIl6I3adFGevHa3tCKiTSdWoML9InNr//8anQK1ibcxwJwgCatosYs+TJ0QdrzPAYg+egaeSS1Ggj0eRXoui9HgU6jUoStcgXRvDOjMiijoMS2E00sNSl92F/edasPNUEz471YR6gzXg/klZOswvTsOCkjSUZuq4eOII4HILONtkFmffeYbvKhtNsDrcIffXxshRlK7xhadCvQbF6RokxCnD3HIiov5jWAqjkRiW6jq6sMszvPbF2RbYnN1vkrEKGf5fQQoWFKdhXnEa9Fqu7TMauNwCLrZZcKrBhNONJlQ2mnC6wYTzLZ1B1+XzStOoAkJUkV6DAn08Z+MR0bDAsBRGIyUsfVtnwMfHGrDzVBNO1hsD7stKiPVM7U/DrPxkxCh48VcS2ZwunG/uFANUg+dfowm17cHXwgPEldhzEuN84anQ83Vsipoz8ogorBiWwijaw1JFbQee+/QMdp1q8m2TSoBpYxIx37M4ZKE+njUpNCBmmxNnGsVeKF9vVIMZLebg2XgAoJBJkJ8S7wlP8Z6hPC2yE2M5tEtEQ4JhKYyiNSwdqzXguU9PY6cnJMmkEiyaoMeiiXpcU5iGJDXrTWjwtZptviG8ykYzTnu+N/nNpPQXq5ChUN9dTO7tkUrVqBjgieg7YVgKo2gLS8cvGfDcp2fw6clGAGIv0g+mZmHV/ALkpagj3DoajQRBQJ3B6glQ3cN5Z5vNsDtDF5UnxCl8heT+heW6WC5wSkT9w7AURtESlr6tM+D5T89g+4nukHTb97Lw7/PHIz81PsKtIwrmdLlR3WbB6QZTQGF5VUsneqkpR4YuJiA8eYvKWWdHRD0xLIXRcA9LJ+uNeO7T0/jnt2JIkkiA26Zk4t8XFGAcQxJFIavDhXPNZl8x+ekGE043mnGpo/ei8rxkNQr18SjSa1CULq4TlZes5kryRKMYw1IYDdewdKrBiOc/PYOPjzcAEN8wbpmciVULCjA+jSGJRh6j1YEznkJysbDciMoGE9otwSuUA+Iq8/mpanEozzs7T69BdmIs66GIRgGGpTAabmGpssGE53eexkfHukPSzZMzsWr+eBToNRFuHVF4CYKAFrO9e1aetzeq0RRylXJAvNSLf3jyFpanxKvC3HoiGkoMS2E0XMLS6UYTnt95Bh8dq4cgiCHpxkkZ+PmCAhQyJBEFcLsFXOro6h7K8xSWn2s2w+EK/WcxWa0MCE+Feg0K9fHQxLConCgaMSyFUaTD0hlPSPrQE5IA4KZJGVi1oABF6QxJRAPhcLlR1dLpq4XyFpZXt1nQ21/LrIRYFOjjMS41HuPTur9y+Q2i4Y1hKYwiFZbONpnxws4zeL+izvdH/IbSdPx8YQGK0yM/HEg0knTZXTjbZPYsbWAU14hqMKHBaO31mMQ4RUCAGpemxvhUDbISYyHjQptEEcewFEbhDkvnms14cecZ/ONonW/69PUT9fj5gkJMyGRIIgong8WBykYTzjaZcbbJjHPN4r/eLvcCACq5FGNT1BiX5t8bpUZ+SjxilVzigChcRlxYam9vx6pVq/CPf/wDAHDrrbfixRdfREJCQq/H9Dab5ZlnnsGjjz4KALj22muxe/fugPuXLl2KrVu39rtt4QpLVS2deH7nGbx35JIvJC2aoMfPFxZgYqZuyJ6XiAauy+7C+RYzzjV3doeoJjPOt3T2utCmRCIO6QX0RqWqfUN6nKFHNLhGXFi64YYbUFtbi1dffRUA8K//+q/Iy8vD+++/3+sxDQ0NAbc//vhjrFixAmfPnkV+fj4AMSwVFhbiV7/6lW+/2NhY6HT9Dx/hCEtGqwOzn96JTs/snYUleqxeWIDSLIYkomjicgu41N6Fs80mnGvqDlJnm83o6GWJA0BcsXx8qt9wnidMZSfGcUiP6Ar19/1bHsY2XbGTJ0/ik08+wVdffYWZM2cCAP73f/8Xs2fPRmVlJYqKikIel56eHnD7vffew7x583xBySsuLi5o3+GmptWCTrsLmhg53rpvFiZlMyQRRSOZVIIxyXEYkxyH+cWB97WabYE9Uc3i0N6lji50WBw4VN2OQ9XtAcco5VLkp6g9IUrsiRrnCVUc0iMaHFERlr788kvodDpfUAKAWbNmQafTYf/+/b2GJX+NjY348MMP8cYbbwTdt3nzZvz5z3+GXq/HDTfcgLVr10Kj6X0Wmc1mg83WfeV0o9E4wFc0cM0m8fnGJMUxKBGNUMnxKiTHqzBjbFLA9i67CxdaOnHWM5R3tseQ3inPrL2eshJiexSXi4EqmUN6RAMSFWGpoaEBaWlpQdvT0tKChtp688Ybb0Cj0eCOO+4I2L5s2TKMHTsW6enpOH78OJ544gkcPXoUO3bs6PWxNmzYgPXr1w/sRXxH3rCUquGieESjTaxShgmZ2qAJHN4hPW8PlH9vVLvFgUsdXbjU0YXdp5sDjkvwzNLz1kN5e6JykjikRxRKRMPSunXrLhs6Dh48CCB0sbYgCP3+dPTaa69h2bJliImJCdi+cuVK3/elpaUoKChAWVkZvvnmG0ybNi3kYz3xxBN4+OGHfbeNRiNycnL61Y4r1Wz2hCWuIExEHv5DevOKAz9QtnXau0OUtzfKM0uvw+LA4ep2HO45pCcTZ+l5Z+d5Z+vlp6oRp4yKz9ZEQyKiP/0PPfQQ7rrrrj73ycvLQ0VFBRobG4Pua25uhl6vv+zz7N27F5WVldi2bdtl9502bRoUCgXOnDnTa1hSqVRQqcIbWpo8a7mkaRmWiOjyktRKJKmTcFVe4JCe1eHC+ebOHr1RnTjfbIbN6RbXkWoMPaTnrYny741KieeQHo18EQ1LKSkpSElJuex+s2fPhsFgwNdff40ZM2YAAA4cOACDwYA5c+Zc9viNGzdi+vTpmDJlymX3/fbbb+FwOJCRkXH5FxBG7FkiosEQo+h9SK+uoytoOO9ccyfaOu2+Ib09PYb0dLGKoAA1Pi0e2YmxkMuk4XxpREMmKvpVS0pKsHjxYqxcuRJ/+tOfAIhLB9x8880Bxd3FxcXYsGEDbr/9dt82o9GIt99+G7///e+DHvfcuXPYvHkzbrzxRqSkpODEiRN45JFHMHXqVFx99dVD/8IGoLtmKeYyexIRDZxMKkFOUhxyknof0jvXFNgbdbHdAkOXA9/UdOCbmo6AY5QyKfJS4oJC1NgUNdSqqHjrIfKJmp/YzZs3Y9WqVVi0aBEAcVHKl156KWCfyspKGAyGgG1bt26FIAj48Y9/HPSYSqUSO3fuxPPPPw+z2YycnBzcdNNNWLt2LWSy4TXltskTljgMR0Th1teQ3oWWzoAAdbbJ7BvSO91oxulGc9DjZepifPVQvqG91HikalQc0qNhKWoWpRzOwrEo5YSnPoHF7sLna65FXop6SJ6DiGgwuN0CLnV0+ZY4ONfc6flqRmunvdfjNDHygF4ob5H5mKQ4KDikR0NgRC1KOdp12pyweFbu5tIBRDTcSf2H9IoCh/TavUN6zYEhqqbNApPViSMXO3DkYkfAMQqZBLnJ6oAFN8enibP0NDGKML4yGq0YlqKA0dp9CYSlr36J5XPG4ubJGYhRDK+hQiKiy0lUK1GmTkJZiCG96lZLd22UN1A1daLL4fJdqBgInBmt16p6XEtPXIAzXRvDIT0aNByGGwRDPQzndgv49Ycn8NaBGtg8F+BMUitx94wx+MmsXKTrWPRNRCOT2y2g3mj19UD5z9LzTnwJRa2U+eqixvtdBiY3WQ2lnEN6JBpxF9IdzsJRswSIM1K2HqzBm19Wo94grrskk0qwuDQdP5uTh+m5ifwkRUSjhqHLgfN+4ckbpqpbLXC5Q7+1yaQS5CbFId/TA+XfK6WL5ZDeaMOwFEbhCkteTpcbO0404vX9Vfj6Qptv+8RMLZbPycMtUzI5REdEo5bd6UZNWyfONnX6hvW8NVJmm7PX41LiVYFrRnl6pDJ1sZDyMjAjEsNSGIU7LPk7UWfEG/ur8PcjlwKG6H48Iwc/mZWLDF1sWNtDRDRcCYKAJpOte6mDpu7lDho8V0kIJVYhQ36P4vJxaWrkJav5wTTKMSyFUSTDkld7px1bD17En7+qxqWOLgCeIbqJ6bh3Th6uyuMQHRFRb8w2J87710R5eqWqWjvhcIV+m5RIgJzEuICaqHFp8RifGo9EtTLMr4CuBMNSGA2HsOTldLnx6clGbNpfha/Odw/RTcjQYvnVebiVQ3RERP3mdLlR02bpronyzNQ722SGydr7kF6SWukZ0tOgOF2DonTxa0IcQ9RwwrAURsMpLPk7WS8O0b1b3j1ElxinwI89s+gyEzhER0R0JQRBQIvZHnRB4nNNZl/vfih6rQpF6VoxQOnFEDU+LZ4fYiOEYSmMhmtY8mrvtGPboYt488vAIbrrJ+qxfM5YDtEREQ0ii92J856eqNONJlQ2mHCqwYTa9tAhSiaVIC85DsXpWhT59ULlJMaxsHyIMSyF0XAPS17iEF0T3thfhS/Pt/q2ZyXEojRLi5IMLSZkiF+zE2MZoIiIBpHJ6sDpRjMqG0yobDDiVIMJlY0mdFgcIfePU8pQoNegWN8doIrSNUiO55UcBgvDUhhFS1jyd6rBiDf2V+Pd8lpYHe6g+7UxcpR4gtOETDFEFejjoZKzq5iIaLB4Z+id8g9QDSacaTLD7gz+2wyISxx4g5M3RBWkaRCr5N/ngWJYCqNoDEteJqsDxy4ZcKLOiJP1JpyoN+Jskynk7A+5VILxafG+HqgJmWKYSuKsDyKiQeV0uVHVagnqhappsyDUu7ZEAuQlq311UN4wlZushoxDeb1iWAqjaA5LodidbpxtMuNEvREn6404UWfEiXojDF2hu4rTtTGe4KTBhAwdSjI0yEtWc6ydiGiQWexOz1Bedy9UZYMJrZ32kPvHKKQoSAsMUEXpGqTGq1hqAYalsBppYSkUQRBQb7B6eqDE8HSi3ojqVkvI/eOUMhSnawKG8YrSNYhT8trNRESDrdlk8xSSG8UA1WjC6UZTyDILQFzaoGcvVKFeA7VqdP2NZlgKo9EQlnpjtjlxqt4vQNWJn3ZsIcbaJRJgbIraV0TuDVFpGn7CISIabC63gJo2S1AvVFVrJ3q5dB7GJMWhUN8doCZn6zAmKW7E/o1mWAqj0RyWQhHH2jvxrV8d1Ik6I1rMoa8QnqxW+uqfvLVQ+SlqyGW8MjgR0WCzOlw402gO6IU61WBCsyn032i9VoWZY5MxY2wSZuUnYVxq/IgJTwxLYcSw1D9NJitO1psC6qDON5tDfsJRyqUo0ms8dVBaTMjUoThDA20MrwpORDQU2jrt3QGqwYSTDSacrDPC7gocKUhWKzFjbBJmjk3CjLHJKE7XRG2NKsNSGDEsXTmrwyX+UvYYxuvtyuCpGhXStTFI18UgQyd+7b4di3RtDKfPEhENEqvDhfKaDhy40IoD59vwTU17UJmFNkbuCU/JmJmfhAkZ2qgZGWBYCiOGpcHldgu42G4J6IE6WW/q8xIC/hLiFIGBShuLdJ0K6bpYX8DSqOQjphuZiChcbE4XjtUacOBCGw5caMPhqjZ02l0B+8Sr5Jiem+gbtpuUlQClfHiGJ4alMGJYCg+DxYGL7RY0GKyoN1rRaLCi3mBFg7FL/GqwwtLjl7Y3aqUMer8wlaGLEW/7hawktZKBioioD06XG9/WGX09T19XtQVdYDhGIcW0MYm+uqepYxKGzbXwGJbCiGFpeBAEASabUwxTBisaDF1oMNgCwlS9wdrrelE9KWVS6HUqZGhjfQFKr+0e/svQxSJVo+KCb0REHi63gFMNRnx9oc0Xntp6rAGllEnxvZwEceguPwnTcxMjtqwMw1IYMSxFly67Cw1GK+oNXWgwWNFgtPoFLPF2i9kWcpXcnqQSIE3Tew1Vhi4GaVoVLxNDRKOSIAg422TGVxfaPAGqFU09Zt3JpRKUZukwMz8J1xamYfa45LC1j2EpjBiWRh67040mU3eIajRa/XqnutBotKHBaIWrt8VKekhWK0MEqtiA26NtMTgiGn0EQUBVqwVfe4btDlxoC6pHXbWgAL9YWBCWMgiGpTBiWBqdXG4BrWYb6oMCVVfA7VALdIaiiZF7wlMsMrRij5QuVgFdrAIJcUokxHm+j1VAF6dgbxURjQi17RYcON+GPWea8d6ROgDAyrlj8csbS4Y8MDEshRHDEvVGEAR0WBwhw5R3+K/BYIWpl6US+hKrkHmClMIvVInBKuB2rLJ7vzgFZwIS0bD1+hcXsP79EwCAZTPH4Ne3lQ7pGk79ff9mvz/REJJIJEhUK5HoWaW8NyarA41GKxoMNl8tVbPZBkOXAx0WBzq6HDB2OdBhscPQ5YBbALocLnQ5xPqrgZBJJdDGyEOEKgV0nm0JfiFM/CpuH67Tf4loZPjZ1WMRp5Th8b8dw+YDNbA63PjtDydFfN0mhiWiYUATo4AmRoHxaZrL7ut2CzDbnTBYvEHK7gtVBr9AFRiyxP2sDjdcbgHtFgfaLf2bFegvTinzC1VyJMQqfT1WYshShuzpUitl7M0ion5ZetUYxChkePgvR/HON7WwOl14bun3oIhgYGJYIooyUqkE2hgFtDEK5CQN7FirwyWGJ2+A8gSrgLDVFbzdaHVAEACL3QWL3YU6w8B6s+RSiRig/Oqugnq2fCFL6evp0sYqIvoHkojCy+0WcL6lEy63gAkZWhy7ZMCHFfVIVivxq9tKI9YuhiWiUSRGIUOMQoY0bcyAjnO5BZisjoAeK0OXAwaLPeC2GLj8erYsDthdbjjdAlo77Wjtsd5Kf8Sr5AGhSq2SI04pQ5xShliF+H2s57b4vRxq3zbP/Qrv/XLEKKTs5SIaBgRBQG17FypqDai41IGKiwYcv2QIWcMZ6d9YhiUiuiyZVOKZkadE7gCWQBEEAVaH29NjZfcFKKP/bV/wChxS9K4CbLY5YbY5+325m8uRSOAJT36hq0f48m5TK+VBQSxO4X9M9/4MYkR9azbZUFHbgaO1BlTUduBYrSHkB6gYhRSlmTpMzk7AlBwdJmXpMDZFHYEWd4uasPRf//Vf+PDDD3HkyBEolUp0dHRc9hhBELB+/Xq8+uqraG9vx8yZM/Hyyy9j4sSJvn1sNhvWrFmDLVu2oKurCwsWLMArr7yC7OzsIXw1RKODRCJBrCdYpOsG1pvldLlhtDp9dVgdnkDVaXeiyzMcaLG70GV3it87XOiyu9Bpc6LL4b3PBYvnfu8SDv7DiYP/euHrxYpVyhCnEMOWWhUcxLwBy9frpQodxLqDHOu+aHiyOV3otIm/e2ab0++rC1Wtnaio7UBFrQH1IYbv5VIJijM0YjDKFgNSQVp8xAu6e4qasGS32/GjH/0Is2fPxsaNG/t1zDPPPINnn30WmzZtQmFhIX7zm9/guuuuQ2VlJTQasZB29erVeP/997F161YkJyfjkUcewc0334zDhw9DJuM6NkSRIpdJkaRWIkmtBPDdP1W63IInRDlhsXnClMPpF7q8AcwTxhwuWGyBQczSM6h5Hs/qGPogBiAwbClC93rFKmVQyCSQy6RQyKRQSCVQyKWQSyVQyqWQS6VQyCRQyKSQe776bkulUMolnn2kfo/j3U88RikTH08mlTDARSG70+0LNBa7yxdw/MNOp2e7xeaE2ROEOu1+99s899udcLj6twKRRAKMT43H5OwETM7WYXK2DiUZ2mFznbi+RN06S5s2bcLq1asv27MkCAIyMzOxevVq/Md//AcAsRdJr9fjt7/9Le6//34YDAakpqbizTffxNKlSwEAdXV1yMnJwUcffYTrr7++X23iOktEo5t/EAvs9fJsc4ifvP2DmH+vl+8Yv4DWM4gNRxIJoJAGhypfCJNKofCEL2WPcCaXSqGQe8JcqOB2maAXENw8j9cz6ClkUt+1GwUBcAsCBIjvD4JnW8D3ELr387wzBm33bINvW/BjwLvds4/vOf23ebZ3P5f/voFtDWi//+OJh8HqdAWFGP8wFBCCbC7YXUPzMxWjkCJeJffUFcoRrxLrI709RqVZOsQPsysVjPp1li5cuICGhgYsWrTIt02lUuGaa67B/v37cf/99+Pw4cNwOBwB+2RmZqK0tBT79+/vNSzZbDbYbN3XtjEajUP3Qoho2JNJJYhXyYfkjcDtC2KeUOXtDbN1BzH/IckuhwtOlwCHS4DD5YbT7YbdKcDpdsPhcndv93x1eArw7U7xq/c+u8sNp9/+DpcbPa/uIwiA3eWG2JE2NL1pNHSUcm+4EevzvEHHe1ut6t4Wr5J57pN77pP57S9OqhhuQ2eDacSGpYaGBgCAXq8P2K7X61FdXe3bR6lUIjExMWgf7/GhbNiwAevXrx/kFhMRBZNKJb43pEhzuwU43GKAcrrcnkAlBIWwgKDlDgxm3mMDjvGFtVBhbmChz+F0w+EOvE8iAaQSCSQQ6+gkAODdJkHAdknANkACie94eLd5tkslgY/nPV7qdxz8t/ltl3geTBr0/D2+93t+/8frvl+c5eoNLGpV7yEn3jOT1Hsfl+Xov4j+9q1bt+6yoePgwYMoKyu74ufoOZ4uCMJlx9gvt88TTzyBhx9+2HfbaDQiJyfnittIRBQNpFIJVFIZhkFuIwqriP7IP/TQQ7jrrrv63CcvL++KHjs9PR2A2HuUkZHh297U1OTrbUpPT4fdbkd7e3tA71JTUxPmzJnT62OrVCqoVKorahcRERFFl4iGpZSUFKSkpAzJY48dOxbp6enYsWMHpk6dCkCcUbd792789re/BQBMnz4dCoUCO3bswJIlSwAA9fX1OH78OJ555pkhaRcRERFFl6jpTK2pqUFbWxtqamrgcrlw5MgRAMD48eMRHx8PACguLsaGDRtw++23QyKRYPXq1Xj66adRUFCAgoICPP3004iLi8Pdd98NANDpdFixYgUeeeQRJCcnIykpCWvWrMGkSZOwcOHCSL1UIiIiGkaiJiw99dRTeOONN3y3vb1Fn332Ga699loAQGVlJQwGg2+fxx57DF1dXXjwwQd9i1Ju377dt8YSAPzP//wP5HI5lixZ4luUctOmTVxjiYiIiABE4TpLwxHXWSIiIoo+/X3/5rxBIiIioj4wLBERERH1gWGJiIiIqA8MS0RERER9YFgiIiIi6gPDEhEREVEfGJaIiIiI+sCwRERERNQHhiUiIiKiPkTN5U6GM+8i6EajMcItISIiov7yvm9f7mImDEuDwGQyAQBycnIi3BIiIiIaKJPJBJ1O1+v9vDbcIHC73airq4NGo4FEIhm0xzUajcjJycHFixd5zbkhxnMdHjzP4cHzHB48z+EzVOdaEASYTCZkZmZCKu29Mok9S4NAKpUiOzt7yB5fq9XyFzFMeK7Dg+c5PHiew4PnOXyG4lz31aPkxQJvIiIioj4wLBERERH1gWFpGFOpVFi7di1UKlWkmzLi8VyHB89zePA8hwfPc/hE+lyzwJuIiIioD+xZIiIiIuoDwxIRERFRHxiWiIiIiPrAsERERETUB4alCHvllVcwduxYxMTEYPr06di7d2+f+9tsNjz55JPIzc2FSqXCuHHj8Nprr4WptdFroOd58+bNmDJlCuLi4pCRkYGf/exnaG1tDVNro9OePXtwyy23IDMzExKJBH//+98ve8zu3bsxffp0xMTEID8/H3/84x+HvqEjwEDP9d/+9jdcd911SE1NhVarxezZs/HPf/4zPI2NYlfyM+31xRdfQC6X43vf+96QtW+kuJLzHO73QoalCNq2bRtWr16NJ598EuXl5Zg7dy5uuOEG1NTU9HrMkiVLsHPnTmzcuBGVlZXYsmULiouLw9jq6DPQ87xv3z7cc889WLFiBb799lu8/fbbOHjwIO67774wtzy6dHZ2YsqUKXjppZf6tf+FCxdw4403Yu7cuSgvL8cvf/lLrFq1Cu+8884QtzT6DfRc79mzB9dddx0++ugjHD58GPPmzcMtt9yC8vLyIW5pdBvoefYyGAy45557sGDBgiFq2chyJec57O+FAkXMjBkzhAceeCBgW3FxsfD444+H3P/jjz8WdDqd0NraGo7mjRgDPc+/+93vhPz8/IBtL7zwgpCdnT1kbRxpAAjvvvtun/s89thjQnFxccC2+++/X5g1a9YQtmzk6c+5DmXChAnC+vXrB79BI9RAzvPSpUuF//zP/xTWrl0rTJkyZUjbNdL05zxH4r2QPUsRYrfbcfjwYSxatChg+6JFi7B///6Qx/zjH/9AWVkZnnnmGWRlZaGwsBBr1qxBV1dXOJocla7kPM+ZMwe1tbX46KOPIAgCGhsb8de//hU33XRTOJo8anz55ZdB/y/XX389Dh06BIfDEaFWjQ5utxsmkwlJSUmRbsqI8/rrr+PcuXNYu3ZtpJsyYkXivZAX0o2QlpYWuFwu6PX6gO16vR4NDQ0hjzl//jz27duHmJgYvPvuu2hpacGDDz6ItrY21i314krO85w5c7B582YsXboUVqsVTqcTt956K1588cVwNHnUaGhoCPn/4nQ60dLSgoyMjAi1bOT7/e9/j87OTixZsiTSTRlRzpw5g8cffxx79+6FXM6316ESifdC9ixFmEQiCbgtCELQNi+32w2JRILNmzdjxowZuPHGG/Hss89i06ZN7F26jIGc5xMnTmDVqlV46qmncPjwYXzyySe4cOECHnjggXA0dVQJ9f8SajsNni1btmDdunXYtm0b0tLSIt2cEcPlcuHuu+/G+vXrUVhYGOnmjGiReC9k9I2QlJQUyGSyoN6NpqamoE/bXhkZGcjKyoJOp/NtKykpgSAIqK2tRUFBwZC2ORpdyXnesGEDrr76ajz66KMAgMmTJ0OtVmPu3Ln4zW9+wx6PQZKenh7y/0UulyM5OTlCrRrZtm3bhhUrVuDtt9/GwoULI92cEcVkMuHQoUMoLy/HQw89BEB8UxcEAXK5HNu3b8f8+fMj3MqRIRLvhexZihClUonp06djx44dAdt37NiBOXPmhDzm6quvRl1dHcxms2/b6dOnIZVKkZ2dPaTtjVZXcp4tFguk0sBfDZlMBqC754O+u9mzZwf9v2zfvh1lZWVQKBQRatXItWXLFixfvhxvvfUW6++GgFarxbFjx3DkyBHfvwceeABFRUU4cuQIZs6cGekmjhgReS8MWyk5Bdm6daugUCiEjRs3CidOnBBWr14tqNVqoaqqShAEQXj88ceFn/70p779TSaTkJ2dLdx5553Ct99+K+zevVsoKCgQ7rvvvki9hKgw0PP8+uuvC3K5XHjllVeEc+fOCfv27RPKysqEGTNmROolRAWTySSUl5cL5eXlAgDh2WefFcrLy4Xq6mpBEILP8/nz54W4uDjhF7/4hXDixAlh48aNgkKhEP76179G6iVEjYGe67feekuQy+XCyy+/LNTX1/v+dXR0ROolRIWBnueeOBuufwZ6niPxXsiwFGEvv/yykJubKyiVSmHatGnC7t27fffde++9wjXXXBOw/8mTJ4WFCxcKsbGxQnZ2tvDwww8LFoslzK2OPgM9zy+88IIwYcIEITY2VsjIyBCWLVsm1NbWhrnV0eWzzz4TAAT9u/feewVBCH2eP//8c2Hq1KmCUqkU8vLyhD/84Q/hb3gUGui5vuaaa/rcn0K7kp9pfwxL/XMl5znc74USQeC4AhEREVFvWLNERERE1AeGJSIiIqI+MCwRERER9YFhiYiIiKgPDEtEREREfWBYIiIiIuoDwxIRERFRHxiWiIiIiPrAsEREI8rGjRuxaNGiSDdjUN1555149tlnI90MolGLK3gT0Yhhs9mQn5+PrVu3Yu7cuUP6XMuXL0dHRwf+/ve/D+nzAEBFRQXmzZuHCxcuQKvVDvnzEVEg9iwR0YjxzjvvID4+fsiD0mCy2+2X3Wfy5MnIy8vD5s2bw9AiIuqJYYmIhp3m5makp6fj6aef9m07cOAAlEoltm/f3utxW7duxa233hqwbfny5fjBD36Ap59+Gnq9HgkJCVi/fj2cTiceffRRJCUlITs7G6+99lrAcZcuXcLSpUuRmJiI5ORk3HbbbaiqqgIArFu3Dm+88Qbee+89SCQSSCQSfP7555c9zr89GzZsQGZmJgoLCwEAr7zyCgoKChATEwO9Xo8777wzoD233nortmzZMtBTSUSDgGGJiIad1NRUvPbaa1i3bh0OHToEs9mMn/zkJ3jwwQf7rEfau3cvysrKgrbv2rULdXV12LNnD5599lmsW7cON998MxITE3HgwAE88MADeOCBB3Dx4kUAgMViwbx58xAfH489e/Zg3759iI+Px+LFi2G327FmzRosWbIEixcvRn19Perr6zFnzpzLHue1c+dOnDx5Ejt27MAHH3yAQ4cOYdWqVfjVr36FyspKfPLJJ/j+978f8BpmzJiBr7/+GjabbZDOMhH1m0BENEw9+OCDQmFhobBs2TKhtLRU6Orq6nXf9vZ2AYCwZ8+egO333nuvkJubK7hcLt+2oqIiYe7cub7bTqdTUKvVwpYtWwRBEISNGzcKRUVFgtvt9u1js9mE2NhY4Z///KfvcW+77baA5+rvcXq9XrDZbL593nnnHUGr1QpGo7HX13f06FEBgFBVVdXrPkQ0NOSRDmtERL357//+b5SWluIvf/kLDh06hJiYmF737erqAoCQ+0ycOBFSaXdHul6vR2lpqe+2TCZDcnIympqaAACHDx/G2bNnodFoAh7HarXi3Llzvbahv8dNmjQJSqXSd/u6665Dbm4u8vPzsXjxYixevBi333474uLifPvExsYCEHu9iCi8GJaIaNg6f/486urq4Ha7UV1djcmTJ/e6b3JyMiQSCdrb24PuUygUAbclEknIbW63GwDgdrsxffr0kAXVqampvbahv8ep1eqA+zQaDb755ht8/vnn2L59O5566imsW7cOBw8eREJCAgCgra3tss9PREODYYmIhiW73Y5ly5Zh6dKlKC4uxooVK3Ds2DHo9fqQ+yuVSkyYMAEnTpz4zussTZs2Ddu2bUNaWlqvU/WVSiVcLteAj+uNXC7HwoULsXDhQqxduxYJCQnYtWsX7rjjDgDA8ePHkZ2djZSUlCt7UUR0xVjgTUTD0pNPPgmDwYAXXngBjz32GEpKSrBixYo+j7n++uuxb9++7/zcy5YtQ0pKCm677Tbs3bsXFy5cwO7du/Hzn/8ctbW1AIC8vDxUVFSgsrISLS0tcDgc/ToulA8++AAvvPACjhw5gurqavzf//0f3G43ioqKfPvs3bt3xC22SRQtGJaIaNj5/PPP8dxzz+HNN9+EVquFVCrFm2++iX379uEPf/hDr8etXLkSH330EQwGw3d6/ri4OOzZswdjxozBHXfcgZKSEvzLv/wLurq6fD1GK1euRFFREcrKypCamoovvviiX8eFkpCQgL/97W+YP38+SkpK8Mc//hFbtmzBxIkTAYg1T++++y5Wrlz5nV4XEV0ZruBNRCPKkiVLMHXqVDzxxBORbsqgefnll/Hee+/1ucYUEQ0d9iwR0Yjyu9/9DvHx8ZFuxqBSKBR48cUXI90MolGLPUtEREREfWDPEhEREVEfGJaIiIiI+sCwRERERNQHhiUiIiKiPjAsEREREfWBYYmIiIioDwxLRERERH1gWCIiIiLqA8MSERERUR/+Pwi5XqUZGTtlAAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(bee_coords[0], bee_coords[1])\n", "plt.xlabel('x (meters)')\n", "plt.ylabel('y (meters)');" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-e2889206cd341445", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "Let's now calculate how far the bee is in any moment in time from a particular location. Remember the distance function we made in lecture:\n", "\n", "```python\n", "def compute_dist(a,b):\n", " \"\"\"compute distance between two points, `a` and `b`\n", " \n", " `a` and `b` are each 2D sequences representing the coordinates of the point.\"\"\"\n", " dx = b[0] - a[0]\n", " dy = b[1] - a[1]\n", " return np.sqrt( dx**2 + dy**2 )\n", "```\n", "\n", "We wrote this function with the idea that `a` and `b` are each a 2D point. With `a = (xa, ya)` and `b = (xb, yb)`.\n", "\n", "Now we want a a function which returns the distance between a single input point `a` with coordinates `(xa, ya)` and each and every point in an array `bee_coords` which has shape `(2, n)`. So column `i` of `bee_coords` is thus a coordinate `(xi, yi)`.\n", "\n", "Let's build up to this function piece-by-piece.\n", "\n", "## Q8\n", "\n", "First write a function called `compute_dx_squared` with the signature:\n", "\n", "```python\n", "def compute_dx_squared(a,b):\n", " \"\"\"return the x distance squared between `a` and all points in `b`.\n", " \n", " `a` is a length 2 sequence with coordinates `(xa, ya)` and `b` is a numpy array of shape `(2, n)`.\n", " \"\"\"\n", "```\n", "\n", "This function returns the squared `x` distance between point `a` (a length 2 sequence of the `x`,`y` coordinates of point `a`) and each coordinate in the 2D array `b` with shape `(2,n)`. Thus, for `a=[1.0,2.1]` and `b=np.array([[1,2,3],[4,5,6]])`, this will return `np.array([0., 1., 4.])`." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-68e7d25afb2f1765", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "def compute_dx_squared(a, b):\n", " dx = b[0] - a[0]\n", " return dx**2" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-917fe5138adc3d85", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(compute_dx_squared([1.0,2.1],np.array([[1,2,3],[4,5,6]])))=='1bacf727ce'\n", "assert ads_hash(compute_dx_squared([1.0,2.1],np.array([[1,2,3,4.2],[4,5,6,8.2]])))=='c9c0596224'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-18f7f59d96101f0c", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Q9\n", "\n", "Now write a function called `compute_dy_squared` with the signature:\n", "\n", "```python\n", "def compute_dy_squared(a,b):\n", " \"\"\"return the y distance squared between `a` and all points in `b`.\n", " \n", " `a` is a length 2 sequence with coordinates `(xa, ya)` and `b` is a numpy array of shape `(2, n)`.\n", " \"\"\"\n", "```\n", "\n", "This function returns the squared `y` distance between point `a` (a length 2 sequence of the `x`,`y` coordinates of point `a`) and each coordinate in the 2D array `b` with shape `(2,n)`. Thus, for `a=[1.0,2.1]` and `b=np.array([[1,2,3],[4,5,6]])`, this will return `array([ 3.61, 8.41, 15.21])`." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-e74d9319eda293c6", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "def compute_dy_squared(a, b):\n", " dy = b[1] - a[1]\n", " return dy**2" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-52b68c1bd8f83a48", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(compute_dy_squared([1.0, 2.1], np.array([[1,2,3],[4,5,6]])))=='759d96c1e4'\n", "assert ads_hash(compute_dy_squared([1.0, 2.1], np.array([[1,2,3,1.2],[4,5,6,-3]])))=='cb574f3c8e'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-3360812bc0c687c5", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Q10\n", "\n", "Now write a function called `compute_distance` with the signature:\n", "\n", "```python\n", "def compute_distance(a,b):\n", " \"\"\"return the distance between `a` and all points in `b`.\n", " \n", " `a` is a length 2 sequence with coordinates `(xa, ya)` and `b` is a numpy array of shape `(2, n)`.\n", " \"\"\"\n", "```\n", "\n", "This function returns the distance between point `a` (a length 2 sequence of the `x`,`y` coordinates of point `a`) and each coordinate in the 2D array `b` with shape `(2,n)`. Thus, for `a=[1.0,2.1]` and `b=np.array([[1,2,3],[4,5,6]])`, this will return `array([1.9 , 3.06757233, 4.3829214 ])`." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-cbbcbdf110a030b2", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "def compute_distance(a, b):\n", " dx = b[0] - a[0]\n", " dy = b[1] - a[1]\n", " return np.sqrt(dx**2 + dy**2)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-eb44de204d241fa6", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(compute_distance([1.0, 2.1], np.array([[1,2,3],[4,5,6]])))=='1e2fc6c5fd'\n", "assert ads_hash(compute_distance([1.0, 2.1], np.array([[1,2,3,10.1],[4,5,6,-2.2]])))=='b7ef5a80ae'" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-b084804c3c32a977", "locked": true, "schema_version": 3, "solution": false, "task": false } }, "source": [ "## Q11\n", "\n", "Now let's imagine you upgraded your tracking system to work in 3 dimensions and now you collect data in `x`, `y` and `z`. Write a function called `compute_distance_3d` with the signature:\n", "\n", "```python\n", "def compute_distance_3d(a,b):\n", " \"\"\"return the distance between `a` and all points in `b`.\n", " \n", " `a` is a length 3 sequence with coordinates `(xa, ya, za)` and `b` is a numpy array of shape `(3, n)`.\n", " \"\"\"\n", "```\n", "\n", "This function returns the distance between point `a` (a length 3 sequence of the `x`,`y`,`z` coordinates of point `a`) and each coordinate in the 2D array `b` with shape `(3,n)`. Thus, for `a=[1.0,2.1,0.0]` and `b=np.array([[1,2,3],[4,5,6],[7,8,9]])`, this will return `np.array([ 7.25327512, 8.56796359, 10.01049449])`.\n", "\n", "Remember that the formula for distance in 3D is:\n", "\n", "```\n", "distance = sqrt( dx**2 + dy**2 + dz**2 )\n", "```\n", "\n", "where `dx` is the distance along the `x` direction, `dy` is the distance along the `y` direction, and `dz` is the distance along the `z` direction, " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "nbgrader": { "grade": false, "grade_id": "cell-d3cd40a661182621", "locked": false, "schema_version": 3, "solution": true, "task": false } }, "outputs": [], "source": [ "def compute_distance_3d(a, b):\n", " dx = b[0] - a[0]\n", " dy = b[1] - a[1]\n", " dz = b[2] - a[2]\n", " return np.sqrt(dx**2 + dy**2 + dz**2)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "nbgrader": { "grade": true, "grade_id": "cell-f0538c2e9972a4ba", "locked": true, "points": 1, "schema_version": 3, "solution": false, "task": false } }, "outputs": [], "source": [ "# If this runs without error, it means the answer in your previous cell was correct.\n", "assert ads_hash(compute_distance_3d([1.0,2.1,0.0], np.array([[1,2,3],[4,5,6],[7,8,9]])))=='6f84b17de0'\n", "assert ads_hash(compute_distance_3d([1.0,2.1,0.1], np.array([[1,2,3,0.0],[4,5,6,1.0],[7,8,9.2,0.0]])))=='0b9c9c17b1'" ] } ], "metadata": { "celltoolbar": "Create Assignment", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 4 }