ANN-route-predition/experiments/Experiments.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# A suggestion of curse changes for a robot sailboat\n",
"\n",
"## Motivation\n",
"\n",
"The goal of this project is to suggest good points to change the curse of a sailboat while going from point $A$ to point $B$.\n",
"\n",
"This project is done as part of the curse \"Maschienen Learning\" at the University of Applied Sciences South Westphalia. The code labeling the was writen by the team of the [Sailing Team Darmstadt e.V.](https://www.st-darmstadt.de/). A society of stundens whose goal it is to build the [\"roBOOTer\"](https://www.st-darmstadt.de/ueber-uns/boote/prototyp-ii/) a fully autonomous sailboat able to cross the atlantic ocean. A technical challenge that was mastered the first time only a few years ago by [a Norwegian team](http://sailbuoy.no/). I myself am part of the Sailing Team Darmstadt e.V. for nearly 10 years.\n",
"\n",
"One of the challenges to solve is a highly efficient way to find a path over the Ocean. The boot is only 2 meters long and powered by solar energy. That makes power a relatively spares commodity.\n",
"\n",
"## Situation as is\n",
"At the moment the pathfinding algorithm generates a set of more or less random routes to the goal. Each route than gets optimized by a gradient decent moving the curse change points over the ocean to find a path with the lowest cost that can be found by following the highest gradient. This is relatively inefficient since only local minima can be found for each of the randomly generated route. The route with the lowest cost for the so optimized route will be chosen as the final route.\n",
"The idea of this project is to ascertain if it is possible to generate a better initial route through a neural network to give the system a kind of good instinct for the initial routes to reduce optimization steps and the number fo routes that need to be calculated to find a good route. Even tough the initial calculation effort could be high the parallel calculation of 40 routes and lots of optimization steps make it possible that some calculation time and therefore energy can be saved this way.\n",
"The idea of this project is to ascertain if it is possible to generate a better initial route through a neural network to give the system a kind of good instinct for the initial routes to reduce optimization steps and the number fo routes that need to be calculated to find a good route. Even tough the initial calculation effort could be high the parallel calculation of 40 routes and lots of optimization steps make it possible that some calculation time and energy can be saved this way.\n",
"\n",
"## The Project\n",
"\n",
"The goal of this project is to calculate a good first route. That allows for some simplifications of this problem.\n",
"\n",
"Some solutions and assumptions can be made.\n",
"1. The route proposed by this network will not be the final route. This make a somewhat accurate solution good enough.\n",
"2. Since the neural network should not learn how to interpret a specific map but the concept of a map the map can be rotated.\n",
"This allows the wind to come always from north.\n",
"3. Since curse speed is only somewhat proportional to the wind speed a final course may change depending on wind speed not only direction.\n",
"These changes are however somewhat small compared to other influences and can hopefully be ignored since later processing of a proposed route should strait these details out.\n",
"4. When the wind comes always from the same direction (After map orientation by wind) map and route can be mirrored allowing to use all data twice for each route.\n",
"5. Scale does only matter when the curvature of the earth has a significant influence. Allowing for different scaling of the problem for additional training data.\n",
"\n",
"Since there is a solution for this project that only needs some optimisation we can used labeled data to train the network.\n",
"\n",
"### The generell structure\n",
"\n",
"Since"
],
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"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%%\n",
"is_executing": true
}
},
"outputs": [],
"source": [
"from typing import Optional\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from PIL import ImageDraw, Image\n",
"from shapely.geometry import Polygon, Point\n",
"from shapely.ops import unary_union"
]
},
{
"cell_type": "code",
"execution_count": 76,
"outputs": [],
"source": [
"SIZE_INNER = 75\n",
"SIZE_ROUTE = 100\n",
"MIN_DESTINATION_DISTANCE = 25"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 77,
"outputs": [],
"source": [
"# https://stackoverflow.com/questions/16444719/python-numpy-complex-numbers-is-there-a-function-for-polar-to-rectangular-co\n",
"def polar_to_cartesian(\n",
" radii: np.ndarray,\n",
" angles: np.ndarray,\n",
"):\n",
" \"\"\"Transforms polar coordinates into cartesian coordinates.\n",
"\n",
" Args:\n",
" radii: A array of radii.\n",
" angles: A array of angles.\n",
"\n",
" Returns:\n",
" An array of cartesian coordinates.\n",
" \"\"\"\n",
" return radii * np.exp(2j * angles * np.pi)\n",
"\n",
"\n",
"def cartesian_to_polar(\n",
" x: np.ndarray,\n",
"):\n",
" \"\"\"Transforms cartesian coordinates into polar coordinates.\n",
"\n",
" Args:\n",
" x: A set of complex number to be separated into polar coordinates.\n",
"\n",
" Returns:\n",
" An distance array and an angle array.\n",
" \"\"\"\n",
" return abs(x), np.angle(x)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 78,
"outputs": [
{
"data": {
"text/plain": "(array('d', [58.14925495607776, 57.991505776535874, 57.76398376244, 56.384785221067744, 55.81452229141463, 56.285256940049436, 56.785512495130604, 58.14925495607776]),\n array('d', [-62.57469140842694, -62.279867618259566, -62.04187518408968, -61.94019571244569, -62.6896502705296, -63.99539776490992, -64.20651127735718, -62.57469140842694]))"
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def random_polygon(\n",
" radius_mean: float = 2,\n",
" radius_sigma: float = 1.5,\n",
"):\n",
" \"\"\"Generates the simplest of polygons, a triangle with a size described by a random polygon.\n",
"\n",
" Args:\n",
" radius_mean: The average radius defining a circumcircle of a triangle.\n",
" radius_sigma: The variance of a radius defining a circumcircle of a triangle.\n",
"\n",
" Returns:\n",
" A single triangle.\n",
" \"\"\"\n",
" number_of_corners = np.random.randint(3, 10)\n",
" array = polar_to_cartesian(\n",
" np.random.lognormal(radius_mean, radius_sigma),\n",
" np.sort(np.random.rand(number_of_corners)),\n",
" )\n",
" offset = np.random.randint(low=-SIZE_ROUTE, high=SIZE_ROUTE, size=(2,))\n",
" return_values = np.zeros((number_of_corners, 2), dtype=float)\n",
" # return_values[1, :] = np.real(offset)\n",
" return_values[:] = offset\n",
" return_values[:, :] += np.array((np.real(array), np.imag(array))).T\n",
" return Polygon(return_values)\n",
" # return np.array( + offset[0], np.imag(array) + offset[1])\n",
"\n",
"\n",
"random_polygon().exterior.xy"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 79,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"POLYGON ((64.24234834022612 114.96629329424587, 62.58155390362512 115.71472980681563, -73.35243626054171 120.99282453832782, -193.33402301375247 -80.4360840218972, 64.24234834022612 114.96629329424587))\n"
]
}
],
"source": [
"print(random_polygon())"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 80,
"outputs": [],
"source": [
"def generate_obstacles(\n",
" seed=None,\n",
" number_of_polygons: int = 40,\n",
" radius_mean: float = 2,\n",
" radius_sigma: float = 1,\n",
"):\n",
" \"\"\"Generates a set of obstacles from a union of triangles.\n",
"\n",
" The union of triangles meas that if polygons overlap o polygon containing the union of those polygons is returned.\n",
" Args:\n",
" seed: A seed to generate a set of obstacles from.\n",
" number_of_polygons: The number of polygons that should be drawn.\n",
" radius_mean: The average radius defining a circumcircle of an obstacle triangle.\n",
" radius_sigma: The variance of a radius defining a circumcircle of an obstacle triangle.\n",
"\n",
" Returns:\n",
" A list of unified obstacles.\n",
" \"\"\"\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
" polygons = []\n",
" for _ in range(number_of_polygons):\n",
" poly = random_polygon(radius_mean, radius_sigma)\n",
" if poly.contains(Point(0, 0)):\n",
" continue\n",
" polygons.append(poly)\n",
" polygon_list = list(unary_union(polygons).geoms)\n",
" return polygon_list"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 81,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"POINT (-61 31)\n"
]
}
],
"source": [
"def generate_destination(obstacles: list[Polygon], seed: Optional[int] = None) -> Point:\n",
" \"\"\"Generates for a map.\n",
"\n",
" Can be used to generate a valid destination for list of obstacles.\n",
" Args:\n",
" obstacles: A list of obstacles.\n",
" seed: The seed determining the point.\n",
"\n",
" Returns:\n",
" A goal that should be reached by the ship.\n",
" \"\"\"\n",
" # sets the seed\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
"\n",
" # generates the point\n",
" point: Optional[Point] = None\n",
" while (\n",
" point is None\n",
" or abs(point.x) < MIN_DESTINATION_DISTANCE\n",
" or abs(point.y) < MIN_DESTINATION_DISTANCE\n",
" or any(obstacle.contains(point) for obstacle in obstacles)\n",
" ):\n",
" point = Point(np.random.randint(-SIZE_INNER, SIZE_INNER, size=(2,), dtype=int))\n",
" return point\n",
"\n",
"\n",
"print(generate_destination(generate_obstacles(42), 42))"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 82,
"outputs": [
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": 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fTIrW4Betw9+5U+bwZ8yA//1PNtKJi3Omol91+f2yX8DNN3vdEkekpqaSUUIS0u4K1u/OzMhky5Yth90fHR1N/fr1q90+FSKWL5cLdre2Y927V45d2ZU6+/bJlKHC2BCoxJVijF3gdSNCmc8HffrAI4/IEH4o2LxZtpOcPl2W36SlyZs20Mtf/H7JcXj22dCaTihDy6PbsnXrNqJjDr++T0o+nYRjh5b62KyNf7Dv04cwHP65kblvD0uXLKFt27aOtlcFoYIC6NULfvnFvZU8NWrA3LnQqVPlHvfDD7IRUhgUuzGw0FqbUtXHaw8+EmRmyrz8ccfJMPnDD0tSXjArWoM/bJj8f80aCfjTpsE330gSTUGBe70HkOA+eLDsihUmwR2gQ4eO7Dv6NGokV343uIRmHUm45rXD7s/btwP75o20aNHCgRaqoPf887LXu5vLdI2RpXKVDfBOZvOHOB3HiBTWSjD8/HPo3h0uuEDK04aKVq3gyislUW/3bpmCeOghOPVUWZOelOTsGvyEBBn1eOedsBvuu/Ly4UStqeZuXYfIWD6Hs84+m/hIqYMQyTZtkmRZt0fTMjKqtlRu377I3COiBOH1yaXKV1Aga0SnToXOnWH4cFi3zutWVY4xUmDm+utlvj41Fb77zrk1+H6/5C588gmUMIwd6s444wzSN60gP925PbPN6rlccdlwx46ngpS1MHKkO3tjHCovD377rfKP27cvcnd6PIQG+EiVny+BfvJkCZZXXSWbzYQiJ9fg+3xST/u77+RCIQz5/X4GnXEGGcvnOHK83D1byN29mYEDBzpyPBXEPvhA5sUDtc68KkvlUlN1HXwhDfCRLi9PAv2bb8pyteuvl7rzoawqa/BjYqB2bRnZeOihsOy5F3fFZcMxq+c6cqys5d9z/vnnE6tLk8Lb7t0wenRgy8CuXVv5x6SmOlMPPwxogFciN1cC/UsvSZ37W26RHZ3CQUlr8N99F264QUr9RkVJRvDSpVJXPwKceuqpZG1fT17qjmofy/45lxGXDnOgVSqojRnjblJrSdLTK78z3J49OgdfSAO8OlhOjryJn35adq67446wWG5ykBo14PTTZWe8lSvlA2H2bGjUyOuWBUx8fDxnnX02GctnV+s4uTs3YjN2c+KJJzrUMhWUvv5aRsECMfdenM9X+WTgnTvdaUsIcjXAG2PaGWMWFbulGmNuMsbca4zZVOz+M9xsh6qC7GwZinv0UWjWDO6/P3z3WK5RI+wy5SvCiWH6rOWzGXLxEKJDfY8BVbqMDEnG9WqHtsruKlfBgk2RwNVPNWvtcmttsrU2GegBZABTCr89seh71toZbrZDVUNWlgT2//4XmjaV+elAD9MpVwwcOJC8vdvI3XN4VbqKsNZS8OdcLr/0EodbpoLK7bd7N4pXlaVy4TbiWA2B7LacBPxprQ2xNVkKkKCemgr33gtNmsCTTwZ+uE45KiYmhvPPP5/MZVUbps/dsY7Yghx69+7tcMtU0Pj5Z3jhBe8u6vPz4ddfK/cYLXSzXyAD/FBgUrH/jzHG/GaMecUYU+fQHzbGjDbGLDDGLNgeuDaq8mRkyJz1v/4lQ/cvvCAJeiokjbxsOFRxmD572WwuuWQIURE4vRER3NwprjKWLKnczwfD/hVBIiDvTGNMHHA28H7hXc8CrYFkYDPwyKGPsda+YK1Nsdam6PYVQSg9HXbsgLFjoXlzeOMNLS4Rgo4//njI3Evuzg2Vepy1lrxVc7h8uGbPh60JE6RqndfWr6/czwd6v4ogFqhL79OBn621WwGstVuttfnW2gLgRaBXgNqhnJaeDlu2SHGYVq3gvffcrU+tHBUdHc2QIUPIWl65rYVztqwiMT6G7t27u9Qy5amVK+E///Eusa64rKzKDbsHQ5uDRKAC/CUUG543xjQu9r3zgN8D1A7llvR02LBBKuK1aSMFY3Qtaki4fPgl5K+aQ2V2lsxd+T2XDb8EE0ab8KhC1krWfLDk2Ph8lcuk93pKIYi4HuCNMYnAKcBHxe5+0Biz2BjzGzAACI+NtpUE+tWr5QOiQwcpGauBPqj17t2bOPLI3b6mQj9vbQHZK+ZwmQ7Ph6cXX5R572AZibO24pn0BQVaxa4Y1wO8tTbdWnuEtXZvsfsus9Z2ttZ2sdaeba3d7HY7VIClp8t2khdeKHXiv/nG6xapUhhjuHTYULIrOEyfvWkZR9SuSafKbuOpgt/mzVLFMpjmsdPTYfnyiv9smJeZrgxNf1XuSk+XZS6DB8Oxx8pGFSroXDrsEvJWVmyYPm/l94y4THvvYWnatOBaFePzyd4RiYkV+/nUVA3wxWiAV4GRkQE//SS13k88ERYu9LpFqphu3bqR5I8nZ0vZc522IJ+sFXMZfokWtwlLo0bJrow+n3dt8PshPh6Sk6XA1ooVslFURezbpwG+GA3wKrAyMuD77+GEE+DUU2HxYq9bpJBh+suHDSVnRdnD9Nkb/qBJk8a0bds2QC1TARUdDQ88AF98AfXqyVbLgZCUJEH9uOOkPPa6dfDLL3DjjbIMt6L27Tt4l8gIpwFeBZ61kun65ZcybH/WWTJfrzx16fBh5KyYg6xeLVnequ+5QneOC3/HHy/z3gMHSo/aDTVqSFA/6SR45hmZ/587F665Bho2rNoxU1M1wBejAV55pyjQz5ghiXjjxnndoojWsWNH6h1Rh+xNS0v8vs3PI2P5XC65ZGiAW6Y8UbeuvDcfesi5IF+zpmzffOaZ8MorUizryy/hssugzmEFTStv3z5dtVOMBnjlvYICeVOG6251IWTkpcPIWzGnxO9lrfuVo1q3pmXLloFtlPKOMVLE6qefpJBVQkLlH1+jhiTJDRkC77wju7198omssElKcra9+/YFz/K+IKABXgUHY+BmLYfgtWGXDCVzxRxsweFlh/NXfc+Vlw/3oFXKcx07wh9/wCWXlN+bj4qSoF6rFowcCVOmyP4V774rq2kqe5FQGampkJfn3vFDjKYbKu/FxcHFF0ODBl63JOK1adOGZs2asmf9Ynwtk/ffb/NySV/5I0MuftW7xilv+XwyrH7mmRK4MzIO7D8REyPz6X6/9NQvuQR695ZgH0j79gXXMj+PaQ9eeSshAbp2le1nVVC48rLh5K86eJg+c81COnToRNOmTT1qlQoa558vvfnkZFmj3qyZbDo1ezZs3Srv5T59Ah/cQUYKdNOr/TTAK+/4fJKt+913knyjgsLQoUPIWDEPm39gqNP+OZerRujwvCp05JHw44+wapXsQTFhgiTKep3BvmuXt+cPMhrglTf8flke99ln7s7JqUpr0aIFrY9uQ9baRQAU5GSxb+VPXHjhhd42TAWX6OjKrVEPBA3wB9EArwLP74cRI2DSJK06FaSuGjGc/D9lmD7zz/l0T+lJA82RUMFuzx6vWxBUNMCrwPL7pRTm0097M0enKuTiiy4ifcWP2LwcWD2XK7X2vAoFldk3PgJo90kFjs8ntaX//nevW6LK0aRJEzp27sLaZd+Ttvpnzj//Y6+bpFT59u3zugVBRbtQKjB8PtlnWoN7yLjq8mHsnfUSffqeQN26db1ujlLl02JZB9EAr9zn98PkyTBcs7BDyYUXXkhBdroWt1GhIyPD6xYEFR2iV+5KSpJ61iec4HVLVCXVr1+f3379ldatW3vdFKUqRgP8QTTAK3cYI6Uqv/5a1seqkNSxY0evm6BUxVgLOTletyKoaIBXzouOhiOOkH3f27TxujVKqUiQmSkrc3Szmf10Dl45KzYWmjaFhQs1uCulAic1VetqHEIDvHJOfLwE9YULpT61UkoFyr59GuAPoQFeOcPnk80nfvgB6tXzujVKqUizb58WzzqE/jVU9fn9cOKJ8M03sg+0UkoFms8n8/D6GbSfjmeo6vH74eyz4c03dXhMqYoqKIDnn5clpPXqQcOG8m+dOoff6taV5aZe79QW7I45BjZtkpHE4hXtatSQLWQLCuRfn0/uz86WWxjTT2RVdX4/XHklPPGEfvgoVVF//gmXXCJ7qhdftx0bK7eYGHk/WQt5eZCbK4HJ75dAX7MmHHccvPyyrFhRBxQUwObNEsQLCqBfP7jhBhg0SP6ue/bAunWwdq38u3IlLF8Oa9bAli2QlSW7W0ZHy5K7zEyvf6Nq0QCvqsbvh//7P7j7bg3uSlXUypXQvbsE9kOXc+Xmyq00aWly27IFNm6UHv/DD7vb3lCTnw99+sCFF8Kllx6eD1S7tty6di358enpsH79gYuAP/+EZctg9Wr46y8ZGUhIkIuFvDx5Hq11+ZeqOmODuHFFUoyxC7xuhDrA54OHHoLrr/e6Jao6CgpgyRLo1MnrlkSGzEzo0kWChhOfuz6fTI1dcEH1j6UqJicHNmw4cAGwZg0sXSrP6caNsHu3rCaKjZWLjZIu5CrBwEJrbUpVH689eFU5Ph+89BIM0+1DQ96HH8LFF0uAv/deOP98HY1x0+jRMkfsVKcqMxNGjIAOHWT+WbkvLg5at5ZbSfLzpadfdAGwdq2MAKxaJRcG27ZJ7z8uTl4HmZkyEuCS0AjwUVFyRRTmCRFBz++H99+HM87wuiXKCS1aSALS77/D5ZfLB9Edd3jdqvD0+uvw0UfOz+lmZMCpp8p8fs2azh5bVV50NBx5pNyOP/7w71srQX7dOrmtWQMrVsht/XqZfrFWpgGg2vvbh8YQfXKyXdCtG7z3XsgnPYSspCT4/HPo29frliin7N0L9esfmPdNSJChxiZNvG1XuFmyBHr2dG8jlPh4Wab6+ee6DjzUWXsgEXDdOsy551ZriD40Xg0xMfDqqzBnjiRHJCZ63aLIYYwkpXz/vQb3cFOr1oElQyCBYs0a79oTru6+291dzrKz5bPx6afdO4cKDGNkaWRyMpxzTrUPFxoBvki3bvDLL/DMMxJ0ioYxlDuio6FBA5g/v/SsUxXaWrU68HVBgWwSpJyVlub+OayFtm3dP48KKaEV4EGucC6/XIYwrr5agrwOSzkvNlbmkRYuhKOP9ro1yi2dOx/4OidHywy7we3dzfx+uP9+OO00d8+jQk7oRsaaNaXAyi+/SNEHHbZ3Tnw8tGsnwb1pU69bo9yUnCwXcz6fvI+0B+88F7Ok8fngootg7Fj3zqFCVugG+CLt28Ps2bIetEEDuZpVVefzSSGOefOkRKYKb+3bS5Ld6afDF1/oMjk35Oe7c9y4OJk6e/FFfd5UiUI/wIO8uM87T9Ycjh0rQUrrolee3w/9+8OsWZI1r8LfwIHw8cey/DE21uvWhCc3huiNkQ7NjBn6vKlShUeAL+LzyVzU0qVw8snam68Mvx/OPRc++USG6FVk8PkkW1fzWNzjxhB9UhJ8/bVkXCtVivB8V7doAZ99BlOnQvPmOj9fHr8fRo2Ct97SzSuUcprTQ/Q+H0ybRs5RLTj9rdM5/a3TSctJ2/91Tn6Os+dTISs8A3yRk0+WEoH33CNBTIeyDuf3w7/+BY8/rvN4SrnBySF6vx8eewz69+ecSefw7bpv+XbdtzR7tNn+r8+ZVP310yo8hHeABwnqt94qFbrOO+/gwh6RzueT3ajuvNPrligVvpzqwfv9cNVVUtO+mMy8TPZm7yUzT6t8qoOFf4Av0qiRlLr96itZAhbpw/Y+H7zyCvztb163RKnwFhdX/WPEx8s2qBMn7r/r/YvfJy764GPHRcfxwcUfVP98KixEToAvctxxsjHDI4/IWvpITCjz+2Xji6FDvW6JUuHvppuqN3IYHQ3NmsGUKQflyFw0+aLD5ttz8nO4cPKFVT+XCiuRF+BB3iTXXCPL6i67TN58kTL/XKMGfPklDBrkdUuUigyXXCLLEGvWrNry3Ro1JGO+lKWrvhgfteJr4YvR6Ud1sMgM8EXq1JEiET/8IHXuw3nYvmgTgzlzZBRDKRU4gwfLdrwpKZVbvuvzyS5xzZsf9q2pl0ylX4t+9GvRj41jN+7/euolUx1suAplobFdbEqKXbBggbsnsRbefRfGjJEtacNpW9qYGKkxPmcOHHWU161RKnIVFMC4cfDf/5b/GePzwcsvywiAikjGmODeLtYYs9YYs9gYs8gYs6DwvrrGmJnGmJWF/3pfrcEYeSOtWyeJZz5feBT/iIuTTWN+/lmDu1Jei4qCu+6SIfcGDUrPAfL74eabNbiraglUBBtgrU0udiVyG/CVtbYN8FXh/4NDUpIk4P36K5xwQmgP2yckSK3xhQuhcWOvW6OUKtK7NyxfDqeccvhnjM8n9z/wgDdtU2HDqy7qOcDrhV+/DpzrUTtK16YNfPMNTJokS+xCreytzyfzfXPnajlLpYJR7dowbZosffP7ZRQxJgZat5bPnUhJ/FWuCUSAt8AXxpiFxpiiCg0NrbWbC7/eAjQMQDuq5qyzYM0a+Oc/Q2cTG79fNhH56qvQHoFQKtwZA1dfDQsWyBRa7dowc6YW5FKOCESAP95a2x04HbjeGHNi8W9ayfI7LNPPGDPaGLPAGLNg+/btAWhmGRIS4O67ZUht0KDg7s37/XDBBVKH34kCG0op9x1zDCxZAhs2yIihUg5wPcBbazcV/rsNmAL0ArYaYxoDFP67rYTHvWCtTbHWptSvX9/tZlbMkUfKbmvTp0OrVsHXO/b74dpr4fXXddMYpUJNXJx0JpRyiKsB3hiTaIypUfQ1cCrwOzANGFH4YyOA0Fq4OWAArFghW9MmJgZHT9nvhzvukARBnbtTSqmI53YPviHwvTHmV+An4FNr7efAeOAUY8xK4OTC/4eWmBhZxrJ6NVx4obdzZj4fPPoo3H67d21QSikVVFzNGLPWrga6lnD/TuAkN88dMA0awNtvw/z5MHKkrKNPTw/c+X0+GZK/6KLAnVMppVTQC4NKLkGiZ09YvFj2Va9VKzBzaYmJkkynwV0ppdQhNMA7KSpK9mteuxauuMLdTWxq1JBlcKec4s7xlVJKhTQN8G6oXRueeQZ++kmKzTiZbR8VBXXrwrx5cOyxzh1XKaVUWNEA76ZOneDHH+GVV2Szl+om4sXEQMOGUhSjY0dn2qiUUiosaYB3mzFw8cUybH/DDRLkq7JGPS4OWrSQTWNatXK8mUoppcKLBvhASUyECRPg99+hf//KVcNLSJBKVwsWaJUrpZRSFaIBPtCOOgq+/BI++ACaNCk/0Pt8kqE/Z47M7SullFIVoAHeK6efLkVybr9dgnxs7OE/4/dLlvyXXwZfWVyllFJBTQO8l+LjpbzsihUwePDBvXm/X+buP/ooOErhKqWUCika4INB06YwZQp8/rnsBR0TA9ddJ9n3ummMUkqpKgiBzc0jyAknwLJlsHQpdO7sdWuUUkqFMA3wwSYmRoN7qLr+enjrLfna2oP/Lfq6+P/r1YPly3WLUKWUKzTAK+WEb7+F116DjIyKP2b7dsnDUEopF+gcvFJOePTRygV3kNoGbu1VoJSKeBrglXLCbbdVvhTx8ce70xallEIDvFLOOO44WdZY0SH3GjWgd29326SUimga4JVyysSJFU+Ysxa6d3e3PUqpiKYBXimn1KkDTz9dsaqDubnQpo37bVJKRSwN8Eo5adgw6NIFosp5a7VtW/7PKKVUNegnjFJOMgbeeKP8ufg+fQLTHqVUxNIAr5TTjj4a/vGP0ncKTErSAK+Ucp0GeKXccMcdULduyd8zRhPslFKu0wCvlBvi4+HNN0teG5+VBe3bB75NSqmIogFeKbf07w9nnSVBPjZWdg0891ypVx+jVaKVUu7STxml3PTqq7BoEXTqBDVret0apVQE0QCvlJv8/pIT6n78EX75BTIzD9z27ZNby5ayM11pc/hKKVUBGuCVCrSdO2HgQKlml58PeXlQUHDg+wkJMH48jB4N//wnNGrkXVuVUiFL5+CVCrQJEySwZ2ZCTs7BwR0kCS8jA555Blq1glGjYN06b9qqlApZGuCVCqRdu6ScbXZ2+T+bkyPB/o03JOt+yBBYvtz9NiqlwoIGeKUC6cEHD++xlyc3VwL9hx9CcjIMHizz90opVQYN8EoFyu7d8OSTEqyrIj9fHvvZZ9C3L/TrB3PmONtGpVTY0ACvVKA8/HDle+8lsVbm77/7Dk49FXr0gMWLq39cpVRY0QCvVCDs2QOPP1713ntpMjLg119lrb1SShWjy+SUCoRHHpEhdifFxcnGNe+/L8vulFKqGO3BK+W2vXth4kRne+9+P5x8MqxcqcFdKVUiDfBKuW3iRGfm3gGioiAxUZL1pk/XandKqVLpEL1SbkpNleH5zMzqH8vvh9atYcoU+VcppcqgPXil3PTYY87Mvft8cOON8PPPGtyVUhWiPXil3LJvHzz0UPV67/HxUKcOfPQRHHecc21TSoU97cEr5ZbHH69e793vl/3jV6zQ4K6UqjTtwSvlhrQ0KUtbld57dLQMyb/8Mlx8sfNtU0pFBA3wSrnhiSeq1nv3+6FTJ6k736yZ8+1SSkUMHaJXymnp6bIlbEZG5R7n88Gdd8K8eRrclVLVpj14pZz25JOQl1fxn/f5oH59+Phj6NbNtWYppYJcfr4k59au7cjhtAevlJMyMmD8+Ir33v1+GDYMli7V4K5UpLvkEmjQQEYAK9NJKIVrAd4Yc6QxZpYxZokx5g9jzI2F999rjNlkjFlUeDvDrTYoFXBPPy37t5cnNhZq1YL33oOXXpJAr5SKXFOmwKefyufHffdBly7VPqSx1jrQshIObExjoLG19mdjTA1gIXAucDGQZq19uKLHSklJsQsWLHClnUo5JiMDmjSR2vNl8fuhVy8J7g0aBKZtSqngtX07tGlz2GeHgYXW2pSqHta1Hry1drO19ufCr/cBS4Gmbp1PKc89+2zZvXdjZL59wgT4+msN7kopsBYuv7zySbkVEJA5eGNMS6Ab8GPhXWOMMb8ZY14xxtQJRBuUclVmJjzwQOlvUr9frtAXLoQxYyTYK6XUW2/Bd99VbGqvklwP8MaYJOBD4CZrbSrwLNAaSAY2A4+U8rjRxpgFxpgF27dvd7uZSlXPc89BTk7J3/P54OqrYfFiOOaYwLZLKRW8Nm2C665zpfcOLs7BAxhjYoHpwP+stY+W8P2WwHRrbaeyjqNz8CqoZWVB48awZ8/B98fFQY0a8P77MGCAJ01TSgUpa6FfP6l7UUrGfNDOwRtjDPAysLR4cC9MvityHvC7W21QKiCef/7w4TW/H049FVau1OCulDrcc8/J7pAOLIcrjZuFbvoClwGLjTGLCu+7HbjEGJMMWGAtcI2LbVDKXdnZsqQlPV3+HxUlQ/JPPQUjRuhcu1LqcKtXwz/+4drQfBHXAry19nugpE+3GW6dU6mAe/FFCfJwIJFuyhRo1crbdimlglNBAVx0kUztuUwr2SlVVdnZcO+90nv3+eCWW2DBAm+Cu7UHLjSUUsHr4Ydh+XIJ9C7TAK9UVb38MuzcKQl2X38tQ/UxHm3vMHSo1K8eM0aG/5RSwWfp0gOdggDQAK9UVc2aJbWjly+H3r29a8cPP8Ann8iQ3wsvQMeOMHAgzJwpPXullPdyc+GCCwIyNF9Ed5NTqqree0+S6rxUUABXXSWFdkA+RHJz5eJj/nyoUwf++U9J+EtK8ratSkWy+++HdesCetEd2T34ffvkQ/DNN+H//g+GDJEhV6UqwuvgDvDGG/KhUZK0NNiwQV7bDRtKQY1VqwLbPqUU/PKLzL27nDV/KFcL3TilWoVurIUtW2DZMpn/+PVXWXv4558S4P1+6QWlp0NCArz6qgR6pYJdWho0bw67d1fs52NjIToajj0Wbr8dTj45OC5SlApn2dlSwXLNmko/tLqFbsJniD4vT/6AS5fKbeFC+P13WLtWgnxcnAxdFg1lFklNPfB1ZibMmaMBXoWGf/+7cvN5RcP3334r749ataR3f8UVUnFPKeW8226TTqYHQq8Hn54OK1ZIEP/9d/mgWroUNm+G+HjpoWRkVL06UNeusGiRY21XyhVr1kgy3aEXrJXl98sF8GWXSeGNNm2caZ9SCubOlZGyKr5Pq9uDD40A36CBXdC6tcwf7tlz4EMpLc35hIX4eLmIiI529rhKOen00yVLPj/fmePFxMitZ08Zvj/1VB2+V6o60tPlgnnz5iofImhr0Ttq+3ZZCrRjh/TMU1Nl/tyNi5PYWJmvVypYzZol20s6FdxB3ldZWTB7tlTZOvJIePzxg6ewlFIVd+ONFc+PcUloBPhAslYy65UKRvn5MGqUu9m4aWnw11/Sk2/UCEaPlmkxpVTF5OZKwnZ11rz7fNVuhgb4Q6WnSy9GqWD0wguwdWtgzpWRIXOHr74quSnHHw8zZgSkxKZSIS02VpZfVzVI+3zwyivVbkZozMEbYwO6G3y7djpMr4LP7t3QooVMT3klKUlut94qBXZq1fKuLUoFu3fekRG3yiTZ+f0wcSKMHo0xJgLm4ANt9WrIyfG6FUod7I47vH9dpqXJkp+77pIa/KNG6cWwUqUZNgxeeqniPXm/X2rVjx7tyOk1wJckIQEWL/a6FUodsGwZvPZa8OwYVzR8/9pr0L079OkD06fr8L1Sh6pokPf7JTHv1lsdO7UG+JLk5WminQouo0cHT3AvLj9fAv28ebLxTtOm8MgjsHev1y1TKngMGya5LHXrSlEpYw7+vt8v+0WMG+foaTXAlyQzU6p9KRUMZsyQ8srB3jsuGr6/+24Zvr/ySilCpZSSCqnbt8O0aRLw/X4J9gkJcM458PTThwf+atIku9K0aCFlbpXyUk4OtG4NGzd63ZLKi46WEtFdusgQZadOXrdIqeCRnQ2ffSZLUG+5pcTiatVNsgufWvRO27RJ5hn9fq9boiLZ4497XiyjyoqG73/8EZ57Dp56yusWqQrIzc1l48aNZAVw3/KI1a4dtGtHwvr1NGvWjNjYWEcPrwG+NH6/bPHXt6/XLVGRavt22VAmPd3rllRfsE8vqP02btxIjRo1aNmyJcbhIWN1OGstO3fuZOPGjbRq1crRY+scfGmys+Gnn7xuhYpk//iHVMQKB06W1VWuysrK4ogjjtDgHiDGGI444ghXRkw0wJcmOxu++cbrVqhI9euv8P773q97d4r24EOKBvfAcuvvrQG+LNqDV16wVgrIhNMcqPbgVSVt3LiRc845hzZt2tC6dWtuvPFGcnJyeO211xgzZkyFj/PYY4+RUcW9G7755hvOPPPMKj02GGiAL8vOnbI9rRd++QVWrvTm3MpbH34oy8tCYIVLhWmAD12NGsnyLadujRqVe0prLeeffz7nnnsuK1euZMWKFaSlpXHHHXdUuvnVCfChTgN8WXw+WBDwBXrimmtkc49AbSyigkNWFlx/fXgk1hWnQ/Shy+nPoAoc7+uvvyYhIYErrrgCgOjoaCZOnMgrr7xCRkYGGzZsoH///rRp04Z///vfAKSnpzN48GC6du1Kp06deO+993jiiSf466+/GDBgAAMGDADgb3/7GykpKXTs2JF77rln/znnz59Pnz596Nq1K7169WLfIXs+pKenc+WVV9KrVy+6devG1KlTnfqLuEaz6MuSkSHD9CefHPhzWws7dsi5f/rJka0DVQh48EEpGBNutAevKuGPP/6gR48eB91Xs2ZNmjdvTl5eHj/99BO///47fr+fnj17MnjwYNatW0eTJk349NNPAdi7dy+1atXi0UcfZdasWdSrVw+AcePGUbduXfLz8znppJP47bffaN++PUOGDOG9996jZ8+epKam4jvkM3fcuHEMHDiQV155hT179tCrVy9OPvlkEhMTA/NHqQLtwZclLw9mzfLm3MZIr2fVKrjgAu0BRYJNm2D8eHf3eveKvn6Vg0455RSOOOIIfD4f559/Pt9//z2dO3dm5syZ/POf/2T27NnUKmWnw8mTJ9O9e3e6devGH3/8wZIlS1i+fDmNGzemZ8+egFxMxMQc3P/94osvGD9+PMnJyfTv35+srCzWr1/v+u9aHRrgy/Pzz96ePytLyuaOHettO5T7brxRLirDkfbgVSV06NCBhQsXHnRfamoq69evJyYm5rCsc2MMbdu25eeff6Zz587ceeed3HfffYcdd82aNTz88MN89dVX/PbbbwwePLjCy9OstXz44YcsWrSIRYsWsX79eo455piq/5IBoAG+POnp3syDF38BZ2TAiy/Cs88Gvh0qMH78UWrOh8u690NpD15VwkknnURGRgZvvPEGAPn5+dxyyy2MHDkSv9/PzJkz2bVrF5mZmXz88cf07duXv/76C7/fz6WXXsqtt97Kz4Wdsxo1auyfT09NTSUxMZFatWqxdetWPvvsMwDatWvH5s2bmV+4ydi+ffvIO+Ri+7TTTuPJJ5+kqLz7L7/8EpC/RXVogC9PfHxw7CyXkSH1ij//3OuWKKcVFMBVV0lZ13ClPXhVCcYYpkyZwvvvv0+bNm1o27YtCQkJ/Oc//wGgV69eXHDBBXTp0oULLriAlJQUFi9eTK9evUhOTubf//43d955JwCjR49m0KBBDBgwgK5du9KtWzfat2/PsGHD6FtYqTQuLo733nuPG264ga5du3LKKacc1rO/6667yM3NpUuXLnTs2JG77rorsH+UKtDNZsoTFQX/+hc88EBgz9u7t/TqDpWYCHPnygYeKjy8/np4Zs4Xd9ZZsouWqp45c+CeeyA2VjYqccHSpUsPHnpu1MjZUcyGDWXXQXWQw/7u6GYz7isoCK6KdunpMHAg/PYbNGnidWtUdaWlwc03h3dwB+3BV0d+vlwc3X03rFkjr5W4OFlpE4iKcxqMQ5YO0VfEb78FvuhIWW/cPXskyId7UIgE990XXhXrSqNz8JWXmSl5N82bw+WXw++/H3jPR0XJZkRKlUEDfEXk5UGgl0OUFeDz82Wv+nPO0Z5RKFuzRrZQDee59yIa4Ctu504Zhm/UCG69Ff766/DaCPHx8Oef3rRPhQwN8BURExMciXbFZWfD1197V2lPVd/114fPZjLl0QvR8q1ZA1dfDc2aScGj1NTSR+ny82H16sC2T4UcDfAVkZYmiW2BFFXGU5OQAHXqwLvvQq9egWuTcs6SJZIkFSk920j5PatiwQI480zo0EESLrOyyp+2yciQIlhKlUGT7CrCWik24zVjJLhfeSX8979Qo4bXLVJV1a4dvPACbN4s0z8bNkgy044dsHu3jNAkJMjoEUiPLSsrdHvCGuBLNmgQzJ4t0zSVyfMpKIA//nCvXSosaICvqCVL5E1VVs/aSYfOwScmwtFHwxtv6BK5cBAdLcOxpcnOliSqrVsPvm3cKBcEf/0lFwO7dskIU3y8LJ2KipKLgJyc4Br+D4HluJ748suqX7QtX+5sW4JIdHQ0nTt3Jjc3l5iYGC6//HJuvvlmoqrw+fuf//yH22+/ff//+/Tpw9wqjsi+9tprnHrqqTQpXME0atQoxo4dS4cOHap0PLdpgK+o6GjZvrVdu8CeNz5ebhMnwsiRgbvAUN6Kj5e52GbNyv/Z/HwJ9tu2HbgQ2LZNatuvXy//btsmyVupqfJajouTfwsKpHqe25n82oMvWd26Vc+G37DB2bYEEZ/Px6JFiwDYtm0bw4YNIzU1df/OcZVxaICvanAHCfCdOnXaH+BfeumlKh8rEDTAV5QxkmgXqABvjATziy+Gxx6TDwKlShIdLcVDGjaEzp3L/llrZZll8YuBrVtlemDdOhkh2LpVLgb27JHAHB8vUwXWyoqSyg4nQ/m91ECOjgWT+vWrHuBTU+XCLCHB2TZVQk5+DudMOgeA9y9+n4smXwTA1EumEhcd58g5GjRowAsvvEDPnj259957KSgo4LbbbuObb74hOzub66+/nmuuuYbNmzczZMgQUlNTycvL49lnn+XTTz8lMzOT5ORkOnbsyNtvv01SUhJpaWl888033HvvvdSrV4/ff/+dHj168NZbb2GM4b777uOTTz4hMzOTPn368Pzzz/Phhx+yYMEChg8fjs/nY968eZx++uk8/PDDpKSkMGnSJP7zn/9grWXw4MFMmDABgKSkJG688UamT5+Oz+dj6tSpNGzY0JG/TbmstUF/6yEfJ97frrnGBszixdb+8EPgzqdUSdLSrP3zT2vnzbP244+tff55a++7z9pRo6w97TRru3SxtkkTa/1+a6OirPX5rK1Z09pataxNTLQ2JkbeO8cee/Bxs7KsnT3b2gcesLZvX3n8l1968it66uSTq/55lJRk7dKljjdpyZIlFf7ZQW8Osr4HfNb3gM/W+m+t/V8PenNQtdqQmJh42H21atWyW7Zssc8//7y9//77rbXWZmVl2R49etjVq1fbhx9+2D7wwAPWWmvz8vJsampqiccq+v+sWbNszZo17YYNG2x+fr7t3bu3nT17trXW2p07d+7/+UsvvdROmzbNWmttv3797Pz58/d/r+j/mzZtskceeaTdtm2bzc3NtQMGDLBTpkyx1loL7H/8rbfeur/thyrp7w4ssNWIndqDr4zZswN3rk6dAncupUqTmAhHHSW38uTkyMhA8dGBLVtkiiAlRVYNfP01fPEFLFsmPc/MTJkiaNAACrfqjChHHln1x0ZFyVK59u2da08VZeZlkpkn9Rx8Mb5yfrp6vvjiC3777Tc++OADQPZ9X7lyJT179uTKK68kNzeXc889l+Tk5HKP1atXL5oVToMlJyezdu1ajj/+eGbNmsWDDz5IRkYGu3btomPHjpx11lmlHmf+/Pn079+f+vXrAzB8+HC+++47zj33XOLi4jjzzDMB6NGjBzNnzqzmX6DiNMBXxsqVMkQZo382pQ4TF3cgb2DzZrkgXrNGEsmefx58PkkILJqPL0oC9Pnggw+gZk3v2l4ZBQUyldG8efWP1bKlTMdVJQkxJ8fztfDvX/w+zR5ttj+4A8RFx/HBxR84ep7Vq1cTHR1NgwYNsNby5JNPctpppx32c9999x2ffvopI0eOZOzYsVx++eVlHjc+Pn7/19HR0eTl5ZGVlcV1113HggULOPLII7n33nsrvKVsSWJjY/dvb1t0jkCJwEmvamjcWOY7lVKl+/RTCVyjRslSwNWrpZeemnp4sp3fD9dcAyec4ElTq+S996BFC2jTRjahqs569MaN5QKnKrKyZCTEQxdNvoic/INXa+Tk53Dh5AsdO8f27du59tprGTNmDMYYTjvtNJ599llyC7dWXrFiBenp6axbt46GDRty9dVXM2rUqP3bxcbGxu7/2YooCub16tUjLS1t/0gBHLz1bHG9evXi22+/ZceOHeTn5zNp0iT69etXnV/bEdoVrajERHj88cBs7qBUKNuwQS6ES/ggPEyTJjB+vPttclJKikwvrFolAf4//5FAPWIEDB0KbdtW/FiNG1dvRDBI1sL7YnzERccdFuyrqigxrmiZ3GWXXcbYsWMBWZq2du1aunfvjrWW+vXr8/HHH/PNN9/w0EMPERsbS1JS0v695EePHk2XLl3o3r07b7/9drnnrl27NldffTWdOnWiUaNG9Cw2dTRy5Eiuvfba/Ul2RRo3bsz48eMZMGDA/iS7c845x5G/RXXodrEV1bkz/PqrBnilyvP00/CPf5S/9M7ng59+Cs18k6ZNpRZBcUVLDxs3lt3fOnYs/zgLFkC/flKZriqaN5fVDw4qadvS0gQiiz5SuLFdrGdD9MaYQcaY5caYVcaY27xqR4X4/bIpiAZ3FUHeXvw2LR9rSdS/o2j5WEveXlx+7weQJXHldRz8ftlQJRSDO8B55x2+rC8nR5IGV6+Gk06q2PK3zp2l13/kkXKBULNm5ZYLbt7saRGhuOg4Prv0Mz679DOS4pL2f63BPTh4EuCNMdHA08DpQAfgEmNMcJYCMgZ69IATT/S6JUoFzNuL32b0J6NZt3cdFsu6vesY/cnoigX5vLyyC9tER0vv9tZbnWtwoJ1/PiQllf79XbvgtNOkImFZ4uPh5ZelINHmzfDOOzB2LHTteiDgF0sEO0x0tKxaUKoEXvXgewGrrLWrrbU5wLuA9xMWJUlIgCef9LoVSgXUHV/dQUbuwcPGGbkZ3PHVHeU/uLwefEICvP9+aBe2Of54SRwsTW6uJMCNGFHxHnbdujB4MDz0ECxaJDkMX3wB48bBySdLsPf7D76wiI31PJNeBS+v3mFNgeJ1FjcW3refMWa0MWaBMWZBFes8VZ/fD2edJVfTSkWQ9XvXV+r+g5QV4P1+maNv0aIarQsCcXHQv3/ZP5OZCZ98IhtDVfUcxx4Lt9wCM2dKZcFff5XpwksvlWH9tLTyRwlUxAraS2hr7QvW2hRrbUp9Lxrg98synwpkXSoVbprXKnmNd2n3HyQ/v+Qh+qKgWM7a5JAxdGjZw/QgyXMPPABTp1b/fMbIhlMjRsCbb8qw/r595V9oqIjlVYDfBBQv4dSs8L7g4PfDq6/KsjgtaqMi0LiTxuGP9R90nz/Wz7iTxpX/4NJ68ImJshtiuCSrnn56xXbsy8yEYcPgt9+cb0NiovPHVGHDqwA/H2hjjGlljIkDhgLTPGrLAUU7eC1YIJu8KBWhhncezgtnvUCLWi0wGFrUasELZ73A8M7Dy39wSZW6/H4ZDTviCOcb65X69aXYTUVkZMg8+tat7rYpjIwbN46OHTvSpUsXkpOT+fHHH3nsscfIqMKSwtdee42/Dl3WGAE86Z5aa/OMMWOA/wHRwCvWWm8rNvj9kin/3nuhUzJTKRcN7zy8YgH9UIcG+IQEGDJEerzh5uKLJQmuIj353bvh1FPhxx893QEuFMybN4/p06fz888/Ex8fz44dO8jJyWHIkCFceuml+P3+8g9SKD8//7BtXiOFZ3Pw1toZ1tq21trW1toKjPu5yOeD22+HGTM0uCtVXYcGu7p1w3clyjnnSCZ7ReTlyX4Wl17q6dp1p1W5XkIZNm/eTL169fbXiq9Xrx4ffPABf/31FwMGDGDAgAEA/O1vfyMlJYWOHTtyzz337H98y5Yt+ec//0n37t2ZNGnS/m1ek5OTyczMLPGc4Shok+wCIjpaAvonn8Add4TP3KBSXireg/f54KOPwneuuEuXstepHyozU3bVG+dtn8Yp1aqXUIZTTz2VDRs20LZtW6677jq+/fZb/v73v9OkSRNmzZrFrFmzABnGX7BgAb/99hvffvstvxXLczjiiCP4+eefufTSS0lJSeHtt99m0aJF+Kpa+z8ERW6A9/mgXTtYvFiqTimlnFEU4P1+uOkmWeoVroyBs8+u3GMyMmDSJHfaU5bMTBg9WmrpJydLFcH27eG556p8yGrVSyhDUlISCxcu5IUXXqB+/foMGTKE11577bCfmzx5Mt27d6dbt2788ccfLFmyZP/3hgwZUq02hIPITBH3++GCC+DFFyt39a2UKl9RAZiWLeHf//a0KQFx4YUySpGaWrGfj4mBc891tUkluuYaKTB06B4BY8dK/lGHyhcTrVa9hHJER0fTv39/+vfvT+fOnXn99dcP+v6aNWt4+OGHmT9/PnXq1GHkyJEHbeuaGK6jRpUQeT14nw8ee0yW67gd3K2V5KKGDWHtWnfPpVSwyMuTIPbRRxWfnw5lAwdWrtiMzycFtAJt0aKSNwDKypKLlCrsU16tegllWL58OStXrtz//0WLFtGiRYuDtmtNTU0lMTGRWrVqsXXrVj777LNSj1faNq/hLnICfGysLGuZPRuuvjow53zqKZg+HXbsgOOOg40bA3Nepbx00UVSiKVdO69bEhg+X+WmIQoKoNgWpAFT2hI9a2VHumeeqfQhq1UvoQxpaWmMGDGCDh060KVLF5YsWcK9997L6NGjGTRoEAMGDKBr165069aN9u3bM2zYMPr27Vvq8Yq2eY20JLvI2C7W75c5p6lToV49h1pVAeecI9tGwoFtJOfPh0aNAtcGpZT7nn1WtsityBrtc86Bjz92vUkHsVZGLEurn5+UBE88AVdcUantYkES7e746g7W711P81rNGXfSuKotr4xwbmwXGzpz8DVqyOYURVsyVpTPB9ddB+PHS5ANpOJV8PLzYcsW6N1bgnx9TwrwKqXccOaZcPPN5f9cUpKMcATanj1lL83LyanytEGV6yUo14VGgO/WTapgrV0rt5UrYelSWLMG/vpLAr7PJxmtOTkyp2SM9NzfesubhBY4/IIiL0/a27cv/PCDrA9WSoW+I4+EJk3kM6ksOTmyjWygxcXJkr7lyyVf4NDPyw4dAju6qQIiNAJ8VBQcc4zcSpKeLnNIxS8ANm+G++6Dtm0D2dKDlVTHPjdX2njCCTB3LtSqFfBmKaVccNFF8OijZSertW7tTSBNTISFC+XrffsO/rxctUqK76iwExoBvjyJiXIFWoVlHq6Kiyv5/txceVP16wfff1/+jlRKqeB33nkyF19atnZsrDfD84eqUUPWwHfq5HVLlMsiJ4veC7fdBpddJhts+P0H15/OyYFly+D440tPfFFKhY7yMuMTEmSuXqkA0QDvpvbtZb399u0yPPbf/0KfPpLNWrOmLJf59deKbVShlApu0dEwaFDZP9OjR2DaohQa4APDGAn2N90Ec+ZIRusHH8C990qugFZcUio8XHSRDIGX5NRTJZ9IqQDRV5sXEhLglFNkBztdE69U+Dj11JKr2tWoIdXiQlDjZs0xxjh2a9ys7Cp3N998M4899tj+/5922mmMGjVq//9vueUWHn30UaZNm8b48eMr9buMHDmSDz74oMTv3XTTTXz33XcAXHXVVXTt2pUuXbpw4YUXkpaWBsC9995L06ZNSU5Opn379vztb3+joKAAgGXLlpGcnEy3bt34888/eeedd/Yfe/HixYwcObJSbXVCeCTZKaVUMKhVS5ajLTikNFd2tgT/ELRl0wZa/HO6Y8dbN6HsPIS+ffsyefJkbrrpJgoKCtixYwepxer8z507l4kTJ9K7d2/OruxGP6XYuXMnP/zww/4Li4kTJ1KzcOvwsWPH8tRTT3HbbbcBcgHyj3/8g4KCAk488US+/fZbBgwYwMcff8yFF17InXfeyTfffMM777zDsGHDAOjcuTMbN25k/fr1NG9evTK+laE9eKWCTWGPQIWoSy45OKEWZLmu1r2okD59+jBv3jwA/vjjDzp16kSNGjXYvXs32dnZLF26lO7du/Paa68xZswYQHrmf//73+nTpw9HHXXU/l66tZYxY8bQrl07Tj75ZLZt21biOT/88EMGFcufKAru1loyMzMxJWwlnpOTQ1ZWFnXq1GHGjBk89thjPPvsswwYMIDbbruN2bNnk5yczMSJEwE466yzePfdd537Q1WABnilgom1srPXiy+WXXlMBa+zzpK8myJxccGxPC5ENGnShJiYGNavX8/cuXM57rjjOPbYY5k3bx4LFiygc+fOxJWwBHnz5s18//33TJ8+fX9ve8qUKSxfvpwlS5bwxhtvMHfu3BLPOWfOHHockgB5xRVX0KhRI5YtW8YNN9yw//6JEyeSnJxM48aNadu2LcnJyZxxxhlce+213HzzzcyaNYvx48dzwgknsGjRIm4urHCYkpLC7NmznfozVYgO0UearCz4+mv4/XfJ3s/NleHD4recHFnad/PN0KKF1y2OLMbAgw9KkPj0U3jtNahd2+tWqcpo0wbq1DlQUjsuzpvd40JYnz59mDt3LnPnzmXs2LFs2rSJuXPnUqtWrVI3lTn33HOJioqiQ4cObC3cWOe7777jkksuITo6miZNmjBw4MASH7t582bqH1I+/NVXXyU/P58bbriB9957jyuuuAI4MESfm5vLhRdeyLvvvsvQoUPL/Z0aNGjAX3/9VZk/Q7VpgI8EW7bIrnaTJkkWf3y8bIqRn196LzEmBp5/XoYb778fmjYNbJsjWZ8+Mod78slSvfGjj2Q3QhU6zj9fdmcrKJDlc8nJXrcopPTt25e5c+eyePFiOnXqxJFHHskjjzxCzZo19wfaQ8UX2/67spuo+Xy+g/aSLxIdHc3QoUN58MEHDztvbGwsgwYN4rvvvqtQgM/KysLn81WqXdWlQ/ThLDdXNrdp0UKW6H39tfTQU1OlnGZZb4K8POntv/WW9EiuvVYuFFRgtGoFP/8MRx8txZDuu08uyFRoOO+8AxUqBw06eMhelatPnz5Mnz6dunXrEh0dTd26ddmzZw/z5s2jT58+FT7OiSeeyHvvvUd+fj6bN29m1qxZJf7cMcccw6pVqwC5OCj+9bRp02jfvv1hj7HWMmfOHFq3bn3Y90raf37FihV0CnD1QO3Bh7Pp0+GPP2TIvarFdHJz5fbqq1K0Z9QouOsu3Q0vEGrVglmz4Oqr4Z57YMYM6c03aeJ1y1SR/HzYvfvw+vLHHy/vOZ8PLrjAm7Y5pFHTI8vNfK/s8crTuXNnduzYsT8Lvei+tLQ06lWilv95553H119/TYcOHWjevDnHlTISNnjwYJ5//nlGjRqFtZYRI0aQmpqKtZauXbvy7LPP7v/ZiRMn8tZbb5Gbm0uXLl247rrrDjtely5diI6OpmvXrowcOXL/3PzgwYMr3HZHWGuD/tajRw+rqqBPH2uln+7cLT7eWp/P2ltusXbHDq9/w8hQUGDtQw/J3z8x0dpp07xukSoyZoy10dHWZmQc/r1+/eQ527070K2qliVLlnjdBE/07dvX7nbpucrKyrLHHnuszc3NLfVnSvq7AwtsNWKnDtGHqzVrZIjXadnZkjz01FPQvLkU69mzx/nzqAOMgX/8A6ZOldGUs8+WKZOSCqqowHnmGXjlFVkSV9J77e67JXtekyRDwiOPPML69etdOfb69esZP348MSXtMOoiDfDhqijBxy3Z2ZKoN3EiNGsmQ8jFilEoF5x9Nvz4o6xweP556NxZ9vdWgffFF3LRlZEhF12F67YPMnAgTJ4c+LapKjn22GPp0qWLK8du06YN/fv3d+XYZdEAH45yc2UddSA2scnKgvR0eOghybR/4AEoLOuoXJCcDIsXy94GK1dC166SG6EC548/ZF69aBlcTg589ZW3bXKY1RoMAeXW31sDfDj65JPAV0PLzJTAPm4cjB4d2HNHmsaNZRndoEESXJ56yusWRY6tW6VnfuhF7E8/edMeFyQkJLBz504N8gFirWXnzp0kHFr90AGaRR+OHnkEDlmiETBZWRDgYg4RKTFRCuG8+y6cdJLXrYkMmZmySdSuXYd/b88e2LHj8Gz6ENSsWTM2btzI9u3bvW5KxEhISKBZs2aOH1cDfLhxK7muMjIyvD1/pIiKgmLLiJSLCgpgyBBYtUpqRBSXkCCFiMKk1nxsbCytWrXyuhnKARrgw43byXUVUTQ3qVS4eOwx+N//Ds9riY+Hbt2kRoHu9a6CjL4iw0kgk+vKosu3VLjp0wcaNpQSzn4/1KwpwX3gQJg58/Dd45QKAtqDDydeJNeVpISazkqFtN69Yd06WTGyaxfs3CnJjo0aed0ypUqlAT6cPPywd8l1xWkPXoUjY6S+fFKSFHlSKsjpEH24WLMGfvnF61YIr6cIlFJKaYAPG8GQXFdEA7xSSnlOA3w4CJbkuiLB0g6llIpgGuDDwbRpwdN7h/L3mldKKeU6DfDhwMvKdSWJjtZEO6WU8pgG+FC3enXwJNcViY7WYjdKKeWx8Fsml5kpW2j+8Qf89hvMnw/168N773ndMnc8+yzk53vdioMVBfg6dbxuiVJKRazQDfAZGbBsGSxZAr/+KoF82TIpQOH3S+JZZqbUh37hBa9b646i5LrcXK9bcrCoKC12o8JTfj6sWAFHHw2xsV63RqkyhUaAz8iAN9+ERYtkm8xly6SalM8nyVzp6QcndaWmSunIv/0NJkyAGjU8a7qrgi25rkhUlA7Rq/CRkSHlaN99V3bwy8+X1/j558PIkXDiiTJqpVSQMaGw529KdLRd4PcfvgdzSRIToUULeOst2QQinB13HPzwg9etOFzNmvDVV5CS4nVLlKqarVvlAvrtt+U9Fh8vHYfiiirbRUXJTnMjRkhJW910RjnEGLPQWlvlD9LQ6MEXFJQf3GNjIS4O/vtfuO668L+iXr1aRjSCkTE6RK9Ci7Uy3TdlCrzzjry/YmJkdBBKXhVi7YHVKy+9JI+Lj4fhw+Hyy6F7d3kvKOWR0Ajw5fH74eST4bnnZAOISPD008GXXFecDtGrYJeXB99/D++/Dx9+KME6L+9AoabKLPUs6oSkpcl78+WXpXc/YgRceil06qTBXgVcaAzRG2MXlPQNnw9q1YLXX4dTTw10s7yTkyMrAw4dMgwWNWtKzsTZZ3vdEqUOtm8ffP45TJok+7tHR0sv3a1clthYuR1xhMzXDx8O7dq5cy4Vdqo7RO/KZJEx5iFjzDJjzG/GmCnGmNqF97c0xmQaYxYV3p6r4gkkuP/97zKUFknBHWRuMJgvzKzVHrwKHhs2SK+6Tx+oVw+uukqG4jMyJOC7maiamyvn2bBBEn67dYPWreE//5ENopRykSs9eGPMqcDX1to8Y8wEAGvtP40xLYHp1tpOlTneQT34xETo0AHeeAPat3e03SEjWJPriiQmygfqiBFet0RFImtl6eyHH0rm+8aN0ikIpovO+HhpU6tWcsExdCg0bep1q1SQCcokO2vtF8X++wNwYbUPGh8vtyeekASWSJ3P+vPP4E2uK1JQEFwfpir85eTAt99KQaspU2T+PCcn+GpEFCma31+6FO66C+68U4bur74aLroIGjTwtn0qLARiPceVwGfF/t/KGPOLMeZbY8wJFTqCMfKiX7NGeoWRGtxBtoUN5uQ6kPZpgFeBMHcuDB4MtWvDhRfCK69IjYz09OAN7ofKzJRVJ7/+Cv/3f9C8ORx7rBSx2rXL69apEFblIXpjzJdAoxK+dYe1dmrhz9wBpADnW2utMSYeSLLW7jTG9AA+Bjpaaw/LFjPGjAZGAzRv2LDHui1bqtTOsBLsyXVFoqLggQfgX//yuiUqnD33HIwdG74Xk4mJktXfqxeMGgXnnisJrCpieDZEb609uazvG2NGAmcCJ9nCqwhrbTaQXfj1QmPMn0Bb4LAkeWvtC8ALACkpKUGcURZAwZ5cV6Sg4MD64UiVkwPTp8ta6l69oFFJ18KqSvLy4PrrpZhVuAZ3OPAemj1bNpQaPVqq5l11FZx5plwAKFUGV+bgjTGDgP8D+llrM4rdXx/YZa3NN8YcBbQBVrvRhrA0cWJwbQtblopUHQxHmzbBU08d2AQoKkrmW/1+6NEDBg6U4deUFO2NVcXu3RLcFi2S7PRIUfR+mjlTEmxzc6X2x1VXwaBBUppbqUO4VejmKSAemGlkvvwHa+21wInAfcaYXKAAuNZaq5NMFXXWWXIln5UV/D35SOrBWysJXhMmwKxZct+hRVKys+HLL+XnfD7peTZoIKVN+/WToN+1qySSqpItXw4nnQTbtx8oRhOJii7yp0+X11NenuQhXHEFnHKKboKj9guNQjcpKXbBghJL3USeP/6QylgrVwZ3EB06VIqJhLN9+2S55kMPwY4dVX8+EhKkzHJmJrRsCSecILdevSSzOtzLLlfE//4nSXSHbiylDqhZU0aNzj1Xgn3//vraCXHVnYPXAB+KCgqkFObYsdKTCcbezFlnSc5AOFq6FB59VDYiMcadoeLERDl2bq7UfejXD/r2laB/5JGRs5LEWvlb33VXeM+3O6loE5yi1UcjRshrRzfBCTka4CPZ9u2ysc6MGcE3H3nSSTIkHS7y8uCTT2Qzo99/l4uqQC5XLPrQzsuTxL1u3aSHdtxx0LOnlEINNzk5cOWVB6rOqcqLipKLxdhYGVUbMUJeL5FygRjiNMArmfe97DJJQAqWD8LjjpM1yqFu2zZJmHvySZlHD6bkweho+fDOypJ14D17woAB0svv3j20s6y3b5fksaVLtefulOhoyf/w++Xz4tJLJe9Dg33Q0gCvRHY23H+/DGdmZ7tbX7s8xkjS2Lx53rWhOqyVTOUJE2TuF0Jn+9uiio8ZGdCsmVxo9e8vwb9Tp9BIwFq8WDLEd+8OnWI1oSYmRvI+6tQ5sAnOMcd43Sp1CA3w6mArVkgp399/D3wSXkyM3E49FR58MPR2zcrIkD29J0yAzZvl/yHw/iiX3y+9t6wsaNNG1lKPGCEZ/MFm6lQYNix4RqIiQWysvG8bNZLkvGHDZEMc5TkN8Opw1sp2rX//u3yoV2Zf66qIi5O5vgsvhHvugaOPdvd8TvvzT3jsMXj1Vfl/MK9OcEqPHhBM7ylrZQRq/HgdkvdS0SY4zZtL9byhQyWpU3kiKLeLVR4zRnrxq1dLFq3P58554uPl2KNGybK9N98MneBeUACffiq92U6d4PnnJbBHQnAH6NLF6xYckJUlF4cTJmhw91p2tjwfK1bA3XfLiE+XLrLJl5YLDzka4MNZ3boSdGfOlG0p/X5njuvzSQLXzTfD+vWyNWyzZs4c2227dkkgadJEeiezZ8sHWiTN9fp8MicfDDZvlqp+wbgSJNIVjf4tXgy33SY1GlJS5GJ4xw6vW6cqQAN8JOjbV6qA/etf8uFe1eIXiYlQq5ZsbfnXX7JkrF49Z9vqlp9/PrDn9r//DVu3BldGfCDFxUHnzl63QqYIOnWS12aoJDFGqsxMCfYLF8Itt8gFfd++Mq21Z4/XrVOl0Dn4SLNmjSTSLFhQ8eHopCS5MLj7bql97daQv9Oys2HyZOmxr1kjQcTL1QXBIjZWlv/Vru1dGyZNkqkd7bWHtqQkGf3q00f2sj/rLLlPOcKz3eRUiGrVStbNf/ABXHONfMCWloSXlCTLaO6/XzJrQ2GJFci0wZNPylCitZHbUy9NjRreBfeCArj9dpnT1fn20Ff03po1C+bPl2A/cKB0BM44I3Q6A2FKh+gjUVEJyzVrpODFoW/CxERZ4vb667B2rSypCvbgbq1UzjvlFGjbVgLIvn0a3EvSvr03501Pl01RnnxSg3s4SkuTzsJnn8ko4RFHwHnnyaY4wVhOOwJogI9ktWrBiy/KjlRt2sjcbPfu8NFHUkHs/PODv3713r3w+OOylOe88yTIZ2frB0pZjj028Odctw6Sk+Gbb3RYPhLs2ycXcR9/LKN/derIvzNnSrllFRA6RK8ko3rJEllW17at162pmN9/h0cegXfflYsQDRoVk5QkF3GBNGeO9Nz37dMciEhUtL3tpEnSmwdZFjliBBx/fGB3vCsokIvNli0jokRvkHfPVMDExAR/cM/NhffflyItvXrJEsCsLA3ulWGMZK4Hyssvy7TJ3r0a3JUE+3374LXXJCHv6KNlRYtbrJXRyKeekgqbNWrIFNUZZ8iS2TCnPXgV/DZvhmeekTdpfv6BHoGqvMzMwNQcz8+Hm26CV17R+XZ1OGsPDOMff7ys6qlVy5ljr1kDX38tuz9+8410DKw9+HX49dfSoZk+PThLNjtEA7wKTtbC999LTfuZM+U+t0vuRoKGDaUCoZv27oWzz5YPbR1dUWXJy5ML+JkzZdi+KjZvliz+6dMlByctTabtyloGnJMDO3fKRegPP1TtvCFAA7wKLmlpMvT+4IOyZWiklI4NFLcL3KxaBSedJMOuekGmKiImpupbG/ftK8V3YmOrtmLm119l6ijYk4mrSAO8Cg4rVsDEifDGGzJPrIHdedHR7g5HfvWVrGRISwuPXfhU4NSsWbXHLV4sF5JVvZiMiZE5+o4dq/b4IBeely0qNOTnw7Rpsmd5167w0ksypKvB3R2JifJ3dsOTT0rS1L59GtxV5VgryW9VUdXHFSkogHnzqneMIKYBXgXejh0wbpzsPz18uMyBZWXp+li35ec7n0GfmwtXXimbkWgynaqK/Pyq9+Crm5iXkSEJd2FKh+hVYOXnSy9y1y7dYCTQcnPhqKOcO97OnbLcaPFiDe6q6vLyqt4Tr1On+uefPbv6xwhS2oNXgfXZZzKMq8E98Fq2dC6ZaMkSSdj75RcN7qp6cnKqFuCzs6uenFfc1q1huyOe9uBVYD34oK5j90pysnPHuuACWZ6kVHVFR0uZ7LJYC3/+CT/+CN99J+W1V692pgqezyfHPe206h8ryGiAV4GzerXsOKUCLy7O2Rr0f/3l3LFUZEtIOPy+Xbvgp59g7lxZnfHrrxLko6IOXg6Xm1v986enS80NDfBKVcOTT8ocvAo8nw+6dHHmWPn5ukufck5Cglz4//CDJLz98IME+IQECb5uf2bk50uhnfvvd/c8HtAArwIjK0uWwTlxxa0qLyvLuSI3O3fKiIDmUSgn7N0rxZFycw9+TQVyR8gff4QtW2RlTxjRJDsVGJMne92CyBYbCw0aOHOsLVvKnzNVqqJyc71PvI2PlyTUwYOl7G2Y1HLQAK8C48EHdVjXS23bOrc95pYtEbHVpoogRdXwZsyQfRSaN5fKmrt3e92yatEAr9z3yy+yw5PyTkqKc8faulVzKVT4SkuDjRvhzjuhSRMYOjRkk4M1wCv3Pfqoztd6ye93NsBv2aLPpwp/GRnyOn//fRgwANq1gxdfDKlS2hrglbv27IEPPpCaz8obMTHO7iK3YYOWFVaRo6BAgvqKFTB2rOSyXHONFHsKchrglbteey1st2IMGZmZzu6WtW6dc8dSKpSkpUnP/uWXZVTsxhuDuvOin7zKPdbCww/LG0J5p1at6u+6VZwWuVGRLj9fLpxfegnOPDNoP+M0wCv3zJola1yVtzp0cPZ427Y5ezylQlVGhnzO9eoluSlBRgO8co8ujfOeMdC7t7PH3LXL2eMpFcqysmD5ctkl848/vG7NQTTAK3f89ZdsCKG8lZTk7CYzublBOxyplGfy8mRkq3dv+OILr1uznwZ45Y5nngmbalAhz8kM+u3bpeqXUupwaWlw7rnw3HNetwTQAK/ckJsLTz8tlaGUt7KyZP2uU7ZulbK3SqmSZWbCLbcERYa9BnjlvGnTtNJZsGjSxNmArGVqlSpf0VK6qVM9bYYGeOW8CRNk8wjlPSeH50ECvBa5Uap86enw+eeeNkEDvHLW0qXw++9et0KBVLA77jhnj6llapWquJkzPT29BnjlrMce0z3fg4XfD126OHvM9et1+kWpitq40dMd6TTAK+ekp8Nbb+kQbrDIy4NOnZw95oYNzh5PqXDm88H333t2eg3wyjnvvKMJWMEkPx9atHD2mJs2OXs8pcLZvn3w5Zeend61AG+MudcYs8kYs6jwdkax7/3LGLPKGLPcGHOaW21QAWStJNeF0FaKYa91a+cvuLZvd/Z4SoUza+F///Ps9DEuH3+itfbh4ncYYzoAQ4GOQBPgS2NMW2utTuyFsh9/DMpazBGtWzfnj+nhfKJSIWn1aunJO7nhUwV5MUR/DvCutTbbWrsGWAX08qAdykm6a1xwSUiQDTCclJ2tGfRKVZbPB/PmeXJqtwP8GGPMb8aYV4wxdQrvawoUz9TZWHifClU7dsD06VqaNpjExzufQb9tm1w4KKUqLi3Ns3n4agV4Y8yXxpjfS7idAzwLtAaSgc3AI5U89mhjzAJjzILtOu8X3F56CaI0XzOoZGY6n0G/ZYusrVdKVVxBgWcFb6r1brXWnlyRnzPGvAhML/zvJuDIYt9uVnjfocd+AXgBICUlRbuGwaqgACZOlICigkdCAtSr5+wxt2519nhKRYrly+Uz0ucL6GndzKJvXOy/5wFF5c2mAUONMfHGmFZAG+Ant9qhXPa//2lwD0bt2zt/TC1Tq1TVJCRIInKAuTne9qAxJhmwwFrgGgBr7R/GmMnAEiAPuF4z6EPYgw9q3flg1LOn88fUMrVKVU1GBsyaBf37B/S0rvXgrbWXWWs7W2u7WGvPttZuLva9cdba1tbadtbaz9xqg3LZ2rXwww9et0IdKjERevRw/rjr1nm+/aVSISkvDz79NOCn1cwoVXVPPqkf+MEoOtr5XeRA6tArpapm8WLIyQnoKTXAq6rJzoYXXwz4C1ZVQEYGdOjg/HE3by7/Z5RSJUtIgAULAnpKDfCqaj74QNe9B6sjjpCd5Jymy1WVqrrMTPjmm4CeUgO8qpoHH5QCDir4OL3+vciePe4cV6lIkJsb8Hl4DfCq8n79FVat8roVqiTGQO/ezh83I0M+oJRSVffzzwFdaqoBXlXeo4/KHLwKPklJ0LWr88fdulXL1CpVXbGx0kEKEA3wqnJSU2HyZNlrXAUfa93JoN+6VcvUKlVdOTkBnYfXAK8q5/XXte58MMvOhqOPdv64W7ZoUqVS1ZWdHdB5eP2kVhVnrW4LG+yaNXOnp711q87BK+WEH38MWP0QDfCq4r77Dnbt8roVqizJye4c96+/tEytUk6IioI//gjMqQJyFhUeHnpIl8YFs9hYdzLoQarY6RC9UtWXlxeweXgN8KpiNm+GL7/0uhWqLD6fOwl2ABs2uHNcpSJNVhbMmBGQU2mAVxXz3HOyxloFr9xc9wK8lqlVyjlz5gRkREwDvCpfXh489ZTOwYaCpk2dP2Zmpm40o5STCgpgxQrXT6MBXpVv+nTNoA4FRx/tzijL+PG6a6BSTrIWvv3W9dNogFflmzAB9u3zuhWqPCkpzh/zr78kuVKXRirlnIyMgMzDa4BXZVuxIqClFVUV+XzQs6fzxx07NqC1s5WKGLNnuz4PrwFele3xx3V4PhTExjqfYPfzzzBtmj7/SrkhMxPWrnX1FBrgVekyMqQ0rfbggl9mJnTs6NzxrIWrr5bjKqWcFx0txcNcpAFele7dd71ugaqopCSoU8e54334ISxf7tzxlFIHS0uDzz5z9RQa4FXpHnwQ0tO9boWqiGOOce5Y2dkwZow+90q5zeWKdhrgVcnmz4eNG71uhaqoXr2cO9ajj+qqCaUCYe9e2LTJtcNrgFcl013jQkdSEnTv7syxtm2DBx7Q516pQIiNdXUeXgO8OtzOnZI9rZuLhAZjnMugv/VWTapUKlD27YP//c+1w2uAV4d7+WWtOx9KMjOhffvqH2fxYpg8GXJyqn8spVTFfPWVa4fWAK8OVlAAEyfq8qhQ0rAhJCRU7xjWwujRkmCnlAqcbdvk5gIN8Opg33+vCVahxonh+enTpQev0zJKBVZCglS1c4EGeHWwtm0hSl8WISM6Go49tnrHyMmBv/1Nl8Up5YXUVPjiC1cOrZ/k6mCNGskcvN/vdUtURSQmQteu1TvGU0/B7t3OtEcpVXlffunKYTXAq8NddBGcfjrEx3vdElWe/PzqDdHv3An33KPL4pTy0saNrlxka4BXJXv5ZahRw+tWqPLk5sJRR1X98f/6l24mo5TXEhJgzhzHD6sBXpWsVi1ZMuXzed0SVZYWLaqeM7F0Kbz1lmbOK+W1fftg5kzHD6sBXpVuwAC48koN8sGsW7eqP/Zvf9PgrlQwsBbmzXP8sBrgVdkefljWWavgExdX9Qz6L76Q/QYKCpxtk1Kq8hIT4dprHT+sBnhVtoQE+Ogj7cUHo4SEqiXY5eVJURtNrFMqODRtCiNHOn5YDfCqfN26STKWLp0LLjk5VQvwzz0HO3Y43x6lVOX5/fDCC67UH9EAryrm9tu1CE6wiYmp/PTJnj1wxx1a1EapYBAdDccfD/36uXJ4/bQOlF27vG5B9URHw4cfVr/muXJOmzaV3xTorrt0MxmlgkVcHDz9tGuH1wAfCHfcAUccEfrbcB51lGxEo0P1waFnz8r9/KpVUt8gK8ud9iilKi4hQVYpHX20a6fQAB8I99wj/95xh7ftcMLVV0Pv3hAb63VLIpvfDykplXvM9ddr712pYBEbC/ff7+opNMAHQlwc9O0LDz7odUuqzxh45x3txXstJgY6dar4z8+aJTsF5ue71yalVMUkJkpwr1PH1dNogA+UGTPk3717vW2HExo2hNde0yDvpczMigf4/HxdFqdUMKlbF667zvXTaIAPlJo1pVpRrVpet8QZ554L55yjG9J4pVatiu8V8MorsHmzu+1RSlVMYiI8+2xApjk1wKuqe/55qF3b61ZEpmOOqdjP7dsHt96qy+KUCgbGSO2KM84IyOk0wKuqq1ED3n9fq9wFmjGS6FgR992n9eaVChYJCVLUprLLW6tIA7yqnhNOgGuu0SAfSElJFdtkZu1aWWOry+KU8l5cHFxwQdWqT1aRKwHeGPOeMWZR4W2tMWZR4f0tjTGZxb73nBvnVwE2fjw0aeJ1KyJLRT4kbrhB93pXKljExMjmXYE8pRsHtdYOKfraGPMIUDx1/E9rbbIb51UeiY+HKVNkZ7PMTK9bE/4yM6VscFnmzIGvvgr94kpKhQO/H267LeA7c7o6RG+MMcDFwCQ3z6OCQOfOUtBHl865r0kTGe4rTUGBFCTSiy2lgkNioiS7Bpjbc/AnAFuttSuL3dfKGPOLMeZbY8wJpT3QGDPaGLPAGLNg+/btLjdTOeLWW6FDB6lbr9zTpUvZ33/rLVi/PjBtUUqVLTERHn/ck308qhzgjTFfGmN+L+F2TrEfu4SDe++bgebW2m7AWOAdY0zNko5vrX3BWptirU2pX79+VZupAikqCj74QDekcVNMjEyFlCY9HW6+WZfFKRUsWrWCoUM9OXWV5+CttSeX9X1jTAxwPtCj2GOygezCrxcaY/4E2gILqtoOFWRatICnnoIxYzTIuMHvh65dS//+f/+rQ/NKBQufD158MWDL4g7lSpJdoZOBZdbajUV3GGPqA7ustfnGmKOANsBqF9ugvDBiBLz3niR5aRa3s/LySs+g37gRHn1UA7xSTjJGEoljYg5MPxYUyHsxJ0e+7/dLXZBataS+fN26UL8+HHdcxWtWuMDNAD+Uw5PrTgTuM8bkAgXAtdbaEN8oXR3GGHjzTdmvfM8er1sTXvLzoXnzkr930016QaVUSWJj5RYTI59P1sp7KTdXbgkJUl+iRg2pzlmnjgTo+vWhQQP5f506B75X/N8gnpJ0LcBba0eWcN+HwIdunVMFkXr1JMgPGaKbnDjpqKMk1+FQ8+fLhka6LE6Fo4r0ohMTJUgX9aKPOEI+hxo1kh51aQG6Zs2S31NhwM0evIp0Z54JF14IkydrNTWndO9++H3W6rI4FRqioiQIO9GLLvo6yHvRXtIAr9z19NMwc6buZuaE+Hjo2fPw+ydPhlWrAt8epSrL74erroKzzoqYXrSXNMArdyUlwUcfwcCB2sOsrvj4w9fAZ2VJSVpdsaBCQVQU9OsHAwZ43ZKIoJdMyn29e0sQ0ip31ZOVBZ06HXzfQw9pcFehwxjprauA0ACvAuOBByT726P1oGEhIUHmI4tYKwFekxhVKKlRw+sWRAwN8CowYmNlqF63la26du0O/r8xkuOQmKgXTio05OdrDz6ANMCrwDnmGLj/fglIqvJ69Tr8vssug0WLoH17nQJRwS8/X3vwAaQBXgXWTTdJJbYYze+slMRE6NGj5O8dfbQE+auu0hESFdxyc7UHH0Aa4FVgRUXB++/rutXKio4+PMGuuLg4eOIJmQapXVsvoFRwys/XkaYA0gCvAq9ZM3juOX2jV0ZGBnTsWP7PDRoEy5bJjnP691XBJj5e80UCSAO88sbw4XDSSdLzVOU74oiKB+yGDeG77+Cuu3TIXgUXfT0GlAZ45Z3XXpNCOKp8Fem9FxcVBbfdBt9+C40bS89JKa9pgm1AaYBX3qlbF955R4eSy2NM1bec7NkTli+HwYP176y8pxf0AaUBXnnrtNNg2DAduitLUhIkJ1f98TVqwIcfwjPP6Jp55S1dIhdQGuCV9x5/XOaYVcmsLTuDvqJGjIBffoG2bfWCSnmjdm2vWxBRNMAr7/n9WuWuLNnZ0KaNM8dq0wZ++03XzCtv1KrldQsiigZ4FRx69oRbbtF54pI0a+bsuva4OHjySRm2r1VL18yrwKlb1+sWRBQN8Cp43HMPtGqlc8SH6trVneOefrqsme/VSy+sVGDoVFxAaYBXwSMmRofqDxUbW/UM+opo1Ahmz4Y77tC/u3JXTIzOwQeYBngVXNq2hQkTdL1sEb8funRx9xxRUXD77fDNNxLwdc28ckNsrGbRB5gGeBV8rr8eunfXuWGAnBzZnCcQevWSNfNnnKFD9sp50dG60UyAaYBXwccYePddHTIu0rRp4M5Vs6Yk3z39tAR5zYdQTomK0h58gGmAV8GpSRN4+WXtSR59dOCDrDEwcqSsmW/TRi+0lDOM0R58gGmAV8Hrootkd7RInhMubQ/4QGjbVtbMX3mlXmip6rNWe/ABpgFeBbeXX47cDwWfT+oDeCk+Hp56CiZP1jXzqnoKCrQHH2Aa4FVwq11bgkskDhPHxgYuwa48gwfD0qVywaG9eVUVeXka4ANMA7wKfgMGwBVXRF6Qz8x0pga9Uxo3hu+/lyV1kfZcqOrLy4vc0TiPaIBXoeGRR6BBA69bEVhJSVCnjtetOFhUlBTFmTVL18yryiko0AvDANMAr0JDQgJMmRJZHxDt23vdgtIde6yUuR00SIfsVcXEx+uyywDTAK9CR7du8K9/RU5AOfZYr1tQtlq15KLrySd1zbwqX6S8b4OIBngVWm6/XdZmR4X5SzcpSS5ogp0xsozu55+Dd818bKz8PZW3NMAHXJh/SqqwEx0tldYSErxuibuMCZ4M+opo107WzI8c6X2QT0qSNtSoIQma//63LPXr1k2CTLhfHAYrvcgKOF3UqkJP69YwcSKMHQvp6V63xh2ZmXDMMV63onLi4+GZZ6SW/fDhkJEhmdNuio2VYJ6ZCS1aQL9+EtSPO+7wrYdHjIBFi2Qzo48/lvuystxtnzpAl8gFnAZ4FZquvhree0+2Os3N9bo1zmvQIHRHKc48UxLwzj8fFi929iIsKUmysaOjpcrfKadA376QklKxHQiTk2HSJNi+HZ59Fh57TF4/aWnOtVGVrFYtr1sQcXSsSoUmY+Cdd8J3Xi+Y1r9XRePGMGcO/POfVR+yj42VXl9srIzaXHUVPPecXDTs3StL9W6/XXrtld1euH59uPtu2LoVXnxRpkM0UdBduhd8wGkPXoWuhg3h1Vfh0ktlODhcREfLEHOoi4qCu+6SXva558KePZCdXfrPV7d3XhWxsTB0qNwWLIDx4+HTT+V7OnzvrLp1vW5BxNEAr0LbeefB2WfLcq2ygkcoSUyErl29boVzeveWfeYvvxy+/FIuxorPnTdvfvDc+VFHedOTTkmBDz6ALVtku9wnn4T8fB2+r66YGLkNHOh1SyKOsdZ63YZypaSk2AULFnjdDBWsUlNlida2bV63xBmJiQe2ag0n1sIrr8gc+IAB0jvv2dO93nl15eTIPgjjxsGGDXJhEgKfl0HF54NTT5Wk2FatvG5NyDHGLLTWplT58RrgVViYPRtOO016hKEuNlZ+j+hor1uiivzwA/z3v/DFFxLkw2W0yC2JibJ08rnnvN8RMYRVN8Brkp0KDyecAKNHh0fSXcuWGtyDTe/eMHUqrFoFN90ka+x1XffhEhOhWTN4+23JadDg7ikN8Cp8TJgg2duhLjnZ6xao0jRtKol427bJBkitWwfvFEMgJSTIiofx42H1ajjnHF2REAQ0wKvwER8f+hvSxMUFfw16JQFt9GhYuRI+/xxOP11ef3FxXrcssIqSJW+4QfIUxoyR+1RQ0ACvwkvnzrK+OVR7VT4fdOnidStURRkDxx8PM2bAihUS4JKSQvf1V1HGyGv1/PPl937wQa1UF4Q0wKvwc+utUuY1FOexs7NDv8hNpGreXIbtt26FRx+VXIpwnKdPTJTljPPmwbvvypy7Ckoa4FX4iY6W9cyhWOo1OhoaNfK6Fao6/H4Zvl+9GqZNg5NPltdiqA9dJybKUrcPP5QqheFUqyFMVSvAG2MuMsb8YYwpMMakHPK9fxljVhljlhtjTit2/6DC+1YZY26rzvmVKlWLFlKoJNSGStu21eSkcGGMrPefOROWLIFrrpHgH2qvSZ8P6tSRuv0rV8pyVBUSqtuD/x04H/iu+J3GmA7AUKAjMAh4xhgTbYyJBp4GTgc6AJcU/qxSzhs5Evr3lw+oWrVCI/kupcpLXlUwa9VKLji3bpX56iOPDP7h+7g4uSC59VZJoBs1KjSnvSJYtUrVWmuXApjDexznAO9aa7OBNcaYVUCvwu+tstauLnzcu4U/u6Q67VCqRMbIEOm6ddLzWLVKelKLF8Off8qHbVycDJ3m5HhfJMfv1wAf7pKS4Lrr4G9/k7K948bBjz/Ktrpub61bUVFR8r4YMkSWnjZs6HWLVBW5VYu+KfBDsf9vLLwPYMMh9+uaIOWeqCjpPbVqJSUzi8vPh40bJfCvXHlw8N+8+UDwz80NzGY2MTGyCkCFP2NkM51TTpHX38MPwxtvyP1ebpzk90tRn6eekkRVFdLKDfDGmC+BkrJ+7rDWTnW+SfvPOxoYXfjfbGPM726dKwjUA3Z43QgXhebvV/EelTO/X2oq9OlT7cO4IDSfv4oJ598NKvv7ZWTA119Dh5CZOQ33569ddR5cboC31p5cheNuAo4s9v9mhfdRxv2HnvcF4AUAY8yC6tTjDXb6+4U2/f1CVzj/bqC/X6gzxlRrExa3lslNA4YaY+KNMa2ANsBPwHygjTGmlTEmDknEm+ZSG5RSSqmIVa05eGPMecCTQH3gU2PMImvtadbaP4wxk5HkuTzgemttfuFjxgD/A6KBV6y1f1TrN1BKKaXUYaqbRT8FmFLK98YB40q4fwYwo5KneqHyrQsp+vuFNv39Qlc4/26gv1+oq9bvFxL7wSullFKqcrRUrVJKKRWGgi7AR1L5W2PMe8aYRYW3tcaYRYX3tzTGZBb73nMeN7VKjDH3GmM2Ffs9zij2vRKfy1BhjHnIGLPMGPObMWaKMaZ24f1h8dxB6L6vSmOMOdIYM8sYs6TwM+bGwvtLfZ2GmsLPkcWFv8eCwvvqGmNmGmNWFv5bx+t2VpYxpl2x52eRMSbVGHNTqD93xphXjDHbii8DL+35MuKJwvfjb8aY7uWewFobVDfgGGTt3zdASrH7OwC/AvFAK+BPJFEvuvDro4C4wp/p4PXvUYXf+xHg7sKvWwK/e90mB36ne4F/lHB/ic+l1+2t5O92KhBT+PUEYEKYPXdh8b465HdqDHQv/LoGsKLwtVji6zQUb8BaoN4h9z0I3Fb49W1Fr9VQvRW+NrcALUL9uQNOBLoX/8wo7fkCzgA+AwzQG/ixvOMHXQ/eWrvUWru8hG/tL39rrV0DFJW/7UVh+VtrbQ5QVP42ZBip9XsxMMnrtgRIac9lyLDWfmGtLaqE8wNS0yGchPz76lDW2s3W2p8Lv94HLOVAhc1wdg7weuHXrwPnetcUR5wE/GmtXed1Q6rLWvsdsOuQu0t7vs4B3rDiB6C2MaZxWccPugBfhqYcXua2aRn3h5ITgK3W2pXF7mtljPnFGPOtMeYErxrmgDGFw0mvFBsaDIfnrLgrkSvrIuHw3IXbc3QQY0xLoBvwY+FdJb1OQ5EFvjDGLDRSDRSgobV2c+HXW4BQLy4/lIM7Q+Hy3BUp7fmq9HvSkwBvjPnSGPN7CbeQ7iGUpIK/6yUc/ILdDDS31nYDxgLvGGNqBrLdFVXO7/cs0BpIRn6nR7xsa2VV5LkzxtyB1Hp4u/CukHnuIpUxJgn4ELjJWptKiL9OD3G8tbY7smPn9caYE4t/08pYb8gunTJSIO1s4P3Cu8LpuTtMdZ8vtzabKZP1qPytF8r7XY0xMciWuz2KPSYbyC78eqEx5k+gLVCtsoVuqOhzaYx5EZhe+N+ynsugUYHnbiRwJnBS4RsxpJ67coTEc1RZxphYJLi/ba39CMBau7XY94u/TkOOtXZT4b/bjDFTkKmWrcaYxtbazYVDuts8bWT1nA78XPSchdNzV0xpz1el35OhNEQfruVvTwaWWWs3Ft1hjKlvjIku/Poo5Hdd7VH7quyQ+aHzgKJM0dKey5BhjBkE/B9wtrU2o9j9YfHcEfrvq8MU5rq8DCy11j5a7P7SXqchxRiTaIypUfQ1kgj6O/K8jSj8sRGAa5uEBcBBo53h8twdorTnaxpweWE2fW9gb7Gh/BJ50oMvi4m88reHzieBZFbeZ4zJBQqAa621hyZihIIHjTHJyBDTWuAagLKeyxDyFLIKYKbEDX6w1l5LmDx31tq8EH9flaQvcBmw2BQuSQVuBy4p6XUaghoCUwpfjzHAO9baz40x84HJxpirgHVIQm/IKbxoOYWDn58SP2NChTFmEtAfqGeM2QjcA4yn5OdrBpJJvwrIAK4o9/iFI4tKKaWUCiOhNESvlFJKqQrSAK+UUkqFIQ3wSimlVBjSAK+UUkqFIQ3wSimlVBjSAK+UUkqFIQ3wSimlVBjSAK+UUkqFof8HuX+O2bUbv5wAAAAASUVORK5CYII=\n"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def plot_situation(\n",
" obstacles: list[Polygon],\n",
" destination: Point,\n",
" obstacle_color: Optional[str] = None,\n",
" legend: bool = True,\n",
") -> None:\n",
" \"\"\"PLots the obstacles into a matplotlib plot.\n",
"\n",
" Args:\n",
" obstacles: A list of obstacles.\n",
" destination: The destination that should be reached by the boat.\n",
" obstacle_color: The color the obstacles should have. Can be None.\n",
" If none all obstacles will have different colors.\n",
" legend: If true plots a legend.\n",
" Returns:\n",
" None\n",
" \"\"\"\n",
" plt.figure(figsize=(8, 8))\n",
" plt.axis([-SIZE_ROUTE, SIZE_ROUTE, -SIZE_ROUTE, SIZE_ROUTE])\n",
" plt.title(\"Test\")\n",
" for polygon in obstacles:\n",
" if obstacle_color is not None:\n",
" plt.fill(*polygon.exterior.xy, color=obstacle_color, label=\"Obstacle\")\n",
" else:\n",
" plt.fill(*polygon.exterior.xy)\n",
" plt.scatter(*destination.xy, marker=\"X\", color=\"green\", label=\"Destination\")\n",
" plt.scatter(0, 0, marker=\"o\", color=\"green\", label=\"Start\")\n",
" # https://www.geeksforgeeks.org/matplotlib-pyplot-arrow-in-python/\n",
" plt.arrow(\n",
" 0,\n",
" int(SIZE_ROUTE * 0.9),\n",
" 0,\n",
" -int(SIZE_ROUTE * 0.1),\n",
" head_width=10,\n",
" width=4,\n",
" label=\"Wind (3Bft)\",\n",
" )\n",
" if legend:\n",
" # https://stackoverflow.com/questions/13588920/stop-matplotlib-repeating-labels-in-legend\n",
" handles, labels = plt.gca().get_legend_handles_labels()\n",
" by_label = dict(zip(labels, handles))\n",
" plt.legend(by_label.values(), by_label.keys())\n",
" plt.show()\n",
"\n",
"\n",
"for s in range(5):\n",
" o = generate_obstacles(s)\n",
" plot_situation(o, generate_destination(o, s), \"RED\")"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 83,
"outputs": [
{
"data": {
"text/plain": "<PIL.Image.Image image mode=RGB size=200x200>",
"image/png": "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\n"
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def generate_image_from_map(\n",
" obstacles: list[Polygon], destination: Point, solution: list[Point]\n",
") -> Image:\n",
" \"\"\"Generate an image from the map.\n",
"\n",
" Can be used to feed an ANN.\n",
" - Obstacles are marked as reed.\n",
" - The destination is marked as green.\n",
" - The points where the route will likely change are blue.\n",
"\n",
" Args:\n",
" obstacles: A list of obstacles as shapely Polygons.\n",
" destination: A destination that should be navigated to.\n",
" \"\"\"\n",
" img = Image.new(\n",
" \"RGB\",\n",
" (SIZE_ROUTE * 2, SIZE_ROUTE * 2),\n",
" \"#ffffff\",\n",
" )\n",
" draw = ImageDraw.Draw(img)\n",
" for polygon in obstacles:\n",
" draw.polygon(\n",
" list(np.dstack(polygon.exterior.xy).reshape((-1)) + SIZE_ROUTE),\n",
" fill=\"#FF0000\",\n",
" outline=\"#FF0000\",\n",
" )\n",
" img.putpixel((int(destination.x) + 100, int(destination.y) + 100), (0, 0xFF, 0))\n",
" return img\n",
"\n",
"\n",
"o = generate_obstacles(42)\n",
"g = generate_destination(o, 42)\n",
"og_img = generate_image_from_map(o, g, None)\n",
"og_img"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 84,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.39 ms ± 209 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"def generate_all_to_series(\n",
" seed: Optional[int] = None, image: bool = False\n",
") -> pd.Series:\n",
" \"\"\"Generates everything and aggregates all data into a `pd:Series`.\n",
"\n",
" Args:\n",
" seed:The seed that should be used to generate map and destination.\n",
" image: If an image should be generated or if that should be postponed to save memory.\n",
" Returns:\n",
" Contains a `pd.Series`containing the following.\n",
" - The seed tha generated the map.\n",
" - The destination in x\n",
" - The destination in y\n",
" - A list of Obstacle polygons.\n",
" - The route generated for this map by the roBOOTer navigation system.\n",
" - Optionally the image containing all the information. Can be generated at a later date without the fear for a loss of accuracy.\n",
" \"\"\"\n",
" obstacles = generate_obstacles(seed)\n",
" destination = generate_destination(obstacles, seed)\n",
"\n",
" return pd.Series(\n",
" data={\n",
" \"seed\": str(seed),\n",
" \"obstacles\": obstacles,\n",
" \"destination_x\": destination.x,\n",
" \"destination_y\": destination.y,\n",
" \"image\": generate_image_from_map(obstacles, destination, None)\n",
" if image\n",
" else pd.NA,\n",
" \"route\": None,\n",
" }\n",
" )\n",
"\n",
"\n",
"%timeit generate_all_to_series()"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 85,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.22 s ± 197 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
},
{
"data": {
"text/plain": " obstacles destination_x \\\nseed \n0 [POLYGON ((-17.62168766659423 -98.369266220295... -66.0 \n1 [POLYGON ((-97.82715137072381 -82.221167700698... -38.0 \n2 [POLYGON ((-46.23706006792075 -76.756994820638... 73.0 \n3 [POLYGON ((-7.4210414351932155 -83.11109606339... 31.0 \n4 [POLYGON ((-77.97638439917915 -70.239097296919... 47.0 \n... ... ... \n995 [POLYGON ((-58.363834699771374 -78.10250502922... 53.0 \n996 [POLYGON ((-45.38105849246964 -86.568664678920... -66.0 \n997 [POLYGON ((-82.90222667221566 -92.864813148784... 41.0 \n998 [POLYGON ((-37.1039416590347 -88.1790371955828... -45.0 \n999 [POLYGON ((-119.59353826736813 -30.54685916387... 74.0 \n\n destination_y image route \nseed \n0 -54.0 <NA> None \n1 65.0 <NA> None \n2 49.0 <NA> None \n3 56.0 <NA> None \n4 54.0 <NA> None \n... ... ... ... \n995 52.0 <NA> None \n996 73.0 <NA> None \n997 51.0 <NA> None \n998 64.0 <NA> None \n999 -25.0 <NA> None \n\n[1000 rows x 5 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>obstacles</th>\n <th>destination_x</th>\n <th>destination_y</th>\n <th>image</th>\n <th>route</th>\n </tr>\n <tr>\n <th>seed</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>[POLYGON ((-17.62168766659423 -98.369266220295...</td>\n <td>-66.0</td>\n <td>-54.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>1</th>\n <td>[POLYGON ((-97.82715137072381 -82.221167700698...</td>\n <td>-38.0</td>\n <td>65.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>2</th>\n <td>[POLYGON ((-46.23706006792075 -76.756994820638...</td>\n <td>73.0</td>\n <td>49.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>3</th>\n <td>[POLYGON ((-7.4210414351932155 -83.11109606339...</td>\n <td>31.0</td>\n <td>56.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>4</th>\n <td>[POLYGON ((-77.97638439917915 -70.239097296919...</td>\n <td>47.0</td>\n <td>54.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>995</th>\n <td>[POLYGON ((-58.363834699771374 -78.10250502922...</td>\n <td>53.0</td>\n <td>52.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>996</th>\n <td>[POLYGON ((-45.38105849246964 -86.568664678920...</td>\n <td>-66.0</td>\n <td>73.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>997</th>\n <td>[POLYGON ((-82.90222667221566 -92.864813148784...</td>\n <td>41.0</td>\n <td>51.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>998</th>\n <td>[POLYGON ((-37.1039416590347 -88.1790371955828...</td>\n <td>-45.0</td>\n <td>64.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>999</th>\n <td>[POLYGON ((-119.59353826736813 -30.54685916387...</td>\n <td>74.0</td>\n <td>-25.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n </tbody>\n</table>\n<p>1000 rows × 5 columns</p>\n</div>"
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%timeit pd.DataFrame([generate_all_to_series(i) for i in range(1000)]).set_index(\"seed\")\n",
"df = pd.DataFrame([generate_all_to_series(i) for i in range(1000)]).set_index(\"seed\")\n",
"df.to_pickle(\"test.pickle\")\n",
"df"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 86,
"outputs": [
{
"data": {
"text/plain": " obstacles destination_x \\\nseed \n0 [POLYGON ((-17.62168766659423 -98.369266220295... -66.0 \n1 [POLYGON ((-97.82715137072381 -82.221167700698... -38.0 \n2 [POLYGON ((-46.23706006792075 -76.756994820638... 73.0 \n3 [POLYGON ((-7.4210414351932155 -83.11109606339... 31.0 \n4 [POLYGON ((-77.97638439917915 -70.239097296919... 47.0 \n... ... ... \n995 [POLYGON ((-58.363834699771374 -78.10250502922... 53.0 \n996 [POLYGON ((-45.38105849246964 -86.568664678920... -66.0 \n997 [POLYGON ((-82.90222667221566 -92.864813148784... 41.0 \n998 [POLYGON ((-37.1039416590347 -88.1790371955828... -45.0 \n999 [POLYGON ((-119.59353826736813 -30.54685916387... 74.0 \n\n destination_y image route \nseed \n0 -54.0 <NA> None \n1 65.0 <NA> None \n2 49.0 <NA> None \n3 56.0 <NA> None \n4 54.0 <NA> None \n... ... ... ... \n995 52.0 <NA> None \n996 73.0 <NA> None \n997 51.0 <NA> None \n998 64.0 <NA> None \n999 -25.0 <NA> None \n\n[1000 rows x 5 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>obstacles</th>\n <th>destination_x</th>\n <th>destination_y</th>\n <th>image</th>\n <th>route</th>\n </tr>\n <tr>\n <th>seed</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>[POLYGON ((-17.62168766659423 -98.369266220295...</td>\n <td>-66.0</td>\n <td>-54.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>1</th>\n <td>[POLYGON ((-97.82715137072381 -82.221167700698...</td>\n <td>-38.0</td>\n <td>65.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>2</th>\n <td>[POLYGON ((-46.23706006792075 -76.756994820638...</td>\n <td>73.0</td>\n <td>49.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>3</th>\n <td>[POLYGON ((-7.4210414351932155 -83.11109606339...</td>\n <td>31.0</td>\n <td>56.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>4</th>\n <td>[POLYGON ((-77.97638439917915 -70.239097296919...</td>\n <td>47.0</td>\n <td>54.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>995</th>\n <td>[POLYGON ((-58.363834699771374 -78.10250502922...</td>\n <td>53.0</td>\n <td>52.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>996</th>\n <td>[POLYGON ((-45.38105849246964 -86.568664678920...</td>\n <td>-66.0</td>\n <td>73.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>997</th>\n <td>[POLYGON ((-82.90222667221566 -92.864813148784...</td>\n <td>41.0</td>\n <td>51.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>998</th>\n <td>[POLYGON ((-37.1039416590347 -88.1790371955828...</td>\n <td>-45.0</td>\n <td>64.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>999</th>\n <td>[POLYGON ((-119.59353826736813 -30.54685916387...</td>\n <td>74.0</td>\n <td>-25.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n </tbody>\n</table>\n<p>1000 rows × 5 columns</p>\n</div>"
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pd.read_pickle(\"test.pickle\")\n",
"df2"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"https://programtalk.com/python-examples/PIL.ImageDraw.Draw.polygon/)\n",
"https://stackoverflow.com/questions/3654289/scipy-create-2d-polygon-mask"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"Ich würde auch zu 1. tendieren, stimme Ihnen aber zu, dass das Thema sehr umfangreich ist. Könnte man sich nicht einen Teilbereich herauspicken? Ich verstehe nicht viel vom Segeln, daher lassen Sie mich kurz zusammenfassen, was Sie vorhaben: - Sie generieren Trainingsdaten mit dem existierenden aber langsamen GD Algorithmus. Ich nehme an, es handelt sich um lokale Routen in einem relativ kleinen Kartenausschnitt. Lässt es die Laufzeit zu, dass Sie eine große Menge an Routen berechnen. - Sie haben dann eine Karte und als Ausgabe eine Liste der Wendepunkte - Warum wollen Sie daraus eine Heatmap berechnen? Diesen Schritt habe ich noch nicht verstanden - Wenn Sie aus einer Karte eine Heatmap trainieren wollen und dafür genügend Beispiele haben, könnnten GANs hilfreich sein: https://arxiv.org/abs/1611.07004 Ich würde Ihnen raten, das Problem möglichst so zu reduzieren, dass es im Rahmen des Moduls noch handhabbar bleibt. Alles Weitere kann man sich auch für spätere Arbeiten aufbewahren. Das 2. Thema ist auch ok. Aber vielleicht nicht ganz so spannend. Ich überlasse Ihnen die Entscheidung. Freundliche Grüße Heiner Giefers"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
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"name": "python3"
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