{ "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Initialschätzung von Kurswechselpositionen eines Segelboots auf einer Karte anhang con Wind, Start und Zielpunkt\n", "\n", "## Motivation\n", "\n", "Ziel dieser Semester abschließenden schriftlichen Ausarbeitung im Fach \"Maschine Learning\" an der Fachhochschule Südwestfalen ist das Generieren einer Heatmap von Kurswechselpositionen eines Segelbootes zu einer Karte abhängig von Wind und der Zielpostion. Dies soll das Finden einer guten Route vereinfachen, indem die Qualität einer ersten Route, die danach über ein Quotientenabstiegsverfahren optimiert werden soll verbessern. Da ein solches Quotientenabstiegsverfahren sehr gerne in einem Lokalen minimum festhängt, müssen mehrere routen gefunden und optimiert werden. Hier soll untersucht werden, ob dies durch eine Ersteinschätzung der Lage durch KI verbessert werden kann.\n", "\n", "Eingesetzt werden soll die so erstellte KI in dem Segelroboter des [Sailing Team Darmstadt e.V.](https://www.st-darmstadt.de/) Einer Hochschulgruppe an der TU-Darmstadt welche den [\"roBOOTer\"](https://www.st-darmstadt.de/ueber-uns/boote/prototyp-ii/) ein vollautonomes Segelboot welches eines Tages den Atlantik überqueren soll. [Eine technische Herausforderung welche zuerst von einem norwegischen Team erfolgreich abgeschlossen wurde](https://www.microtransat.org/)." ] }, { "cell_type": "markdown", "source": [ "## Inhaltsverzeichnis\n", "\n", "1. Einleitung\n", "\n", " 1.1. Situation\n", " \n", " 1.2. Vorgehen zur unterstützenden KI\n", " 1.2.1. Eingaben und Ausgeben\n", "2. Vorbereitungen\n", "\n", " 2.1. Imports\n", " \n", " 2.2. Parameter und Settings\n", " \n", "3. Szenarien und Routen Generieren\n", "\n", " 3.1. Generieren von Karten\n", " 3.2.1 Paremter zum Generieren der Karte\n", " \n", " 3.2. Generieren des Zieles\n", " \n", " 3.3. Das Normieren eines Scenarios\n", " \n", " 3.4. Massengenerierung von Scenarios\n", " \n", " 3.5. Daten Zusammenfassen\n", "\n", "4. Sencarios Filtern\n", "\n", " 4.1. Die Route verlässt die Karte\n", " \n", " 4.2. Routen auf Fehler überprüfen\n", " \n", " 4.3. Filter der Routen nach Kosten\n", " \n", " 4.4. Filter der Routen nach Komplexität\n", " \n", "5. Das konvertieren in trainierbare Daten\n", "\n", "6. Das KI Model erstellen\n", " \n", " 6.1. Der Generator\n", " \n", " \n", "\n", "8. Analyse der KI\n", " Model Erstellung\n", " Model Training\n", " Training des Modells mit Routen als Linien\n", " Betrachtung des trainierten Models mit Routen als Linien\n", " Training mit routen als liste von Wendepunkten\n", " Betrachtung des trainierten Models mit Routen als Liste von Kurswechselpositionen.\n", "\n", "9. Ausblick und Reflektion\n", " \n", "10. Literaturverzeichnis\n", "11. Eigentständigkeitserklärung" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "## Einleitung\n", "\n", "### Situation\n", "\n", "Eine Routenplanung für ein Segelboot hat ein Problem, welches man sonst so eher nicht kennt. Eine relativ freie Fläche auf der Sich das Schiff bewegen kann. Dies verändert die Wegfindung wie man sie von der Straße kennt fundamental.\n", "\n", "Navigiert man auf Straßen, hat man zumindest nach einer ersten abstraction relativ wenige Freiheitsgrade für den Weg.\n", "Die Richtung kann nur an Kreuzungen gewechselt werden und dort nur in Richtungen in die es Straßen gibt. Beim Segeln auf dem freien Meer ist jeder Ort ein potenzieller Wendepunkt von dem aus Potenziell in jede Richtung gesegelt werden kann.\n", "\n", "Dennoch ist es oft auch ohne Hindernisse zwischen Boot und Ziel oft nicht möglich das Ziel direkt anzufahren das sich die Maximalgeschwindigkeiten relativ zur Windrichtung verändern.\n", "Das folgende Diagramm zeigt die Segelgeschwindigkeiten an einem Katamaran.\n", "\n", "\"Ship\n", "\n", "Da der roBOOTer anders als an Katamaran nicht auf Geschwindigkeit, sondern auf mechanische Belastbarkeit ausgelegt wurde hat der Fahrtwind einen geringeren einfluss auf das Fahrtverhalten des Segelboots dies und eine andere Maximalgeschwindigkeit sorgen für ein etwas anderes Fahrverhalten. Die ungefähre Form der Kurven trifft aber auch auf den roBOOTer zu. Man kann deutlich erkennen das auch, wenn man nicht direkt gegen den Wind fahren kann man schräg gegen den wind immer noch erstaunlich schnell ist.\n", "\n", "Das aktuelle Verfahren zum Finden einer Route läuft folgendermaßen ab:\n", "\n", "Eine direkte Route wird berechnet. Die Route wird an jedem Hindernisse geteilt und rechts und links um jedes hindernis herum gelegt. Bei folgenden hindernissen werden die Routen wieder geteilt somit erhält man $2^n$ Vorschläge für Routen, wobei $n$ die Anzahl der Hindernisse auf der Route ist. Jeder Abschnitt der Route wird noch einmal zerteilt, um der Route mehr Flexibilität zu geben.\n", "\n", "Die Routen werden dann simuliert, um die Kosten der Route zu berechnen. Die so simulierte Route wird danach über die Kosten in einem Gradientenabstiegsverfahren optimiert.\n", "\n", "Das ganze oben beschriebene Verfahren ist relativ schnell sehr rechenaufwendig und findet nicht immer ein Ergebnis. Wird kein Ergebnis gefunden wird eine mehr oder weniger zufällige Route optimiert.\n", "\n", "Diese Ausarbeitung soll wenigstens bei der alternativen Routenfindung helfen. Im idealfall kann es aber auch genutzt werden, um die auswahl der Routen um Hindernisse frühzeitig zu reduzieren und den Rechenaufwand unter $2^n$ zu senken wobei $n$ die Anzahl von Hindernissen auf der Route ist.\n", "\n", "### Vorgehen zur unterstützenden KI\n", "\n", "#### Eingaben und Ausgeben\n", "\n", "Die Algorithm zur Wegfindung vom Sailing Team Darmstadt e.V. arbeiten intern mit Polygonen als Hindernissen. Diese werden durch die Shapely Bibliothek implementiert. Da eine variable Anzahl an Polygonen mit einer variablen Form und Position eine Relative komplexer Input muss dieser in eine normierte Form gebracht werden. Ein binärfärbens Bild ist dafür die einfachste Form.\n", "\n", "Für den Computer spielen sowohl Zentrierung, Skalierung und Ausrichtung der Karte keine Rolle.\n", "Wir rotieren also die Karte immer so das der Wind von *Norden* kommt und das Boot / die Startposition in der *Mitte* der Karte liegt. Da distanz Liner ist, wird davon ausgegangen das Scenario einfach skaliert passend skaliert werden kann.\n", "\n", "Die nächste eingabe ist die Zielposition relativ zum Startpunkt. Diese kann entweder durch ein einzelnes Pixel in einem zweiten Farbkanal oder aber in abstrakterer Form an die KI übergeben werden.\n", "\n", "Als ausgabe wird eine Heatmap erwartet. Zwei alternative Heatmaps sind relative einfach denkbar.\n", "\n", "1. Eine Headmap der Kurswechselpositionen\n", "2. Eine Headmap des Kursverlaufes\n", "\n", "Headmaps sind in gewisser Weise Bilder. Das Problem wird daher wie ein Bild zu Bild KI Problem betrachtet. Diese werden normalerweise durch ANNs gelöst.\n", "\n", "Um eine ANN zu trenntieren gibt es immer die Wahl zwischen drei Primären prinzipien. Dem unüberwachten Lernen, dem reinforcement Learning und dem überwachten Lernen. Letzteres ist dabei meist am einfachsten wenn auch nicht immer möglich.\n", "\n", "Der Wegfindealgorithmus des Sailing Team Darmstadt e.V. ist zwar noch in der Entwicklung, funktioniert aber hinreichend gut, um auf einem normalen PC Scenarios mit Routen zu paaren oder auch diese zu *labeln*, um beim KI lingo zu bleiben. Um anpassungsfähig an andere Scenarios zu sein wird eine große Menge unterschiedlicher Scenarios und Routen benötigt.\n", "Da das Haupteinsatzgebiet das Meer ist gehen wir von einer Insellandschaft oder Küstenlandschaft aus.\n", "\n", "Zum Finden von Scenarios gibt es zwei Möglichkeiten.\n", "\n", "1. Das Auswählen von umgebungen von der Weltkarte und das Bestimmen eines Zielpunktes.\n", "2. Das Generieren von künstlichen Scenarios.\n", " \n", "Hier wird die Annahme getroffen das sich ANNs von einem Datensatz auf dem anderen Übertragen lassen.\n", "Der Aufwand für künstliche Scenarios wird hierbei als geringer eingestuft und daher gewählt." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "## Vorbereitungen\n", "\n", "Folgende Python Bibliotheken werden verwendet:\n", "\n", "1. `tensorflow`\\\n", " Die `tensorflow` Bibliothek ist das Werkzeug welches verwendet wurde, um neuronale Netz zu modellieren, zu trainieren, zu analysieren und auszuführen. Tensorflow wird mit den kürzel `tf` abgekürzt.\n", "\n", "2. `pyrate`\\\n", " Die `Pyrate` Bibliothek ist Teil des ROS Operating Systems, welches den roBOOTer betreibt. Kann Routen zu Scenarios finden.\n", "\n", "3. `Shapley`\\\n", " Die `shapley` Bibliothek wird genutzt, um geometrische Körper zu generieren, zu mergen und an den Roboter zum Labeln weiterzugeben.\n", "\n", "4. `pandas`\\\n", " Die `pandas` Bibliothek verwaltet, speichert und analysiert daten. `pandas` wird üblicherweise mit `pd` abgekürzt.\n", "\n", "5. `numpy`\\\n", " Eine Bibliothek um Mathematische operations an multidimensionalen Arrays auszuführen. `numpy`wir üblicherweise mit `np` abgekürzt.\n", "\n", "6. `matplotlib`\\\n", " Wird genutzt um Diagramme zu plotted. Das modul `pyplot` wird hier vermehrt genutzt und mit dem kürzel `plt` abgekürzt.\n", "\n", "6. `PIL`\\\n", " Eine Library um Bilder manuell zu zeichnen.\n", "\n", "7. `humanize`\\\n", " Konvertiert Zahlen, Daten und Zeitabstände in ein für menschen einfach leserliches Format.\n", "\n", "8. `tqdm`\\\n", " Fügt einen Fortschrittsbalken zu vielen Problemen hinzu.\n", "\n", "9. `black`\\\n", " Der `black` code Formatier wurde genutzt um den Code in diesem Notebook zu Formatieren." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "#### Imports\n", "Importiert die Imports the necessary packages from python and pypi." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "import sys\n", "\n", "# Pins the python version executing the Jupyter Notebook\n", "assert sys.version_info.major == 3\n", "assert sys.version_info.minor == 10\n", "\n", "import os\n", "from typing import Optional, Final, Literal\n", "import glob\n", "import pickle\n", "\n", "from tqdm.notebook import tqdm\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, LineString\n", "from shapely.ops import unary_union\n", "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\" \n", "import tensorflow as tf\n", "import humanize" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Definiert den Pfad an dem das Jupyter Notebook ausgeführt werden soll.\n", "Importiert die pyrate module. Wird nur ausgeführt, wenn innerhalb des Pyrate Containers ausgeführt." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Import route generation if started in the docker container\n", "if os.getenv(\"PYRATE\"):\n", " %cd /pyrate/\n", " import experiments\n", " from pyrate.plan.nearplanner.timing_frame import TimingFrame\n", "\n", "# Protection against multi execution\n", "if not os.path.exists(\"experiments\"):\n", " %cd ../" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "if os.getenv(\"PYRATE\"):\n", " # Sets the maximum number of optimization steps that can be performed to find a route.\n", " # Significantly lowered for more speed.\n", " experiments.optimization_param.n_iter_grad = 50\n", "\n", " # Disables verbose outputs from the pyrate library.\n", " experiments.optimization_param.verbose = False" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Activate pandas for tqdm\n", "tqdm.pandas()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "#### Parameter und Settings\n", "\n", "In der nachfolgenden Sektion werden verschiedene Parameter gesetzt. Zum Beispiel die Skala auf der Routen generiert werden, das äußere Limit für mögliche Ziele und die Minimaldiestanz von Zielen zum Startpunkt." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# The scale the route should lie in. Only a mathematical limit.\n", "SIZE_ROUTE: Final[int] = 100\n", "\n", "# The outer limit in with the goal need to be placed.\n", "# Should be smaller than\n", "SIZE_INNER: Final[int] = 75\n", "assert SIZE_ROUTE > SIZE_INNER, \"The goal should be well inside the limit placed \"\n", "\n", "# The minimum distance from the start that should\n", "MIN_DESTINATION_DISTANCE: Final[int] = 25\n", "assert (\n", " SIZE_INNER > MIN_DESTINATION_DISTANCE\n", "), \"The goal should be well closer to the outer limit the\"\n", "\n", "# The size the ANN input has. Equal to the image size. Should be an element of $n^2$ to be easier compatible with ANNs.\n", "IMG_SIZE: Final[int] = 128\n", "\n", "# The size an image should be in to be easily visible by eye.\n", "IMG_SHOW_SIZE: Final[int] = 400\n", "\n", "# The number of Files that should be read to train the ANNs\n", "NUMBER_OF_FILES_LIMIT: Final[int] = 500\n", "\n", "#\n", "NO_SHOW = False\n", "GENERATE_NEW = True\n", "\n", "# The path of all the collected files\n", "DATA_COLLECTION_PATH: Final[str] = \"data/collected.pickle\"\n", " \n", "# The \n", "BATCH_SIZE: Final[int] = 32\n", " \n", "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "## Szenarien und Routen Generieren\n", "\n", "Um das neuronale Netz zu trainieren werden Datensätze benötigt. Für die Abschätzung der Routen wird eine Karte mit Hindernissen und eine zugehörige Route benötigt. Hier wurde die Designentscheidung getroffen die Karten nicht auszuwählen, sondern zu generieren.\n", "\n", "### Generieren von Karten\n", "\n", "Eine Karte ist für das Sailing Team Darmstadt eine Mange von statischen und dynamischen Hindernissen. Statische Hindernisse sind Inseln, Landmassen und Untiefen und Fahrverbotszonen. Dynamische Hindernisse sind andere Teilnehmer am Schiffsverkehr und Wetterereignisse.\n", "In dieser KI wird sich auf statische Hindernisse beschränkt. Daher ist eine Scenario eine Mange an Hindernispolygonen.\n", "Um das Generieren der Polygone einfacher zu regeln und größere statistische Kontrolle über die den Generationsvorgang zu haben sind alle generierten Basispolygone als Abschnitte auf einem Umkreis definiert die Zufällig über die Karte verteilt werden.\n", "\n", "Ein einzelnes Polygon wird hier folgendermaßen generiert:\n", "1. Die Anzahl der Kanten/Ecken wird festgelegt.\n", "2. Ein lognormal verteilter Radius wird zufällig ausgewählt.\n", "3. Auf dem Radius werden n winkel abgetragen.\n", "4. Die Winkel werden sortiert damit sich das Polygon nicht selbst schneidend.\n", "5. Die durch Radius und Winkel entstehenden Punkte werden in das kartesische Koordinatensystem umgewandelt.\n", "6. Der zufällige Offset / Polygon mittelpunkt wird aufaddiert.\n", "7. Aus den so generierten `np.ndarray` wird ein `shapely.geometry.Polygon` erstellt.\n", "8. Polygonen die den Mittelpunkt berühren oder einschließen werden ersatzlos gelöscht.\n", "\n", "So wird eine festgelegte Anzahl von Polygonen generiert.\n", "Setzt man vor dem Generieren des ersten Polygons eines Scenarios eine random seed über `np.random.seed` so erhält man zu jedem seed ein eindeutiges mange an Polygonen wenn auch alle anderen Parameter übereinstimmen. Diese Polygon-mange hat nun mit hoher Wahrscheinlichkeit überlappende Polygone. Dies ist für den Algorithmus des Sailing Teams Darmstadt e.V. ein Problem. Die Shapley Bibliothek besitzt eine Union function die Vereinigungsmengen von Polygonen bildet wenn möglich. So erhält man eine reduzierte mange an Polygonen. Diese kann später an einen Solver übergeben werden." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "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", " perigons: np.ndarray,\n", "):\n", " \"\"\"Transforms polar coordinates into cartesian coordinates.\n", "\n", " Args:\n", " radii: A array of radii.\n", " perigons: A array of angles in perigons [0, 1[.\n", "\n", " Returns:\n", " An array of cartesian coordinates.\n", " \"\"\"\n", " return radii * np.exp(2j * perigons * np.pi)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "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 polygon.\n", " \"\"\"\n", " # define the number of corners\n", " number_of_corners = np.random.randint(3, 10)\n", "\n", " # generate cartesian coordinates from a radius and a sorted list of perigons.\n", " array = polar_to_cartesian(\n", " np.random.lognormal(radius_mean, radius_sigma),\n", " np.sort(np.random.rand(number_of_corners)),\n", " )\n", "\n", " # add an offset\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", "\n", " return_values[:] = offset\n", " return_values[:, :] += np.array((np.real(array), np.imag(array))).T\n", " return Polygon(return_values)\n", "\n", "\n", "np.random.seed(42)\n", "random_polygon()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Parameter zum Generieren der Karte\n", "\n", "Die folgenden Parameter wurden für das Generieren von Karten genutzt:\n", "* `radius_mean = 2` \n", "* `radius_sigma = 1`\n", "* `number_of_polygons = 40`" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def generate_obstacles(\n", " seed: Optional[int] = None,\n", " number_of_polygons: int = 40,\n", " radius_mean: float = 2,\n", " radius_sigma: float = 1,\n", ") -> dict[str, Polygon]:\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", " # sets a seed\n", " if seed is not None:\n", " np.random.seed(seed)\n", "\n", " # generate a list of polygons\n", " polygons = []\n", " for _ in range(number_of_polygons):\n", " poly = random_polygon(radius_mean, radius_sigma)\n", " # skip polygons that are to close to the start int point P(0, 0)\n", " if poly.contains(Point(0, 0)):\n", " continue\n", " if poly.exterior.distance(Point(0, 0)) < 1:\n", " continue\n", " # append to polygon list\n", " polygons.append(poly)\n", "\n", " # build unions of all polygons\n", " polygon_list = list(unary_union(polygons).geoms)\n", " return {str(i): p for i, p in enumerate(polygon_list)}" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Generieren des Zieles\n", "\n", "Zu jedem Scenario gehört neben einer Situation auch ein Ziel. Auch zum Generieren eines Ziels wurde zu erste der gleiche seed gesetzt wie für den Karten Generator. Danach wird eine zufällige Position mit Abstand zum Kartenrand ausgewählt.\n", "Die so generierte Zielposition wird danach auf Plausibilität überprüft. Folgende Prüfungen finden statt:\n", "1. Es wird sichergestellt dass, das Ziel nicht in oder an einem Hindernis liegt.\n", "1. Eine Minimaldistanz in x und y wird sichergestellt. Leider ist hier ein Fehler passiert. Anstelle die Summe der absoluten Distanz zu prüfen wurden die Distanzen für X und Y separat geprüft was verhindert, das Ziele über, unter und neben dem Startpunkt gefunden werden können. Zielpunkte werden nur in den äußeren vier Quadranten gefundene. Bedauerlicherweise ist dies erst aufgefallen als schon zu viel Zeit vergangen war und die Daten nicht neu generiert werden konnten. Dies sollte aber zumindest das Konzept dieser KI nicht beeinflussen. Wohl aber ihre direkte anwendbarkeit." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def generate_destination(\n", " obstacles: dict[str, Polygon],\n", " seed: Optional[int] = None,\n", ") -> 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.values())\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": "markdown", "source": [], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def plot_situation(\n", " obstacles: dict[str, Polygon],\n", " destination: Point,\n", " obstacle_color: str | None = \"RED\",\n", " route=None,\n", " legend: bool = True,\n", " title: str | None = None,\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", " route: The route that should be plotted.\n", " legend: If true plots a legend.\n", " title: The title of the plot.\n", " Returns:\n", " None\n", " \"\"\"\n", " # Create a plot in the defined size\n", " plt.axis([-SIZE_ROUTE, SIZE_ROUTE, -SIZE_ROUTE, SIZE_ROUTE])\n", "\n", " # Sets a title if one is demanded\n", " if title:\n", " plt.title(title)\n", "\n", " # Plots the obstacles.\n", " if obstacles:\n", " for polygon in obstacles.values():\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", "\n", " # Plots the wind direction\n", " # The following code for an arrow was taken modeled after:\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", "\n", " if route is not None:\n", " if isinstance(route, np.ndarray):\n", " plt.plot(route[:, 0], route[:, 1], color=\"BLUE\", marker=\".\")\n", " else:\n", " if isinstance(route, TimingFrame):\n", " plt.plot(\n", " route.points[:, 0], route.points[:, 1], color=\"BLUE\", marker=\".\"\n", " )\n", " else:\n", " raise TypeError()\n", "\n", " # Plots the estimation\n", " if destination:\n", " plt.scatter(*destination.xy, marker=\"X\", color=\"green\", label=\"Destination\")\n", " plt.scatter(0, 0, marker=\"o\", color=\"green\", label=\"Start\")\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", " return None" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Die aus den Seeds 0 - 11 generierten Karten werden unten angezeigt um Beispiele der von der KI zu Lösenden Scenario zu zeigen.\n", "Wird dieses Notebook im Pyrate Docker Container ausgeführt werden auch die Routen eingezeichnet." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "if not NO_SHOW:\n", " # create a subplot with 12 routes.\n", " plt.figure(figsize=(17.5, 25))\n", " for seed in tqdm(range(12)):\n", " plt.subplot(4, 3, seed + 1)\n", " generated_obstacles = generate_obstacles(seed)\n", " generated_destination = generate_destination(generated_obstacles, seed)\n", " route_generated = None\n", "\n", " # try to generate a route\n", " try:\n", " route_generated, _ = experiments.generate_route(\n", " position=Point(0, 0),\n", " goal=generated_destination,\n", " obstacles=generated_obstacles,\n", " wind=(18, 180),\n", " )\n", " except Exception:\n", " route_generated = None\n", "\n", " # plot the situation\n", " plot_situation(\n", " obstacles=generated_obstacles,\n", " destination=generated_destination,\n", " obstacle_color=\"RED\",\n", " route=route_generated,\n", " title=f\"Seed: {seed}, Cost: {route_generated.cost:.3f}\"\n", " if route_generated\n", " else f\"Seed: {seed}\",\n", " legend=(seed == 0),\n", " )\n", " plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Das Normieren der Scenarios\n", "\n", "Um für ein neuronales Netz Verständlich zu sein ist es immer einfacher, wenn ein Input normieren ist. Hier wurde sich entschieden die Scenarios, als Bilddaten zu normieren. 128 x 128 Pixel sind wesentlich gleichförmiger als eine Mange von maximal 40 Polygonen mit unterschiedlichen Formen. Daher verwandelt die folgende Funktion die mit den Oben definierten Funktionen genierten Scenarios Datensätze in eine Bildform. Rot ist dabei das Hindernis. Grün das Ziel und Blau die Route. Entweder als Linie oder als Punkt, wenn die Route sich ändert.\n", "Für diesen code wurde sich am folgenden Beispiel orientiert. https://programtalk.com/python-examples/PIL.ImageDraw.Draw.polygon/" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def generate_image_from_map(\n", " obstacles: dict[str, Polygon],\n", " destination: Point,\n", " route=None,\n", " route_type: Literal[\"line\", \"dot\"] = \"dot\",\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 dict of obstacles as shapely Polygons. Keyed as a string.\n", " destination: A destination that should be navigated to.\n", " route: The calculated route that should be followed.\n", " route_type: How the route is drawn. If 'line' is selected the complete route is selected.\n", " If 'dot' is selected the turning points a drawn in.\n", " \"\"\"\n", " # generate an empty image (All black)\n", " img = Image.new(\n", " \"RGB\",\n", " (IMG_SIZE, IMG_SIZE),\n", " \"#000000\",\n", " )\n", " draw = ImageDraw.Draw(img)\n", "\n", " # draw in all obstacles in red\n", " for polygon in obstacles.values():\n", " draw.polygon(\n", " list(\n", " (np.dstack(polygon.exterior.xy).reshape((-1)) + SIZE_ROUTE)\n", " / (2 * SIZE_ROUTE)\n", " * IMG_SIZE\n", " ),\n", " fill=\"#FF0000\",\n", " outline=\"#FF0000\",\n", " )\n", "\n", " # draw in a route if possible. Does so in blue\n", " if os.getenv(\"PYRATE\"):\n", " if isinstance(route, TimingFrame):\n", " route = route.points\n", " if route is not None:\n", " route = ((route + SIZE_ROUTE) / (2 * SIZE_ROUTE) * IMG_SIZE).astype(int)\n", " # draws the route as collection of lines\n", " if route_type == \"line\":\n", " draw.line([tuple(point) for point in route], fill=(0, 0, 0xFF))\n", " # draw the route as a collection of points. The starting point is seen as redundant and left out.\n", " elif route_type == \"dot\":\n", " for point in route[1:]:\n", " img.putpixel(point, (0, 0, 0xFF))\n", " else:\n", " raise ValueError(\"Route type unknown.\")\n", " # draws in the destination in green\n", " img.putpixel(\n", " (\n", " int((destination.x + SIZE_ROUTE) / (2 * SIZE_ROUTE) * IMG_SIZE),\n", " int((destination.y + SIZE_ROUTE) / (2 * SIZE_ROUTE) * IMG_SIZE),\n", " ),\n", " (0, 0xFF, 0),\n", " )\n", " return img" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def generate_example_image(route_type: Literal[\"line\", \"dot\"]):\n", " \"\"\"\n", " Generates an example image with the seed 42.\n", "\n", " Args:\n", " route_type: How the route is drawn. If 'line' is selected the complete route is selected.\n", " If 'dot' is selected the turning points a drawn in.\n", "\n", " Returns:\n", " The example image.\n", " \"\"\"\n", " # generate obstacles and a destination\n", " obstacles = generate_obstacles(42)\n", " destination = generate_destination(obstacles, 42)\n", " # try to generate a route\n", " try:\n", " route, _ = experiments.generate_route(\n", " position=Point(0, 0),\n", " goal=destination,\n", " obstacles=obstacles,\n", " wind=(18, 180),\n", " )\n", " except Exception:\n", " route = None\n", "\n", " # draw the scenario\n", " return generate_image_from_map(\n", " obstacles=obstacles,\n", " destination=destination,\n", " route=route,\n", " route_type=route_type,\n", " )" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Nachfolgend werden zwei solcher Scenarios Bilder gezeigt. Zuerst aber wird zum Vergleich das Scenario mit dem Seed 42 als Karte dargestellt, um den Unterschied zu zeigen." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "if not NO_SHOW:\n", " # set the default seed of 42\n", " seed: int = 42\n", " # create a figure\n", " plt.figure(figsize=(8, 8))\n", " wind_dir = 180\n", " # generate obstacles and a destination\n", " generated_obstacles = generate_obstacles(seed)\n", " generated_destination = generate_destination(generated_obstacles, seed)\n", " route_generated = None\n", " # try generating a route\n", " try:\n", " route_generated, _ = experiments.generate_route(\n", " position=Point(0, 0),\n", " goal=generated_destination,\n", " obstacles=generated_obstacles,\n", " wind=(18, wind_dir),\n", " )\n", " except Exception as e:\n", " route_generated = None\n", " # plotting the situation\n", " plot_situation(\n", " obstacles=generated_obstacles,\n", " destination=generated_destination,\n", " obstacle_color=\"RED\",\n", " route=route_generated,\n", " title=f\"Seed: {seed}, Cost: {route_generated.cost:.3f}\"\n", " if route_generated\n", " else f\"Seed: {seed}\",\n", " legend=seed == 0,\n", " )\n", " plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Zeigt das Scenario mit dem Seed 42 mit eingezeichneten Wendepunkten, wenn dieses Notebook im Pyrate Docker Container ausgeführt wurde. Wichtig zu beachten ist in dieser Darstellung die Drehung des Vorzeichens der Y Achse was zu einer Horizontalen Spiegelung der Darstellung führt." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "generate_example_image(route_type=\"dot\").resize(\n", " (IMG_SHOW_SIZE, IMG_SHOW_SIZE), Image.Resampling.BICUBIC\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Zeigt das Scenario mit dem Seed 42 mit eingezeichneten Wendepunkten, wenn dieses Notebook im Pyrate Docker Container ausgeführt wurde." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "generate_example_image(route_type=\"line\").resize(\n", " (IMG_SHOW_SIZE, IMG_SHOW_SIZE), Image.Resampling.BICUBIC\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Massengenerierung von Daten\n", "\n", "Die oben definierten Funktionen generieren immer einen Datensatz.\n", "Die folgenden Funktionen definieren einen einzelnen Datensatz als `pd.Series` einer einzelnen Zeile in einem `pd.DataFrame`. Die so erzeugten Datensatze werden in `pd.DataFrames` zusammengefasst. Hier wurde eine Anzahl von 50 Datensätzen auf einmal gewählt. Diese werden dann gespeichert, um danach mehr Daten zu generieren. Da der Wegfindealgorithmus immer noch experimentell ist, werden Wege die nicht gefunden worden oder bei deren finden ein Fehler auftritt werden mit `NaN` gefüllt." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "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.\n", " Can be generated at a later date without the fear for a loss of accuracy.\n", " \"\"\"\n", " # generate obstacles\n", " obstacles = generate_obstacles(seed)\n", " # find a destination\n", " destination = generate_destination(obstacles, seed)\n", "\n", " # find a possible route\n", " try:\n", " route, _ = experiments.generate_route(\n", " position=Point(0, 0),\n", " goal=destination,\n", " obstacles=obstacles,\n", " wind=(18, wind_dir),\n", " )\n", " except Exception:\n", " route = None\n", "\n", " # collect all generated data in a `pd.Series`\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, route)\n", " if image\n", " else pd.NA,\n", " \"route\": route.points if route else pd.NA,\n", " \"cost\": route.cost if route else pd.NA,\n", " },\n", " name=str(seed),\n", " )" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Nachfolgend wird ein kurzes Beispiel eines solchen `pd.DataFrame` angezeigt." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "if not NO_SHOW:\n", " df = pd.DataFrame(\n", " [generate_all_to_series(i, image=False) for i in tqdm(range(12))]\n", " ).set_index(\"seed\")\n", " df.to_pickle(\"test.pickle\")\n", "if os.path.exists(\"test.pickle\"):\n", " df = pd.read_pickle(\"test.pickle\")\n", "else:\n", " df = None\n", " print(\"No data generated or cached!\")\n", "df" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "Die folgende Zelle ist Verantwortlich für das massenweise Generieren von Trainingsdaten. Sie kann entweder so eingestellt werden das nur eine einzige Batch aus 50 neuen Datensätzen generiert werden soll oder eine ganze Reihe von Batches. Sind nicht alle anforderungen zun Ausführen der Zelle erfüllt, wird sie automatische übersprungen." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Skips the following cell if the code can't be executed.\n", "if os.getenv(\"PYRATE\"):\n", " save_frequency = int(os.getenv(\"save_frequency\", \"50\"))\n", " start_seed = int(os.getenv(\"seed_start\", \"0\"))\n", " continues = bool(os.getenv(\"continues\", \"false\"))\n", "\n", " # try finding a block of seeds that is not used\n", " files = glob.glob(\"data/*.pickle\")\n", " seed_groups = {int(file[9:-7]) for file in files}\n", " for next_seeds in range(start_seed, 1_000_000, save_frequency):\n", " # skip if the seed block already exists or is generated by another instance if this notebook\n", " if next_seeds in seed_groups:\n", " continue\n", "\n", " # start generating routes for the seed block\n", " print(f\"Start generating routes for seed: {next_seeds}\")\n", "\n", " # reserving the seed block by looking down the seed block with an empty file\n", " tmp_pickle_str: str = f\"data/tmp_{next_seeds:010}.pickle\"\n", " pd.DataFrame().to_pickle(tmp_pickle_str)\n", "\n", " # generate the data\n", " df = pd.DataFrame(\n", " [\n", " generate_all_to_series(i, image=False)\n", " for i in tqdm(range(next_seeds, next_seeds + save_frequency, 1))\n", " ]\n", " ).set_index(\"seed\")\n", "\n", " # saves the data and delete the temporary file\n", " pickle_to_file = f\"data/raw_{next_seeds:010}.pickle\"\n", " df.to_pickle(pickle_to_file)\n", " os.remove(tmp_pickle_str)\n", "\n", " # break the loop if only a single block of data should be generated.\n", " if not continues:\n", " break" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Daten Zusammenfassen\n", "\n", "Nachdem man den generierenden Teil des Codes für eine Weile hat laufen lassen, erhält man eine vielzahl einzelner Dateien. Diese werden nachfolgend zusammengefasst. Diese so zusammengefasste Tabelle wird nachfolgend bereinigt.\n", "Direkt nach dem Zusammenfassen der Daten werden alle einträge für die keine Routen gefunden wurde weggelassen.\n", "\n", "Dies kann folgende Gründe haben:\n", "* Startpunkt $P(0, 0)$ ist von Hindernissen eingeschlossen\n", "* Der Zielpunkt ist von Hindernissen eingeschlossen\n", "* Fehler im Algorithmus der die Routen generiert" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "DATA_COLLECTION_PATH: Final[str] = \"data/collected.pickle\"\n", "\n", "# Load a cached result should it not be demanded to generate all data new.\n", "if os.path.exists(DATA_COLLECTION_PATH) and not GENERATE_NEW:\n", " collected_data = pd.read_pickle(DATA_COLLECTION_PATH)\n", "else:\n", " # Read the first n files\n", " # The number of files read can be defined with the constant: NUMBER_OF_FILES_LIMIT\n", " # The dataframes read are concatenate directly after\n", " collected_data = pd.concat(\n", " [\n", " pd.read_pickle(filename)\n", " for filename in tqdm(glob.glob(\"data/raw_*.pickle\")[:NUMBER_OF_FILES_LIMIT])\n", " ]\n", " )\n", "# Prints a short summary of the data.\n", "number_of_maps = len(collected_data.index)\n", "print(f\"{number_of_maps: 8} maps collected\")\n", "collected_data.dropna(subset=[\"route\"], inplace=True)\n", "number_of_routes = len(collected_data.index)\n", "print(f\"{number_of_routes: 8} routes collected\")\n", "collected_data.to_pickle(DATA_COLLECTION_PATH)\n", "collected_data" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Daten Filtern\n", "\n", "Die so erzeugten Daten sind ungefiltert. Sie müssen nun überprüft werden. Dazu wurden einige hundert Datensätze geplottet. Einige Muster sind dabei aufgefallen. Die nachfolgenden Filter resultieren aus diesen Mustern.\n", "\n", "#### Die Route verlässt die Karte\n", "\n", "Das Generieren von Heatmaps von Segelrouten erfordert, das sich das mögliche Ergebnis sinnvoll darstellen lässt. Dazu muss die Route vollständig im definierten Bereich liegen. Alle Routen, die die Karte verlassen werden, weggelassen." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def check_route_in_bounds(route):\n", " \"\"\"\n", " Check if a route exists and is in bounds.\n", "\n", " Args:\n", " route: An `np.ndarray` of points the builds the route.\n", "\n", " Returns:\n", " A non-existing route or a route that leaves the area routes should stick to return `False` otherwise, `True` is returned.\n", " \"\"\"\n", "\n", " # CHecks if the route exists\n", " if route is None:\n", " return False\n", " if route is pd.NA:\n", " return False\n", " # Checks if the route is of the right data type.\n", " if not isinstance(route, np.ndarray):\n", " return False\n", " # Checks if a position is out of bounds.\n", " if np.array(\n", " abs(route) > SIZE_ROUTE,\n", " ).any():\n", " return False\n", " return True\n", "\n", "\n", "# Count the number of data points there are before this filter is used.\n", "data_before = len(collected_data.index)\n", "\n", "# Filtering\n", "df_filter = collected_data[\"route\"].apply(check_route_in_bounds)\n", "filtered = collected_data[~df_filter]\n", "collected_data = collected_data[df_filter]\n", "\n", "# Count the number of data points there are after this filter is used.\n", "data_after = len(collected_data.index)\n", "\n", "# Print a short report over the changes to the dataset.\n", "print(\n", " f\"{data_before} - {data_before-data_after} = {data_after} sets of data remaining.\"\n", ")\n", "\n", "# delete variables that where only used inside this cell\n", "del data_before, data_after, filtered, df_filter" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "#### Routen auf Fehler überprüfen\n", "\n", "Ein bug in der Routenfindung hat zu selbstschneidung der Routen geführt dieser wurde beim Sailing Team Darmstadt e.V. behoben. In den ersten ca. 27000 datensätzen gibt es dennoch Selbstschneidungen der Routen. Diese werden hier erkannt und da nicht Representative und nicht richtig aus diesem Datensatz herausgenommen." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def check_route_self_crossing(route):\n", " \"\"\"\n", " Check if a route has self intersections.\n", "\n", " Args:\n", " route: An `np.ndarray` of points the builds the route.\n", "\n", " Returns:\n", " `True` if the route is self intersecting.\n", " \"\"\"\n", " if isinstance(route, float):\n", " print(float)\n", " return not LineString(route).is_simple\n", "\n", "\n", "# count the number of data points before this filter was applied.\n", "data_before = len(collected_data.index)\n", "\n", "# filter the data\n", "collected_data = collected_data[\n", " ~collected_data[\"route\"].apply(check_route_self_crossing)\n", "]\n", "\n", "# count the number of data points after this filter was applied.\n", "data_after = len(collected_data.index)\n", "\n", "# print a short report over the changes to the dataset.\n", "print(\n", " f\"{data_before} - {data_before-data_after} = {data_after} sets of data remaining.\"\n", ")\n", "\n", "# delete variables that where only used inside this cell\n", "del data_before, data_after" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "#### Filter der Routen nach Kosten\n", "\n", "Einige der Routen haben trotz einer Erfolgreichen wegfindung enorm hohe kosten. Kosten werden beim Generieren der route mitberechnet und sind was bei dem Routen generierenden Gradientenabstiegsverfahren optimiert worden. Sie setzen sich zusammen aus Segelzeit und Risiken. Außerordentlich hohe Kosten legen daher entwendet nahe, dass keine gute Route gefunden werden konnte oder das die gefundene Route zu einem schlechten Lokalen Minimum konvergiert hat. Daher werden die teuersten $5\\%$ der Routen weggelassen.\n", "\n", "Die folgende Route berechnet das $95\\%$ Quantil und errechnet wie viele Einträge über $95\\%$ liegen." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "QUANTILE_LIMIT: Final[float] = 0.95\n", "if \"DATA_UPPER_LIMIT_QUANTIL\" not in locals():\n", " DATA_UPPER_LIMIT_QUANTIL: Final[float] = collected_data[\"cost\"].quantile(\n", " QUANTILE_LIMIT\n", " )\n", " OVER_QUANTILE: Final[int] = int(len(collected_data.index) * (1 - QUANTILE_LIMIT))\n", "# noinspection PyUnboundLocalVariable\n", "print(\n", " f\"{OVER_QUANTILE} entries over the {QUANTILE_LIMIT} quantile at {DATA_UPPER_LIMIT_QUANTIL:.3f}\"\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Der folgende Codeschnipsel berechnet das Histogramm der Kosten. Wie wenig repräsentativ die höchsten $5\\%$ der Kosten sind, ist direkt ersichtlich." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "collected_data[\"cost\"].plot.hist(bins=15, log=True)\n", "plt.axvline(x=DATA_UPPER_LIMIT_QUANTIL, color=\"RED\", label=\"95% Quantil\")\n", "plt.legend()\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Nachfolgend werden einige der Route mit sehr hohen Kosten gezeigt. Die Meisten kommen dem Land sehr nahe oder Segeln sehr stark gegen den Wind." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "plt.figure(figsize=(15, 25))\n", "for count, (seed, row) in tqdm(\n", " enumerate(\n", " collected_data[collected_data[\"cost\"] > DATA_UPPER_LIMIT_QUANTIL]\n", " .sort_values(\"cost\")\n", " .iloc[0 :: int(OVER_QUANTILE / 12)]\n", " .iloc[:12]\n", " .iterrows()\n", " ),\n", " total=12,\n", "):\n", " plt.subplot(5, 3, count + 1)\n", " plot_situation(\n", " destination=Point(row.destination_x, row.destination_y),\n", " obstacles=row.obstacles,\n", " obstacle_color=\"RED\",\n", " route=row.route,\n", " title=f\"Cost: {row.cost}\",\n", " )\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Die Daten werden nun beim $95\\%$ Quantil der Kosten gefiltert." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "collected_data = collected_data.loc[collected_data[\"cost\"] < DATA_UPPER_LIMIT_QUANTIL]\n", "collected_data" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Ein neues Histogramm der Kostenfunktion wird geplottet." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "collected_data[\"cost\"].plot.hist(log=True)\n", "plt.title(\"Route costs cut at the 95% quantile\")\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "#### Filter der Routen nach Komplexität" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "Gegenüber den Routen mit zu hohen Kosten stehen die Routen mit zu geringen Kosten. Daher werden als nächsten Routen mit zu niedrigen Kosten betrachtet.\n", "Nachfolgend ist eine Auswahl solcher günstiger Routen angezeigt. Es fällt auf das all diese Routen direkt sind.\n", "Eine betrachtung der Verteilung der Routenpunkte ist daher notwendig." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "plt.figure(figsize=(17.5, 25))\n", "for count, (seed, row) in enumerate(\n", " collected_data[collected_data[\"cost\"] < DATA_UPPER_LIMIT_QUANTIL]\n", " .sort_values(\"cost\")\n", " .iloc[1:600:51]\n", " .iterrows()\n", "):\n", " plt.subplot(4, 3, count + 1)\n", " plot_situation(\n", " destination=Point(row.destination_x, row.destination_y),\n", " obstacles=row.obstacles,\n", " obstacle_color=\"RED\",\n", " route=row.route,\n", " title=f\"Cost: {row.cost:.3f}\",\n", " legend=count == 0,\n", " )\n", "plt.show()\n", "del seed" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def get_route_points(data):\n", " \"\"\"\n", " Counts how many stops are made inbetween.\n", "\n", " Args:\n", " data: a `pd.DataFrame` collecting all the data.\n", " Returns:\n", "\n", " \"\"\"\n", " complexity = data[\"route\"].apply(lambda r: r.shape[0] - 2)\n", " complexity.name = \"route complexity\"\n", " return complexity\n", "\n", "\n", "route_points = get_route_points(collected_data)\n", "route_points.plot.hist()\n", "plt.title(\"Route complexity in intermediate points\")\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Bei der oben angezeigten Komplexität wird, deutlich das diese teilweise etwas noch ist. Hier wird ein Limit von 15 eingeführt." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "routes_before = len(collected_data.index)\n", "collected_data = collected_data[route_points <= 15]\n", "routes_after = len(collected_data.index)\n", "print(\n", " f\"{routes_before} - {routes_before - routes_after} = {routes_after} \"\n", " f\"if only routes with less then 15 course changes remain.\"\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "get_route_points(collected_data).plot.hist(bins=15)\n", "plt.title(\"Route complexity in intermediate points\")\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "Die nun reduzierte Anzahl der Routen enthält eine zwar Representative mange an sehr einfachen Routen. Da das Ergebnis dieser Routen aber eine lehre, Heat Map für Kursänderungen ist, muss hier deutlich reduziert werden sodas sie nur einen angegebenen anteil am Gesamtvolumen ausmachen. Dieser Anteil wurde hier auf $5\\%$ gesetzt." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Define the upper limit of the percentage easy routes should reach\n", "LIMIT_SIMPLE_CASES = 0.05\n", "values = get_route_points(collected_data).value_counts().sort_index()\n", "chance_limit = (\n", " (len(collected_data.index) * LIMIT_SIMPLE_CASES * (1 - LIMIT_SIMPLE_CASES))\n", " / values.get(0, 1)\n", " if 0 in values.index\n", " else 0\n", ")\n", "print(\n", " f\"Limiting simple cases to {LIMIT_SIMPLE_CASES * 100:.1f}% of the total routes. Reducing simple routes to {(chance_limit * 100):.1f}% of their volume.\"\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Der folgende Abschnitt setzt das oben aufgestellte limit um." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "np.random.seed = 0\n", "collected_data = collected_data[\n", " (\n", " (get_route_points(collected_data) > 1)\n", " | (np.random.random(len(collected_data.index)) < chance_limit)\n", " )\n", "]\n", "del chance_limit" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Die so veränderte distribution der Routenkomplexität sieht dann so aus." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "get_route_points(collected_data).plot.hist(bins=15)\n", "plt.title(\"Complexity Distribution after an enforced limit to trivial solutions.\")\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Das `pd.DataFrame` welches die gefilterten Daten sammelt, sieht dann wie folgt aus:" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "collected_data" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Das konvertieren in trainierbare Daten\n", "\n", "Die bisher erstellten und gefilterten Daten müssen nun mit der oben definierten methode zum Generieren von Bildern `generate_image_from_map` transformiert werden. Die so transformierten daten werden dann zusammengefasst und in ein `tf.Dataset` konvertiert werden welches von Pandas genau für solche Fälle vorgesehen wird. Es gibt dort auch andere Methoden wie zum Beispiel die methode `tf.keras.utils.image_dataset_from_directory`. Bei diesem Problem besteht aber die Hoffnung, das auch ohne solche Methoden der RAM ausreicht und die Daten nicht immer wieder neu von der Festplatte gelesen werden müssen." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def generate_image_maps(row, route_type: Literal[\"dot\", \"line\"]):\n", " \"\"\"Generates the image version of the route.\n", "\n", " Adds another dimension to prepare vor concatenation in a later step.\n", " Divides by 0xFF to contain only 0 and 1 and values.\n", " Color channel zero contains obstacles.\n", " Color channel one contains the destination.\n", " Color channel two contains the route either as course change points or as continues lines.\n", "\n", " Args:\n", " row: The row of the pd.DataFrame that should be used to generate an image.\n", " route_type: Defines if the route should be drawn as a collection of course change points or continues lines.\n", " Returns:\n", " The image modified for concatenation and scaled to be easily used for pandas.\n", " Cast as uint8 for a minimal memory consumption.\n", " \"\"\"\n", " # expands the dimension by one\n", " img = np.expand_dims(\n", " # converts the image into a numpy array\n", " np.asarray(\n", " # generate the situation image form a map\n", " generate_image_from_map(\n", " obstacles=row.obstacles,\n", " destination=Point(row.destination_x, row.destination_y),\n", " route=row.route,\n", " route_type=route_type,\n", " )\n", " ),\n", " axis=0,\n", " )\n", " # integer divide to ensure all values are between 0 and 1\n", " img = img // 0xFF\n", " return img" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# drop the image column to save some space in the dataset\n", "if \"image\" in collected_data.columns:\n", " del collected_data[\"image\"]" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# save the collected and filtered data into a pickle file to load again later and flush the ram a bit.\n", "DATA_WITH_IMG_PATH: Final[str] = \"data/collected_and_filtered.pickle\"\n", "collected_data.to_pickle(DATA_WITH_IMG_PATH)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# collecting map images with routes as lines.\n", "collected_routes = np.concatenate(\n", " collected_data.progress_apply(generate_image_maps, axis=1, args=(\"line\",))\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Die gesammelten Daten sind relativ groß die nachfolgende Operation zeigt an wie viel RAM dafür gerade belegt ist." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "humanize.naturalsize(sys.getsizeof(collected_routes))" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Stellt sicher, dass ein Datentyp verwendet der ein Minimum an Speicher verwendet." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "assert (\n", " str(collected_routes.dtype) == \"uint8\"\n", "), \"Dtype needs to be unit8 to fit in the ram.\"" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "COLLECTED_ROUTES_LINE_DUMP = \"data/collected_routes_np_line.pickle\"\n", "with open(COLLECTED_ROUTES_LINE_DUMP, \"wb\") as f:\n", " pickle.dump(collected_routes, f)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Generiert die Daten für das Line format." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# collecting map images with routes as dots.\n", "collected_routes_dots = np.concatenate(\n", " collected_data.progress_apply(generate_image_maps, axis=1, args=(\"dot\",))\n", ")\n", "assert (\n", " str(collected_routes_dots.dtype) == \"uint8\"\n", "), \"Dtype needs to be unit8 to fit in the ram.\"\n", "COLLECTED_ROUTES_DOT_DUMP = \"data/collected_routes_np_dot.pickle\"\n", "with open(COLLECTED_ROUTES_DOT_DUMP, \"wb\") as f:\n", " pickle.dump(collected_routes_dots, f)\n", "\n", "# deletes the collection object\n", "del collected_routes_dots" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "## Das Model\n", "\n", "\n", "Jedes neuronale Netz, hat eine bestimmte *Form*, diese wird im Modell definiert. Die Form bestimmt die Anzahl der versteckten Schichten, ob beim Training spezielle Filter wie `Dropout` oder `BatchNormalization` verwendet werden sollen. Auch die art der Aktivierungsfunktionen wird hier definiert.\n", "\n", "Oft gibt es für bestimmte Probleme schon die Eine oder andere Art von Netzwerkstruktur/Modellstruktur, die sich als geeignet erwiesen hat. Für diese Art von Problem wo aus einem Bild eine anderes Generiert werden soll haben sich GAN netzwerke als besonders Schlagfertig erwiesen [2], [3], [4].\n", "\n", "GAN netzwerke bestehen immer aus zwei Komponenten einem `Generator` und einem `Discriminator` der `Generator` generiert ein Bild von einem anderen Bild. Der `Discriminator` versucht die Builder zu Unterscheide und weist so den `Gernerator` auf Fehler hin die durch eine normale Lernfunktion mit einer $l_1$ oder $l_2$ norm nicht hervorgehoben werden weill es für diese oft reicht wenn das Ziel ungefähr erreicht wird. Der `Discriminator` sorgt also für Klare Kontraste und saubere Farbverläufe.\n", "\n", "Das hier betrachtete Problem erwartet nun eine Heat map. Da nicht davon auszugehen ist das die Perfekte Route direkt gefunden wurde ist ein etwas verwaschenes Ergebnis eine Funktion nicht ein Problem. Daher wird hier versucht den Routenschätzer ohne `Discriminator` aufzubauen." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "### Der Generator\n", "\n", "Der `Generator` ist eine Art von Autoencoder. Er nimmt das Bild abstrahiert es in eine Sammlung von Features und generiert aus diesem Abstraction Format wieder ein Bild.\n", "Der `Generator` besteht daher aus einer Reihe von *Downsamplern* gefolgt von ebenso vielen *Upsamplern*. Der Downsampler fasst alle zwei Pixel, 3 Pixel über ein `tf.keras.layers.Conv2D` layer zusammen. Symmetrisch dazu macht der Upsampler macht dies wieder Rückgängig. So wird die Bildgröße mit jedem Downsampler Layer halbiert und mit jedem Upsampler Layer wieder verdoppelt. Dafür erhält jeder Downsampler mehr features während der Upsampler jedes mahl Features verliert. Up und Downsmapler sind meist Symmetrisch aufgebaut.\n", "Wie in, sowohl dem TensorFlow Tutorial[4], als auch in dem Praxiseinstieg Maschine Learning in der Sektion über GANs[2] zu lesen ist benötigt der Downsampler eine BatchNormaliszation leaky Relu als Aktivierungsfunktion im Downsampler. Relu wird für den Upsampler verwendet.\n", "* Relu `y = x if x >= 0 else 0`\n", "* LeakyRelu `y = x if x >= 0 else b * x` wobei $x$ eine Zahl viel kleiner als 1 ist.\n", "\n", "BatchNormalization normalisiert die Ausgabewerte einer Schicht über eine Training Batch, indem der Durchschnitt jeder Ausgangsschicht auf 0 geschoben wird und auf die Varianz 1 skaliert wird[5]. Beim Ausführen des Models wird die in der letzten Epoche festgelegte Gesamtbeschreibung und Skalierung genutzt. Dies sorgt zusammen mit dem DropOut Filter im Upsampler für ein konsistentes Lernen und verhindert das Overfitting.\n", "Interessanterweise erhält jedes Upsampling Schicht sowohl dass, vorangegangene Schicht als auch das Symmetrisch Downsampling Schicht als Input." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Source: https://www.tensorflow.org/tutorials/generative/pix2pix\n", "def downsample(filters, size, apply_batchnorm=True):\n", " \"\"\"Create a downsample layer.\n", "\n", " A downsample layer contains:\n", " * tf.keras.layers.Conv2D\n", " * An aktivation Function\n", " * Optional a batchnorm\n", " * A activation function (LeakyRelu)\n", " Args:\n", " filters: The number of features that should be gernated.\n", " size: The number of features / pixels should be reduced.\n", " apply_batchnorm: If True the Batchnorm is applied. Batch norms are used by default.\n", " Returns:\n", " A sequentail model contain the keras generated layers.\n", " \"\"\"\n", "\n", " initializer = tf.random_normal_initializer(mean=0.0, stddev=0.02)\n", "\n", " result = tf.keras.Sequential()\n", " result.add(\n", " tf.keras.layers.Conv2D(\n", " filters,\n", " size,\n", " strides=2,\n", " padding=\"same\",\n", " kernel_initializer=initializer,\n", " use_bias=False,\n", " )\n", " )\n", "\n", " if apply_batchnorm:\n", " result.add(tf.keras.layers.BatchNormalization())\n", "\n", " result.add(tf.keras.layers.LeakyReLU())\n", "\n", " return result" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Source: https://www.tensorflow.org/tutorials/generative/pix2pix\n", "def upsample(filters, size, apply_dropout=False):\n", " \"\"\"Create a upsample layer.\n", "\n", " A downsample layer contains:\n", " * tf.keras.layers.Conv2D\n", " * An aktivation Function\n", " * Optional a batchnorm\n", " * A activation function (LeakyRelu)\n", " Args:\n", " filters: The number of features that should be used to upsample the layer.\n", " size: The number of\n", " apply_dropout: If True a dropout layer ist used.\n", " Returns:\n", " A sequentail model contain the keras generated layers.\n", " \"\"\"\n", " initializer = tf.random_normal_initializer(0.0, 0.02)\n", "\n", " result = tf.keras.Sequential()\n", " result.add(\n", " tf.keras.layers.Conv2DTranspose(\n", " filters,\n", " size,\n", " strides=2,\n", " padding=\"same\",\n", " kernel_initializer=initializer,\n", " use_bias=False,\n", " )\n", " )\n", "\n", " result.add(tf.keras.layers.BatchNormalization())\n", "\n", " if apply_dropout:\n", " result.add(tf.keras.layers.Dropout(0.5))\n", "\n", " result.add(tf.keras.layers.ReLU())\n", "\n", " return result" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "#### Model erstellung\n", "Erstellt ein erstes model des Generatos wie oben beschrieben. Ein Schematisches Layout findet sich darunter." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def model_generator() -> tf.keras.Model:\n", " \"\"\"Creates an initial sequential model.\n", " \n", " Returns:\n", " A Sequential model.\n", " \"\"\"\n", "\n", " inputs = tf.keras.layers.Input(shape=[IMG_SIZE, IMG_SIZE, 2])\n", "\n", " down_stack = [\n", " downsample(64, 4, apply_batchnorm=False), # (batch_size, 64, 64, 128)\n", " downsample(128, 4), # (batch_size, 8, 8, 512)\n", " downsample(128, 4), # (batch_size, 4, 4, 512)\n", " downsample(256, 4), # (batch_size, 2, 2, 512)\n", " downsample(256, 4), # (batch_size, 1, 1, 512)\n", " downsample(512, 4), # (batch_size, 1, 1, 512)\n", " downsample(512, 4), # (batch_size, 1, 1, 512)\n", " ]\n", "\n", " up_stack = [\n", " upsample(512, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)\n", " upsample(256, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)\n", " upsample(256, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)\n", " upsample(128, 4), # (batch_size, 16, 16, 1024)\n", " upsample(128, 4), # (batch_size, 32, 32, 512)\n", " upsample(64, 4), # (batch_size, 64, 64, 256)\n", " ]\n", "\n", " initializer = tf.random_normal_initializer(0.0, 0.02)\n", " last = tf.keras.layers.Conv2DTranspose(\n", " 1,\n", " 4,\n", " strides=2,\n", " padding=\"same\",\n", " kernel_initializer=initializer,\n", " activation=\"sigmoid\", # was tanh\n", " ) # (batch_size, 256, 256, 3)\n", "\n", " x = inputs\n", "\n", " # Down sampling through the model\n", " skips = []\n", " for down in down_stack:\n", " x = down(x)\n", " skips.append(x)\n", "\n", " skips = reversed(skips[:-1])\n", "\n", " # Up sampling and establishing the skip connections\n", " for up, skip in zip(up_stack, skips):\n", " x = up(x)\n", " x = tf.keras.layers.Concatenate()([x, skip])\n", "\n", " x = last(x)\n", " \n", " # drop the chanel dimension\n", " reshaped = tf.keras.layers.Reshape((128, 128))(x)\n", "\n", " return tf.keras.Model(inputs=inputs, outputs=reshaped)\n", "\n", "tf.keras.utils.plot_model(model_generator(), show_shapes=True, dpi=64)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Um mehr Kontrolle über den Lernprozess zu haben werden drei Callbacks verwendet. Der EarlyStopping Callback verhindert vor allem das Verschwenden von Rechenzeit, indem er den Lernvorgang abbricht, wenn eine weile keine Verbesserung gefunden wurde. Verschlechtert sich die beobachtete Metric wird der Lernvorgang abgebrochen. Zudem wird das beste Ergebnis wieder hergestellt was Oberfitting verhindert." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "early_stop = tf.keras.callbacks.EarlyStopping(\n", " monitor=\"mean_squared_error\",\n", " min_delta=0.00001,\n", " patience=10,\n", " verbose=0,\n", " mode=\"auto\",\n", " restore_best_weights=True,\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Die Lernrate kann mit der nachfolgenden Methode gesenkt werden damit eventuelle Platues überwunden werden können." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "reduce_learning_rate = tf.keras.callbacks.ReduceLROnPlateau(\n", " monitor=\"loss\", factor=0.2, patience=3, min_lr=0.001, verbose=1\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "plt.figure(figsize=(17.5, 25))\n", "np_array = np.flip(collected_routes[1, :, :, :], axis=0)\n", "\n", "for chanel in tqdm(range(3)):\n", " plt.subplot(1, 4, chanel + 1)\n", " plt.imshow(np_array[:, :, chanel], interpolation=\"nearest\")\n", " plt.axis('off')\n", "plt.subplot(1, 4, 4)\n", "plt.imshow(0x88 * np_array[:, :, 0] + 0xFF * np_array[:, :, 2], interpolation=\"nearest\")\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Model Training\n", "\n", "Das Oben erstellte Model muss nun trainiert werden. Leider war es mir nicht möglich mehr als ein Model auf einmal im Speicher zu haben was dazu geführt hat, das nicht alle Modelle in diesem Notebook aufgezeichnet werden konten.\n", "\n", "Es wurde versucht das Modell mit den Fehler Function `MSE`, `MAE`, `Binary Crossentropy` wurden versucht. Die Ergebnisse waren sehr unterschiedlich.\n", "* `MAE` Der *M*ean *A*verage *E*rror wird durch den Durchschnitt des absoluten Fehlers berechnet. Das Ergebniss war in diesem Fall annähernd 0 über die gesamten ergebnisse. Das Optimieren hat also überhaupt nicht funktioniert.\n", "* `MSE` Der *M*ean *S*quare *E*rror bildet den Durchschnitt über die Quadrate der Fehlerfunktion. Die Dadurch verstärkten Ausreißer erlaubten ein wesentlich besseres Lernverhalten.\n", "* `Binary Crossentropy` Die Binary Crossentropy ist eine Fehlerfunktion für Wahrscheinlichkeitsfunktionen. Da wir hier eine Wahrscheinlichkeitsfunktion als Bild darstellen ist es die Theoretisch am besten geeignete Funktion. Dies wurde Experimentell bestätigt.\n", "\n", "Das Training wurde mit $20\\%$ der Daten als Validierungsdaten durchgeführt.\n", "Experimentell hat sich gezeigt das die Ergebnisse schon mit der Standard Lernrate sehr gute Ergebnisse Liefern. Diese werden mit einer geringeren Lernrate etwas besser auch, wenn der Lernaufwand so um ein Vielfaches größer ist.\n", "\n", "Leider war es mir nicht möglich mehr als die Routen von 500 pickel Detain auf einmal mit meiner GPU zu trainieren. Es ist zu vermuten, dass ein besseres Ergebnis mit mehr Datensätzen erreicht werden kann. Ohne GPU war der Rechenaufwand dieses Trainings aber nicht praktikabel.\n", "\n", "Als Optimierer wurde RMSprop verwendet." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "markdown", "source": [ "#### Training des Modells mit Routen als Linien\n", "\n", "Erstellt ein neues Model, Compiliert das Model und initialisiert die Schichten.\n", "Danach wird direkt das Training begonnen." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Delete the generator variable if allrady defined. Saves some gpu memory,\n", "if 'generator' in globals():\n", " print(\"Generator exists. Deleting for reset\")\n", " del generator\n", "\n", "# creates the model\n", "generator = model_generator()\n", "\n", "# compiles the model\n", "generator.compile(\n", " optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.00001), # \n", " loss=\"binary_crossentropy\",\n", " metrics=[\n", " \"binary_crossentropy\",\n", " \"mean_squared_error\",\n", " \"mean_absolute_error\",\n", " ],\n", ")\n", "\n", "# starts the training\n", "history = generator.fit(\n", " collected_routes[:, :, :, :2],\n", " collected_routes[:, :, :, 2],\n", " validation_split=0.2,\n", " epochs=100,\n", " use_multiprocessing=False,\n", " workers=1,\n", " callbacks=[early_stop, tf.keras.callbacks.TerminateOnNaN()],\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def plot_history(history_data, metrics: str | list) -> None:\n", " \"\"\"Plot some metrics from a training history.\n", "\n", " Args:\n", " history_data: The history data to plot.\n", " metrics: The metrics that should be ploted.\n", "\n", " Returns:\n", " None\n", " \"\"\"\n", " if isinstance(metrics, str):\n", " metrics = [metrics]\n", " for metric in metrics:\n", " plt.plot(history_data[metric], label=metric)\n", " plt.plot(history_data[f\"val_{metric}\"], label=f\"val_{metric}\")\n", " if len(metrics) == 1:\n", " plt.ylabel('loss')\n", " plt.xlabel('epoch')\n", " plt.legend(loc='best')\n", "\n", "# Plots the training history in a single figure\n", "plt.figure(figsize=(20, 5))\n", "\n", "# plots the loss\n", "plt.subplot(1, 3, 1)\n", "plot_history(history.history, \"loss\")\n", "plt.title('model loss')\n", "\n", "# plots the binary cross entropy <-> equal to loss\n", "plt.subplot(1, 3, 2)\n", "plot_history(history.history, \"binary_crossentropy\")\n", "plt.title('binary crossentropy')\n", "\n", "# plots the other metrics\n", "plt.subplot(1, 3, 3)\n", "plot_history(history.history, [\"mean_absolute_error\", \"mean_squared_error\"])\n", "plt.title('other metrics')\n", "\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "#### Betrachtung des trainierten Models mit Routen als Linien\n", "\n", "Die besten ergebnisse lagen bei einer `binary_crossentropy` von 0.0103 und einer `val_binary_crossentropy` von 0.0127 nach 51 Epochen.\n", "Bei dieser Art von Problem ist jedoch eine Visuelle überprüfung der Prediction notwendig." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "def plot_predicted_data(model: tf.keras.Model, data: np.ndarray, pos=0) -> None:\n", " \"\"\"Plots a single dataset in three versions.\n", "\n", " - The labeled situation.\n", " - The solution in context.\n", " - The prediction as a heat map.\n", " \n", " Flips everything for more consistency.\n", "\n", " Args:\n", " model: The trained model that should be visualised.\n", " data: The raw data that should be used as an example to visualise.\n", " pos: The position of the dataset that should be shown. If negativ test data is shown else training data is shown.\n", "\n", " Returns:\n", " None\n", " \"\"\"\n", " plt.figure(figsize=(15, 5))\n", " tt = \"test\" if pos < 0 else \"train\"\n", " plt.title(f\" for {tt} Nr: {abs(pos)}\")\n", " data = data[pos, :, :, :]\n", " predicted = model.predict(\n", " np.expand_dims(\n", " data[:, : , :2], 0),\n", " verbose=\"0\",\n", " steps=None,\n", " callbacks=None,\n", " max_queue_size=10,\n", " workers=1,\n", " use_multiprocessing=False,\n", " )[0]\n", " \n", " # flip for viewer consistency (Wind from north)\n", " data = np.flip(collected_routes[pos, :, :, :], axis=0)\n", " predicted = np.flip(predicted, axis=0)\n", " plt.axis('off')\n", " \n", " # Plots the original labeled data\n", " plt.subplot(1, 3, 1)\n", " plt.title(\"Original map\")\n", " plt.imshow(data[:, :, 0] * 2 + data[:, :, 1] * 3 + data[:, :, 2], interpolation=\"nearest\")\n", "\n", "\n", " # Plots the prediction in context\n", " plt.subplot(1, 3, 2)\n", " plt.title(\"Prediction in context\")\n", " plt.imshow(data[:, :, 0] * 2 + predicted[:, :] / predicted[:, :].max() * 3, interpolation=\"nearest\")\n", "\n", " # Plots the predication as a pure heat map\n", " plt.subplot(1, 3, 3)\n", " plt.title(\"Predicted head map\")\n", " plt.imshow(predicted[ :, :], interpolation=\"nearest\")\n", " plt.colorbar()\n", " plt.suptitle(f'Plot of the {tt} scenario with the index {pos if pos >= 0 else -pos - 1}')\n", " plt.show()\n" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Nachfolgend werden sowohl Vorhersagen mit Scenarios verglichen nebeneinander und gemeinsam dargestellt, um eine Abschätzung über die Vorhersagequalität zu machen.\n", "Erst wurden die Vorhersagen von Trainingsdaten visualisiert. Danach von Validierungsdaten." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "for i in range(10):\n", " plot_predicted_data(generator, collected_routes, i)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "for i in range(10):\n", " plot_predicted_data(generator, collected_routes, -i - 1)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### Training mit routen als liste von Wendepunkten\n", "\n", "Analog wurde das gleiche Model mit einer Liste an Wendepunkten trainiert. Hier wurde mti einem neuen Modell gearbeitet.\n", "Ein Weitertrainieren / verändern des Alten modells where vermutlich genauso gut möglich." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "del collected_routes\n", "collected_routes = np.load(COLLECTED_ROUTES_DOT_DUMP, allow_pickle=True)\n", "collected_routes.shape" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Darstellung von Hindernissen, ziel und Label in der Darstellung der Route als Liste von Wendepunkten-" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "plt.figure(figsize=(17.5, 25))\n", "np_array = np.flip(collected_routes[1, :, :, :], axis=0)\n", "\n", "for chanel in tqdm(range(3)):\n", " plt.subplot(1, 4, chanel + 1)\n", " plt.imshow(np_array[:, :, chanel], interpolation=\"nearest\")\n", "plt.subplot(1, 4, 4)\n", "plt.imshow(0x88 * np_array[:, :, 0] + 0xFF * np_array[:, :, 2], interpolation=\"nearest\")\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Delete the generator variable if allrady defined. Saves some gpu memory,\n", "if 'generator' in globals():\n", " print(\"Generator exists. Deleting for reset.\")\n", " del generator\n", "\n", "# creates the model\n", "generator = model_generator()\n", "\n", "# compiles the model\n", "generator.compile(\n", " optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.00001), # \n", " loss=\"binary_crossentropy\",\n", " metrics=[\n", " \"binary_crossentropy\",\n", " \"mean_squared_error\",\n", " \"mean_absolute_error\",\n", " ],\n", ")\n", "\n", "# starts the training\n", "history = generator.fit(\n", " collected_routes[:, :, :, :2],\n", " collected_routes[:, :, :, 2],\n", " validation_split=0.2,\n", " epochs=100,\n", " use_multiprocessing=False,\n", " workers=1,\n", " callbacks=[early_stop, tf.keras.callbacks.TerminateOnNaN()],\n", ")" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "plt.figure(figsize=(20, 5))\n", "plt.subplot(1, 3, 1)\n", "plot_history(\"loss\")\n", "plt.title('model loss')\n", "plt.subplot(1, 3, 2)\n", "plot_history(\"binary_crossentropy\")\n", "plt.title('binary crossentropy')\n", "plt.subplot(1, 3, 3)\n", "plot_history([\"mean_absolute_error\", \"mean_squared_error\", \"root_mean_squared_error\"])\n", "plt.title('other metrics')\n", "plt.show()" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "Betrachtung des trainierten Models mit Routen als Liste von Kurswechselpositionen.\n", "Die besten Ergebnisse wurden nach 20 Epochen erreicht und lagen bei einer binary_crossentropy von 0.0023 und einer val_binary_crossentropy von 0.0035. Bei dieser Art von Problem ist jedoch eine Visuelle überprüfung der Prediction notwendig." ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } } }, { "cell_type": "code", "execution_count": 60, "metadata": { "pycharm": { "name": "#%%\n" }, "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3MAAAFGCAYAAADAYcZNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAABfmUlEQVR4nO39eZykZX3v/7/e3bOxDavhsAkoaA56XAng1/WEowIuePJzQY2iIZl4ookmGgU1SjyaYI5xi0ZDhIBLBMRtjKOouEWjyGDcAA0DgjPI4rAMA8PMdHd9fn9c1111V01Vd3V3VdddVe/nPOrRde9X3V19zX1tn0sRgZmZmZmZmQ2XiUEnwMzMzMzMzObPhTkzMzMzM7Mh5MKcmZmZmZnZEHJhzszMzMzMbAi5MGdmZmZmZjaEXJgzMzMzMzMbQi7MmdlYk/RNSX+4RNf6P5Juk3SvpP272P9lkr6zFGnrhqQnSvrFoNPRL/n38qA+nv/Dkv5qlu1nS/r4Is7/FEmbFnp8y7leLOkrCzz2CEkhaVkv0mJmZp25MGdmI0/SjZLuzw/rt0m6QNKe8zzHoh5QJS0H3g08LSL2jIg7enn+Lq6/qIICQET8e0Q8tFdpqpr8e7mhj+d/RUT8X+hNwSt/X47qTeqaRcQnIuJp/Tj3Ykh6jKRvl/6WXz3oNJmZDZILc2Y2Lp4VEXsCjwGOBd68xNc/EFgFXL3E1+2KkrH8P8EtSMNB0gHAl4F/AvYHjgIW1HpoZjYqxvI/bjMbXxFxM/Al4OGt2yRNSHqzpJsk3S7po5L2zpu/nX/enVsFHtfm+JWS3ivp1/n13rzuIcAvSsd/vU3SOp5f0rsk3SXpl5JOLq3fW9J5km6RdLOkt0uabJOuk4A3Ai/I5/5xXv9NSe+Q9F1gG/AgSS+XdK2krZJukPTHpfM0tSblFs/XSfqJpC2SLpa0qs1nQ9JRkr6V99ss6eLStodJ+qqkO3NryxtLv48zJV0v6Q5Jl0jaL28rWjJPl/SrfM43lc55nKTvSbo7358PSFpR2h6SXinpOuC60rqjSvf2o5J+k78Pb25X2JW0Krf6HpCX3yRpWtLqvPx/Jb03v78g/472IH0HD86/j3slHZxPuSJfd6ukqyUd2+F+Ft+XH+fjX1Da9tr8/b1F0stL61fm79Kv8n3+sKTdOpy/qYtvvjevkHRdvqcflKS8bTKfd7OkG4BntJyr7fdU0gpJP5L0p6XzfFfSW9qlCfgL4LLcargjIrZGxLUd9jUzGwsuzJnZWJF0GHAK8J9tNr8sv/4n8CBgT+ADeduT8s99cne877U5/k3ACcCjgEcCxwFvjoj/Ah5WOv532xzb6fzHkwqCBwB/B5xXPEQDFwDTpBaKRwNPA3YZ/xcRXwb+Brg4n/uRpc0vAdYAewE3AbcDzwRWAy8H3iPpMW3SW3g+cBJwJPAI0v1r5/+SWlH2BQ4F/gFA0l7A10gtLgfnz3J5PuZPgecAT87b7gI+2HLeJwAPBU4E3iLpv+f1M8Cfk+7b4/L2P2k59jmk+3tMm/T+A7A36XvwZOClpPvRJCK2A1fmfcg/bwIeX1r+Vssx9wEnA7/Ov489I+LXefOzgYuAfYC1NL5/rdctvi+PzMcXheP/ltN9CHAG8EFJ++Zt5wAPIX0/j8r7dCo4tfNM4HdIv+fnA0/P6/8ob3s0qdX7uS3HXUCb72lE7AR+H3hb/r2dCUwC7+hw/ROAOyX9Ry6sfkHSA+eRfjOzkePCnJmNi89Juhv4Dunh+m/a7PNi4N0RcUNE3AucBZym7rvhvRh4W0TcHhG/Af6aVFhajJsi4p8jYga4EDgIOFDSgaRC6Wsi4r6IuB14D3DaPM9/QURcHRHTETEVEV+MiOsj+RapAPbEWY5/f0T8OiLuBL5AKii0MwUcDhwcEdsjomj1eSZwa0T8fV6/NSKuyNteAbwpIjZFxA7gbOC5Lb+Pv46I+yPix8CPSYVoIuKqiPh+/lw3krrmPZlmfxsRd0bE/eWVSq2bpwFn5fTcCPw9nX+X3wKenNP1COD9eXkVqfDz7Q7HtfOdiFiXf98fKz7PPEyRvoNTEbEOuBd4aK4AWAP8ef7MW0l/A/P5vpwTEXdHxK+Ab9D4XT8feG9EbMzfg78tDpjrexoRPwPeDnwOeB3wkvzZ2zkUOB14NfBA4JfAJ+eRfjOzkeNxAmY2Lp4TEV+bY5+DSa0qhZtI+eSBXV6j3fEHd9i3W7cWbyJiW26U2xPYD1gO3NJoqGMC2DjP8zftr9SN862kFpwJYHfgp92kj9RVs9PnfT2pde4Hku4C/j4izgcOA67vcMzhwGcl1UrrZmj+fbRef8/8OR5CCjhzbP4My4CrWs7f6V4dQLq3rb/LQzrs/618rceQ7tVXgfNILUkbWoPdzKH186yStCwiprs8/o6WfYt78gDSfbiq9H0RqSVsoWkrgggdTPO9LN+3w5n7e3ohqTXu0xFx3SzXvx/4bERcCSDpr4HNkvaOiC3z+BxmZiPDLXNmZg2/Jj18Fh5I6h52GxALPP7XHfZt1c35yzYCO4ADImKf/FodEQ/rsH+n89fXS1oJfBp4F3BgROwDrCM99C9KRNwaEX8UEQcDfwz8Yx6ftpHUlbGdjcDJpc+3T0SsyuMe5/Ih4OfA0RGxmjRmsPVzdLonm2m0JBYeCHS67n+Qunr+b+BbEXFN3v8UWrpYdnHtftlMKgw9rHQv985BgRbrFlKhvFDu+tjN9/QfgX8Dni7pCbNc5yc037elvodmZpXjwpyZWcMngT+XdKTS1AXFOLNp4DdAjc4Fj+L4N0t6QA6I8Rag2+kAujl/XUTcQuoC+feSVisFC3mwpNauhIXbgCPaBfEoWQGszGmZzq10PQlPL+l5kg7Ni3eRHsRrpIf4gyS9Jgfo2EvS8Xm/DwPvkHR4PscDJJ3a5SX3Au4B7pX028D/6TatuZvfJfnae+Xr/wUdfpcRsY3U6vdKGoW3/yB1E+1UmLsN2F+NADsLcRvdf19qwD+TxkD+FoCkQyQ9ffYju3IJ8GeSDs3j884sXXfW76mklwCPJY21/DPgQnWeNuRfgP8t6VFKU338FalbqlvlzGxsuTBnZtZwPmmc0rdJ43G2k4JwFA/s7wC+m6P5ndDm+LcD60ktCD8FfpjXzanL87d6KakAdg2pgHQpaUxdO5/KP++Q9MMOadhKeqC+JJ/vRaQgHL3wO8AVku7N53x1Hpu4FXgq8CxSN77rSAFoAN6X9/2KpK3A90kBS7rxupz+raRCzMWz776LPwXuA24gjbP8V9L3o5NvkboT/qC0vBcdxstFxM9Jhf8b8u97Id1xzyYVfu6W9Pwu9n8DsAH4vqR7SIFnejFv4D8Dl5HGLP4Q+EzL9rbf0xy85L3ASyPi3oj4V9Lfz3vaXSQivk5qYf0iKVDPUaTfsZnZ2FKEeymYmZmZmZkNG7fMmZmZmZmZDSEX5szMzMzMzIaQC3NmZmZmZmZDyIU5MzMzMzOzIeTCnJmZmZmZ2RByYc7MzMzMzGwIuTBnZmZmZmY2hFyYMzMzMzMzG0IuzJmZmZmZmQ0hF+bMzMzMzMyGkAtzZmZmZmZmQ8iFOTMzMzMzsyHkwpyZmZmZmdkQcmHOzMzMzMxsCLkwZ2ZmZmZmNoRcmDMzMzMzMxtCLsyZmZmZmZkNIRfmzMzMzMzMhpALc2ZmZmZmZkPIhTkzMzMzM7Mh5MKcmZmZmZnZEHJhzszMzMzMbAi5MGdNJL1R0kd6vW8X5wpJR/XiXGZWbZIukPT2/P6Jkn6xwPN8WNJf9TZ1vc3bzMzM+kkRMeg0WJ9IehnwWuDBwD3AZ4GzIuLuASarLUkBHB0RGwadFjMDSTcCBwIzwH3Al4BXRcS9PTj3BcCmiHjzPI55GfCHEfGExV5/2Eh6CvDxiDi0SucyM7PBc8vciJL0WuCdwF8CewMnAIcDX5W0osMxy5YuhWY2BJ4VEXsCjwGOBXYpfDnfMDMzGxwX5kaQpNXAXwN/GhFfjoipiLgReD5wBPD7eb+zJV0q6eOS7gFeltd9vHSul0q6SdIdkv5K0o2S/lfp+I/n90fkrpKnS/qVpM2S3lQ6z3GSvifpbkm3SPpAp0Jlm8/zTUlvl/Qfku6V9AVJ+0v6hKR7JF0p6YjS/u+TtDFvu0rSE0vbis98saStkn4o6ZELvtlmYyAibia1zD0c6t2iXynpOuC6vO6Zkn6U/8b/Q9IjiuMlPTr/rW2VdDGwqrTtKZI2lZYPk/QZSb/J+c4HJP134MPA43IecHfet95dMy//kaQNku6UtFbSwaVtIekVkq7LafygJLX7vPPJ29ocu5ukv8/55hZJ35G0W972bElX5+t/M3+u4rgbJb1O0k/ycRdLWiVpj3zvD86f/V5JB0uakHSmpOvzfbpE0n75XB+S9OnSud8p6fJO5+r4izczG2FP/597xLGPXDXvl6QvDzrtZa5RHU3/H+lh6TPllRFxr6R1wFOB8/PqU4HnAS8FVgJvKPaXdAzwj8BJwA+AvwEOmePaTwAeCjwE+IGkz0TEtaSuWn8OrAcOJT1Q/Anw3i4/02nA04HNwPfy60+A0/NneSvw8rzvlcDbgC3Aq4FPSToiIraXPvMLSYXaVwOfk/SQiJjqMi1mY0XSYcApNOcpzwGOB+6X9GjS3+GzSH/jvw+slfRQIIDPkf7WP0D6+/skqedA63UmgX8Dvg68hJRvHBsR10p6BbN0s5T0u8DfAk8DrgbeBVwEPKm02zOB3wFWA1cBXwC6/U+5U97W6l3Aw0j58K2ke1ST9JD8uZ8DfJOUH35B0jERsTMf+3xSfrsd+C7wsoj4sKSTaekaKenV+VxPBn4DvB/4IClvey3wI6WuqdcDZwCPioj72p3LzGwcbb5zhisum39WuPyg6w/oQ3IWzC1zo+kAYHNETLfZdkveXvheRHwuImoRcX/Lvs8FvhAR38kPG28hPZjN5q8j4v6I+DHwY+CRABFxVUR8PyKmcyvhP5EeQrr1LxFxfURsIRUEr4+Ir+XP+Cng0cWOEfHxiLgjX+vvSYXUh5bOdVVEXJoLb+8mFXxPmEdazMbF53Ir2HeAb5EqdAp/GxF35nxjDfBPEXFFRMxExIXADtLf1QnAcuC9uZfApaQKl3aOAw4G/jIi7ouI7RHxnS7T+mLg/Ij4YUTsAM4iteQdUdrnnIi4OyJ+BXwDeFSX54YOeVuZpAngD4BXR8TN+V78R07PC4AvRsRXc97zLmA3UqGv8P6I+HVE3EkqaM6WvlcAb4qITfn8ZwPPlbQsIraRCsPvBj5O6qWxqfOpzMzGUTATtXm/qsYtc6NpM3BA/k+9tUB3UN5e2DjLeQ4ub4+IbZLumOPat5bebwP2BMi10u8mjbvZnfTdu2qOc5XdVnp/f5vlPYsFSa8j1UQfTCp8rqa5AFv+TLXcxctdjcx29ZyI+FqHbeW843DgdEl/Wlq3gsbf4M3RHG3rpg7nPAy4qUNF1FwOBn5YLOSeCHeQehPcmFe3zZ+61M2xB5Aqh67vkL765855z0aaezu0XmO2fOlw4LOSyk8WM6SgNTdHxBWSbgB+C7hklvOYmY2lAGpztlFUn1vmRtP3SLXiv1deKWlP4GTg8tLq2b7Ft5C6RBbH7wbsv8A0fQj4OSli5WrgjUDb8SqLkcfHvZ7UXWnfiNiH1N2yfK3DSvtPkD7jr3udFrMRV847NgLviIh9Sq/dI+KTpHzkkJbxaQ/scM6NwAPVPqjKXP/j/ppUwAEgjw/bH7h5rg/SQ5tJXSQf3GZba/pEyou6SV+7z74ROLnlnq/K4xuR9EpSr4Rfk/LE2c5lZjaWagv4VzUuzI2g3BXxr4F/kHSSpOW5q9ElwCbgY12e6lLgWZL+P6VgJWez8ALYXqTpEe6V9NvA/1ngebq5zjRpDMkySW8htcyVPVbS7+UHxteQCr7f71N6zMbBPwOvkHS8kj0kPUPSXqTKpWngz3Je9Huk7pTt/IBU+Dsnn2OVpMfnbbcBh6pz4KRPAi+X9ChJK0ldQq/I3bqXRETUSGMH352DlExKelxOzyXAMySdKGk5aVzbDuA/ujj1bcD+kvYurfsw8A5JhwNIeoCkU/P7hwBvJ41dfAnwekmPmuVcZmZjJwhmYv6vqnFhbkRFxN+RWr/eRSpEXUGqyT0xj6/o5hxXA39KCiJwC3AvcDvpAWS+Xge8CNhKevC7eAHn6MZlpIAG/0Xq0rSdXbuSfp40fuUu0oPO7zn4idnCRcR64I9IAU7uAjYAL8vbdpJ6CbwMuJP0t/eZDueZIQVROQr4Fany6QV589dJgU1ulbS5zbFfA/4K+DQpv3owKXDSUnsd8FPSuMA7SYFeJiLiF6TC1T+QWvCeRZr6YWenExUi4uekwuoNORLmwcD7gLXAVyRtJVVIHZ8rqT4OvDMifhwR15H+L/iYpJUdzmVmNpZqxLxfVeNJw61ruZvm3aSukr8ccHIWRNLZwFER8fuDTouZmZmZDcajHrkiLv/Sb837uAMOufmqiDi2D0laELfM2awkPUvS7nn8ybtINc43DjZVZmZmZmaLMwotc45maXM5lTTGTqT5o04LN+eamZmZ2RALqOQYuPnqW8tcDrzxC0kbJJ3Zr+tYf0XEH+YoaXtHxIl53MfQioiz3cVyvDlvMjMzM4DaAl5zmes5Q9JKSRfn7VcU86HmIGEXSvqppGslndXNZ+hLYU7SJPBBUhj8Y4AXSjqmH9cyM+uW8yYzMzODHM1yAa/ZdPmccQZwV0QcBbyHFCgL4HnAyoj4H8BjgT8uCnqz6VfL3HHAhoi4IUfquojUXc/MbJCcN5mZmVm/dPOccSpwYX5/KXBinns0gD1yVOLdgJ2kiPSz6teYuUNoDge/CTi+084rtDJWsUefktI/M0evbLt+8rqFRO43q77iO9/Nd3w797EzdvR8YvhFmlfeBM6fzIbFCORPZraUAmYWNmTuAEnrS8vnRsS5+X03zxn1fSJiWtIWYH9Swe5U0vQ6uwN/HhF3zpWYgQVAkbQGWAOwit05XicOKikLtuX9R7Vdv/cpG5Y4JeNjy7p0z32Pl1Zx3wvd3P8r4vJ+JafvnD/ZQjh/Goxxy5/MrDeC7sbAtbG5T1MTHAfMAAcD+wL/LulrEXHDbAf1q5vlzcBhpeVD87q6iDg3Io6NiGOX074G2cysx+bMm8D5k5mZ2egTMwt4zaGb54z6PrlL5d7AHcCLgC9HxFRE3A58F5iz0NivlrkrgaMlHZkTfFpO4Egpav9aawU7ca1t73R7z32vF6/dvR7i7/JY5E3g/GmQnD8tnRHLn8xsCQVQ6/3MBN08Z6wFTge+BzwX+HpEhKRfAb8LfCzP73wC8N65LtiXwlzu//kq4DJgEjg/Iq7ux7XMzLrlvMnMzMwKXbS0zUun5wxJbwPWR8Ra4DxSgW0DcCepwAcpCua/SLqaNL/zv0TET+a6pqow//Nq7RfDOCalVad++6NWS1uFWs9u7+lshuV+D9ps93q2e3hFXM49cefQBxhw/tS8f9U5fxov454/mdnCPewRK+KiL/7WvI97xANvvqpPY+YWZGABUMzMzMzMzAalFsNfp+PCXA+11nT3ona2Ssb184x7Dfm4f/5R4fxpuDh/6s64f34zW7ig990sB8GFOTMzMzMzGyuBmOlbYP+l48KcdTRXzfAgx6bMd7yPmY0W509m40nSBcCmiHizpCcCH4mIhy7BdQM4OiJ2yVQkfRP4eER8ZJDpsPkbhW6Ww18cNTMzM7PKkHSjpPsl3SvpNkkXSNqz19eJiH/vpiAn6WWSvtPr69twK7pZ9nieuSU39i1zvay9HbVa2NZ7UoUocYMwrp/bBs/5U2fOn5Jx/dw2FJ4VEV+TdAgpTPubgTPLO0haFhHTA0mdGWImhr9da/g/gZmZmZlVUkTcDHwJeDikboKSXinpOuC6vO6Zkn4k6W5J/yHpEcXxkh4t6YeStkq6GFhV2vYUSZtKy4dJ+oyk30i6Q9IHJP134MPA43JL4d1535WS3iXpV7n18MOSdiud6y8l3SLp15L+oIuPerik7+Z0fkXSAaVznZA/192SfizpKaVtL5d0bT7uBkl/XD7pfNIh6ZuS3p6vda+kL0jaX9InJN0j6UpJR5T2f5+kjXnbVbnbarHtbEmXSro4p+2Hkh7ZxX0YGgHUmJj3q2rGvmWusJBa69aa0FEfJ1HFmt+luOdV/Nxb1h1VyXRZfzh/mlsV/x6cP5mlAhZwCvCZ0urnAMcD90t6NHA+8CxgPfD7wFpJDyU9b38OeC/wAeBU4JPAO9tcZxL4N+DrwEuAGeDYiLhW0iuAP4yIJ5QOOQd4MPAoYAr4V+AtwFmSTgJeB5wI/BL45y4+6ouAk4GNpMLr64Azc8vkF3OavpzP+WlJvx0RvwFuB54J3AA8CfiSpCsj4ocLTMdpwNOBzcD38utPgNNJ9/mtwMvzvlcCbwO2AK8GPiXpiIjYnrefCryQ9Dt5NfA5SQ+JiKku0jEUqthtcr7GvjDX7X845f+M/Z/UeKj677nTJNA2Opw/WSdV/z07fzLSg/80qaDwReBvStv+NiLuBJC0BviniLgib7tQ0huBE0iFueXAeyMigEsl/UWH6x0HHAz8ZanrZttxcpIErAEeUUrH35AKdGcBzwf+JSJ+lredTSrUzOZfIuK/8v6XAM/O638fWBcR6/LyVyWtJxVwL4yIL5bO8S1JXwGeCPxwEem4Pu//JeCYiPhaXv4U8H+LHSPi46Xj/l7Sm4GHAj/O666KiEvzse8GXkv6vfz7HGkYChGj0c1y7AtzZmZmZtZzzykKEW1sLL0/HDhd0p+W1q0gFcwCuDkX5Ao3dTjnYcBNXY7BewCwO3BVKtcBIGAyvz8YuKqLa5bdWnq/DSgCvhwOPE/Ss0rblwPfAJB0Mqm17CGk4U+7Az9dRDpuK72/v81yPRCNpNcBZ9C416uBA0r7139PEVHLXVoP7iINQ6Pmlrnx4VrFaupH96Uq/65n+7yuCR9f/l1Xk/Onztuq/DlsSZQLZxuBd0TEO1p3kvRk4BBJKhXoHghc3+acG4EHdgiqEi3Lm0kFm4flMX2tbiEVDgsP7PxR5rQR+FhE/FHrBkkrgU8DLwU+HxFTkj4H9RJGL9PReu0nAq8ndeG8OhfW7ipdm/K1JU0AhwK/7lUarDeGv23RzMzMzIbVPwOvkHS8kj0kPUPSXqTxXtPAn0laLun3SN0p2/kBqfBzTj7HKkmPz9tuAw6VtAJSK1O+7nsk/RaApEMkPT3vfwnwMknHSNqd1HK2UB8HniXp6ZImc7qeIulQUgvkSuA3wHRupXta6dhepqPVXqR7+xtgmaS3kFrmyh4r6fckLQNeA+wAvt/DNAxUmppgYt6vqnHLXB/MVePoUNILN26BBBbDNeHWjvOn/nH+1D3nT1aIiPWS/ogU4ORoUovZd4BvR8TOXID7Z+DtwDqaA6mUzzOTuzK+H/gV6Vn9X4HvkoKiXA3cKqkWEQcAbyAFPPl+jjx5M/Ah4LKI+JKk9+bjaqRpFV68wM+3UdKpwN+RgrfMkAqe/ycitkr6M1KhbSXwBWBt6diepaONy0gBWf4LuA94D83dXwE+D7wAuBDYAPzeKAU/GZWpCVyYMzMzM7OeiYgjZtm2yyCliPgyqWDRbv/1wKM7bPsmqetfsfwrUqTM1v12As9oWbcdeGN+tTv3OaSIl4Xz2+2X931Ky/IFwAWl5SuAJ3c49oPAB2c592LS8eaW5a8BR+X3M8Af5Ffh71pOuT0ifr/T9YZdMTXBsHNhbgBcA1ktw/D76EWNv2vCrRv+XlTLMPw+nD+Z2bCa2bVuYegMf3HUzMzMbMhJOknSLyRtkHTmoNNjNuoCecyc2TAbpprfYUqrmS3eMP3ND1NaqypPeP1B4KnAJuBKSWsj4prBpszGVUScPeg0LIWax8yZmZmZ2SIdB2yIiBsAJF0EnAq4MGfWJ0U0y2HnwpyNHdcim1lVOX8aW4fQHElwE3D8bAes0MpYxR59TZTZMNnKXZsj4gHd7h9oJMbMuTBnZmZmNgQkrQHWAKxid46f+F8QrfNhm40ZpQLZ12qfumm+hzqapdkQGYUa73LEt1H4PGaWjMLfs/OnRbkZOKy0fGhe1yQizgXOBVit/cIFOTMWXKERgeeZMzMzM7NFuxI4WtKRpELcacCLBpsks1EnaribpdmSKmp7ezGv0TBybbdZdTl/cv60UBExLelVwGXAJHB+RFw94GSZjbRgNFrmhv8TmJmZmQ25iFgXEQ+JiAdHxDsGnR6zcdCPeebmmjNS0kpJF+ftV0g6Iq9/saQflV41SY+a63pumbOR59piM6sq509mZoMRiFqPo1l2OWfkGcBdEXGUpNOAdwIviIhPAJ/I5/kfwOci4kdzXdMtc2ZmZmZmNnb60DJXnzMyInYCxZyRZacCF+b3lwInSmotVb4wHzsnt8yZmZmZmdlYCaDW+zFz3cwZWd8nj5fdAuwPbC7t8wJ2LQS25cKcmZmZmZlZdw6QtL60fG6eNqQnJB0PbIuIn3WzvwtzNlTGNUqcmVWf8yczs2EiZhY2NcHmiDi2w7Zu5ows9tkkaRmwN3BHaftpwCe7TcyC2xYlHSbpG5KukXS1pFfn9ftJ+qqk6/LPfRd6DTOzhXD+ZGZmZrMpulnO9zWH+pyRklaQCmZrW/ZZC5ye3z8X+HpEmvlc0gTwfLocLweLa5mbBl4bET+UtBdwlaSvAi8DLo+Ic3I4zjOBNyziOmbz5ghxY8/5k1WW8yczs2pYYMtcR53mjJT0NmB9RKwFzgM+JmkDcCepwFd4ErAxIm7o9poLLsxFxC3ALfn9VknXkgb0nQo8Je92IfBN/LBkZkvI+ZOZmZnNJkL9CIBCRKwD1rWse0vp/XbgeR2O/SZwwnyu15Mxc3myu0cDVwAH5gcpgFuBA3txDTNo1Gh7bIp1y/mTLRXnT2Zmw2WmD4W5pbbowpykPYFPA6+JiHvK0yREREiKDsetAdYArGL3xSbDzGwXzp/MzMysnQBqPe5mOQiLKsxJWk56UPpERHwmr75N0kERcYukg4Db2x2bQ3ieC7Ba+7V9oDKbr6qPRSlq7KuezlHg/Mmqpup/986fzGy8aLxb5vJM5ecB10bEu0ubiggt5+Sfn19UCs3aaH3YGLZuTX5o6i/nTzZIzp/MzKovRbMc75a5xwMvAX4q6Ud53RtJD0mXSDoDuIkUXtPMbCk5fzIzM7NZzSx8lrbKWEw0y+9Ax46mJy70vGYLMSw1yA6QsDScP1mVOH8yM6ueQGPfMmdmZmZmZjaUauPcMmdmCzcsNfVmNn6cP5nZOIiAGbfMmZmZmZmZDR93szQzMzMzMxsyacycu1mamZmZmZkNnZlxnzTczMzMzMxs2IzKPHPD37ZoZmZmZmY2htwyZ2ZmZmZmY2Y0xswN/ycwMzMzGwKSDpP0DUnXSLpa0qvz+v0kfVXSdfnnvoNOq9k4qKF5v6rGhTkzMzOzpTENvDYijgFOAF4p6RjgTODyiDgauDwvm1kfFfPMzfdVNe5maWZmZrYEIuIW4Jb8fquka4FDgFOBp+TdLgS+CbxhAEk0Gyuj0M3ShTkzMzOzJSbpCODRwBXAgbmgB3ArcOCg0mU2LtI8c9VraZsvF+bMzMzMlpCkPYFPA6+JiHukxgNlRISk6HDcGmANwCp2X4qkmo20Ko6Bmy8X5sxG3JZ1RwGw9ykbBpwSM7Nm45g/SVpOKsh9IiI+k1ffJumgiLhF0kHA7e2OjYhzgXMBVmu/tgU+M+uO55kzMzMzs64pNcGdB1wbEe8ubVoLnJ7fnw58fqnTZjaOajEx71fVuGXOrEe2rDuqUrXLRY13p+UqpdXM+sv5U2U8HngJ8FNJP8rr3gicA1wi6QzgJuD5g0me2RgJj5kzMzMzsy5FxHeg4yCdE5cyLWbjLvCYOTOrkNaa7fnuP8I14WY2YM6fzKyK+tEyJ+kk4H3AJPCRiDinZftK4KPAY4E7gBdExI152yOAfwJWAzXgdyJi+2zXc2HOzMzMzMzGSj8CoEiaBD4IPBXYBFwpaW1EXFPa7Qzgrog4StJpwDuBF0haBnwceElE/FjS/sDUXNd0Yc5skco1yPONzNaptnohtdCdjum2Rtw14Wajx/mTmVlnfWiZOw7YEBE3AEi6CDgVKBfmTgXOzu8vBT6QgyM9DfhJRPwYICLu6OaC1QvJYmZmZmZm1kfFpOHzfc3hEGBjaXlTXtd2n4iYBrYA+wMPAULSZZJ+KOn13XwOt8yZLVAvaoqLY+Y7nmQh1ygspCbcteBmw8X5k5nZ3BYYAOUASetLy+fmOSAXaxnwBOB3gG3A5ZKuiojL5zrIzMzMzMxsfMSCu1lujohjO2y7GTistHxoXtdun015nNzepEAom4BvR8RmAEnrgMcALsyZ9UMva4OXoga89VqtZru2x6uYDRfnT2ZmA3ElcLSkI0mFttOAF7XssxY4Hfge8Fzg6xERki4DXi9pd2An8GTgPXNd0IU5swoZ5EPIfLo7+eHJbPw4fzKzUdKPaJYRMS3pVcBlpKkJzo+IqyW9DVgfEWuB84CPSdoA3Ekq8BERd0l6N6lAGMC6iPjiXNd0Yc7MzMzMzMZOP+aZi4h1wLqWdW8pvd8OPK/DsR8nTU/QNRfmzKytdrXZnWrDXRNuZkvJ+ZOZLVYRzXLYuTBnZmZmZmZjJ0agMOd55qxStqw7akkG2Vt/bVl3FDNHrxx0Msx6yvnTaHD+ZGaFGpr3q2rcMmdmZmZmZmMlFj41QaUsumVO0qSk/5T0b3n5SElXSNog6WJJKxafTOukCjXFVUiDWTvOnwarCnlDFdJgZmbVFKF5v6qmF90sXw1cW1p+J/CeiDgKuAs4owfXMDNbCOdPZmZm1kYKgDLfV9UsqpulpEOBZwDvAP5CkoDfpTE53oXA2cCHFnMd21VrTXOxvJRRuvpxTUcZGw17n7KBydgx0DQ4fxoc509WZVXIn8ysGqrY0jZfix0z917g9cBeeXl/4O6ImM7Lm4BDFnkNM7OFeC/On8zMzKyNfkwaPggLLsxJeiZwe0RcJekpCzh+DbAGYBW7LzQZNkCupR4/xe98EC0t8+H8yar63bT+GZb8ycwqIlIQlGG3mJa5xwPPlnQKsApYDbwP2EfSslz7fShwc7uDI+Jc4FyA1dpvBG6lmVWI8yczMzObVRWnGpivBRfmIuIs4CyAXPP9uoh4saRPAc8FLgJOBz6/+GRaYa6obK6RtKVQ9e+X86fBcP5kVeDvl5l1IxiNMXP9mDT8DaRgAxtIY1TO68M1zMwWwvmTmZmZ4WiWJRHxTeCb+f0NwHG9OK+Z2WI5fzIzM7N2RmHMXD9a5szMzMysA0mTkv5T0r/l5SMlXSFpg6SLJa0YdBrNxsEoTBrek5Y567+5xqJ02t9jB8ys35w/mc3bq4FrScGZAN4JvCciLpL0YeAMPAemWV9FeMycmZmZmc2DpEOBZwAfycsCfhe4NO9yIfCcgSTOzIaOW+Yqbr413p2Odw14//ge27hy/lR9vseV9F7g9cBeeXl/4O48ZQrAJuCQAaTLbOxUMaDJfLllzszMzGwJSHomcHtEXLXA49dIWi9p/RQ7epw6s/ETMf9X1bhlzqxLi22FMDPrF+dPQ+PxwLMlnQKsIo2Zex+wj6RluXXuUODmdgdHxLnAuQCrtV8FHyvNhovHzJmZmZlZVyLirIg4NCKOAE4Dvh4RLwa+ATw373Y68PkBJdFsbATzj2RZxcKfC3MVtWXdUT2tae31+WxXvr82Lpw/DR/f38p7A/AXkjaQxtCdN+D0mI2FWMCratzN0szMzGyJRcQ3gW/m9zcAxw0yPWZjZ0SmJnBhroL6WYPqyGZmthjOn8zMbGRUsaltnlyYMzMzMzOzsTMKLXMeM2dmZmZmZmOnH1MTSDpJ0i8kbZB0ZpvtKyVdnLdfIemIvP4ISfdL+lF+fbibz+CWuQpZygHq7s7UH76vNqqcPw0/31czs4ag9y1zkiaBDwJPBTYBV0paGxHXlHY7A7grIo6SdBrwTuAFedv1EfGo+VzTLXNmZmZmZjZeAgjN/zW744ANEXFDROwELgJObdnnVODC/P5S4ERJCy5VujBnZmZmZmZjZ4HdLA+QtL70WlM65SHAxtLypryOdvtExDSwhTQlCcCRkv5T0rckPbGbz+BulmZmZmZmNn4WFs1yc0Qc2+OUANwCPDAi7pD0WOBzkh4WEffMdpALcxUwyMlct6w7yuMn+qDT79T32oaN86fR4/zJzAxA/YhmeTNwWGn50Lyu3T6bJC0D9gbuiIgAdgBExFWSrgceAqyf7YLuZmlmZmZmZuMnFvCa3ZXA0ZKOlLQCOA1Y27LPWuD0/P65wNcjIiQ9IAdQQdKDgKOBG+a6oFvmBmiQNd5ljnDWP76nNqycP40+31MzG2vR+2iWETEt6VXAZcAkcH5EXC3pbcD6iFgLnAd8TNIG4E5SgQ/gScDbJE0BNeAVEXHnXNd0Yc7MzMzMzKwHImIdsK5l3VtK77cDz2tz3KeBT8/3ei7MmZmZmZnZ+FlYAJRKcWHOzMzMzMzGUM8DoCw5F+YGoCpjUVp5bErv+Z7asHH+ND58T81s7LllzszMzMzMbAi5MGfzUdUab+tOUXvt36ONIn+vh5vzJzOzeQqg9/PMLTkX5szMzMzMbOyEW+ZsFLXW7Ho8hZlVRd/yp4nJxvvaTG/OaWZm1ebCnJmZmZmZ2RByN0vrxrCPYXDEM7PRNfb5k9J/5JoQUYumdSPR/8bMzDrSCGTzE4s5WNI+ki6V9HNJ10p6nKT9JH1V0nX55769SqyZWbecP5mZWV9NTDZeNnxiga+KWWzL3PuAL0fEcyWtAHYH3ghcHhHnSDoTOBN4wyKvYxXgsXQL59bNgXD+NEbmnT+1PHxp2bL61LExU2veN5qXYyaPqRuRljvnT2bz5Bb9EaGR6Ga54JY5SXsDTwLOA4iInRFxN3AqcGHe7ULgOYtLopnZ/Dh/MrOqcq8BswoZ85a5I4HfAP8i6ZHAVcCrgQMj4pa8z63AgYtL4vAa9rEoc3FtrlWY86c5OH9qtktrHMBEqrGdWJ2eq2t33ZV3ruD/5jZM3GtgmJVa9Tu26Jda80etJX/kjMCvZTFj5pYBjwE+FBGPBu4jZT51EdGxDCtpjaT1ktZPsWMRyTAz24XzJzOrHPcaGC0xU6u/6ibExL77MrFvblyNcEGuysa8ZW4TsCkirsjLl5Ielm6TdFBE3CLpIOD2dgdHxLnAuQCrtV8Fb83CjXqNd6txG0s36p9vRDh/6mBc8yflkGWrT7mh7X4xPYUmc417/jmx155peWpnOseKFWnfmZZ56Cr0oOb8qfIW1WtA0hpgDcAqdu9/as1GWTDeY+Yi4lZgo6SH5lUnAtcAa4HT87rTgc8vKoVmZvPk/MnMKmpRvQYi4tyIODYijl3Oyr4n1kqkRpCTLKanUpfKqKVu2RNKlUBTO2FqZ6oAKo7T8BcaRpFi/q+qWWw0yz8FPpH7fN8AvJxUQLxE0hnATcDzF3kNW4TWWtqlqJX3WDqrCOdPFbeU+VPk2td71j0IaN9CV0SlK1rxalu2pg157JyKhzFNFAf0K7k2uhbVa8AGqN4C3xLdtl2+Uc4znF9Yny2qMBcRPwKObbPpxMWc18xssZw/mVnVRMStkjZKemhE/IJGr4FrSL0FzsG9BsyWTgVb2uZrsS1zVlKlsShVaBUbt7F0ZlXm/KlZ0UJXWP2MG1GuTS9q1etyzXvkCnYtT/91qrXLVQ6CEHmMnVkH7jUwzCIaXSY10T7fKOUZ7fKLmKk5n7CecWGuhwbRpXGuNAxSldIyasrfLd9n64bzp2at3Sw1OVnvDrVLIa3T6PKJlg0VCoQySM6fZudeA0OulD9oQm3zjbZ5Rjm/cF5RGVUcAzdfLsyZmZmZmdn4GYFoli7M9VGnGsl+1Ih3W/tZ7NfPWnnXxPbf3qdsqP8OHXDGFmLc8qdlE6kL5J7P2gTAxD77ABDT00CuVS+mJvhvBwBQ23MVAJO3353W70jdomr3pMAoWpb+Cy2mKiimLiAvF4ERqLVMZTDinD/ZyJmYrE9dMrHnHnPmG+U8o5xfNOUV5XxizPKIyqjovHHz5cKcmZmZmZmNHxfmbCF6OXalCrWdVUjDOOr0PfLvwxZjVPMnLU+tZlqxPP3cfbe0w86p9LM2A1Optr34+ctT06ThB/w4Tc68+r/uScdu3w40Ap7U1fJyMYYmV9hHEZJ8jMbJOH+yUaLJyea8Y458o5xntM0varWmfGIc84iq6MeYOUknAe8DJoGPRMQ5LdtXAh8FHgvcAbwgIm4sbX8gKcLt2RHxrrmu58KcmZmZmVmrHNREy5elycCBmYP3RztzN8ud02jrNgCmHrAagL1+Cd993z8B8LB/+BMO/k7urr19mom77wMgdlvBxK9uSdeoBTP35m6WxfAtd7tcOj0uzEmaBD4IPJU0r+SVktZGxDWl3c4A7oqIoySdBrwTeEFp+7uBL3V7TRfmKmAhNeGLrd3sxdgU17BWi38f1g/Dlj8VE/euPvn6YkX6kcODlx/IgDkfyqD5wQxoejgDmh7QgKaHNMAPajh/MrOK6n3L3HHAhoi4AUDSRcCppJa2wqnA2fn9pcAHJCkiQtJzgF8C93V7QRfmzMzMzMzKylOU1GqNoCdTM8Sy1EUylq9kcsdU02H7XbON//5PqeKntkewY//UPTMmVrBsv5UArLptW72bJbSp3JHc5XIJKPrSzfIQYGNpeRNwfKd9ImJa0hZgf0nbgTeQWvVe1+0FXZiroKrWYFY1XWa2dKqaD+ySLrVO/J0n9C49kAFzPpQBTQ9mQNPDGdD8gAazP6SV0+aHNbPBK+cV5UnANQHRGOcW96aGEt10CxMrc+Ta5cuJVen98k13pHXLJnngl9JxU/us5N6DUn4xtYfY7xcp/9m5326svKlx2Yk8Hq+Yiy527hzbaLhLbmFTExwgaX1p+dyIOLcHqTkbeE9E3Ns63+lsXJgzMzMzM7Pxs7A6tc0RcWyHbTcDh5WWD83r2u2zSdIyYG9SIJTjgedK+jtgH6AmaXtEfGC2xLgwZ3Oqak28mY0PtfSFmcjLe56cB7ZNpNCRTbXq0FSzDs2168CcNexAUy070FTTDjTVtgNNNe7QvtY9Jc3zS5ktudYWj9bWuLJiDjkgcku6ZmaIHKFSK1eg7TvzvvnY6RmUW9VW3HE/Ew9Ij9qqwfRu6XyT22eguGaoce5SRFzluTEj3OWyn/rQzfJK4GhJR5IKbacBL2rZZy1wOvA94LnA1yN9CZ5YT5d0NnDvXAU5cGHOzMzMzMZFMSVAUZgqFdg0OVkv7E2s3osoClc7dtSnI6jt2FHff6IW9eO1794ARFF5A2i6xn4/uisdt2oZtVWNbfy3B6R9tm1nYtm9aV1RaLt/e3232L4DTaY01btepgUX8nqhx7cwj4F7FXAZaWqC8yPiaklvA9ZHxFrgPOBjkjYAd5IKfAtWycKc56NZOnufsmGXiHG9vu/+fdoo8fd56bTLn1afckN6U5+jabJpeRelBzVorl0H0EW5tex0ta1hB5pq2YGmmnZoqW2Hphr3pmu2zkM3UUu17mmn9uk3M7P+6E8AFCJiHbCuZd1bSu+3A8+b4xxnd3u9ShbmzMzMzMx6rWOXSkiBkoru0NMtXaCLaU5K3TRjZqZxvqI1bfWeTK1OXbKX39NoxdNMEMt2DWoRq1agnSvzNfME5JOT9aBN9fNbf4xAPVqlC3NzzTHkmvHe6leLXHnZvzMbFc6flkZ93rhn3JiWi5a2lha3Rgtdo4sU0NxNCupdpbZdul/afSqdf7cLpuD3Ustbu+5SkLpMAU3dptLP5v3K3aeAObtQpfS36UaVVjQfa2aLEjNFIS23vJfzkpUr639rsW0bWpHG0cbOUqTbycmmv8eYyYWuvKztO1h585b6dpWOXVace8UyplevaqyfKMb4pu0TUuO8y5fV56ys3b+9nifEDI6Ma0DFC3NmZmZmZmZ9MQLlYBfmrGctCK1jidwyYWaLtfrk6wHQsvzfVaexcUULXIcuUhOf2x2AmWfcA8Buz761aXtMTqYacGjqLgW07TIFqdsU0LbrFFCPjtm2C1VOs7tQmVVIRCMvmZyEohVPquct1GqN/Gii0YJWbx3bsROKvGT5MiK/19Q02jnd2Hd5yhd27r2CieliAG76ObnbKjSV9o3p6RRGI1+P2hzzWNq89GPM3FJzYc7MzMzMxkNLl8Smrs1T042uzTM1gtRFUpMT9eO0cmV9d61aCVNpn9iWumnHzAzK71m+DO25R3q/c6oeBEm1YOLOrXmXSWr7pIqjWJYKajsO35/Jbem8k/fuUV+v6zfWC5gxU67YKk3B4i6XY8eFOVu0zWseB8ABp3xvwCkxs1E16zgXSGNdoGm8C1Af8zJzyn3N+xfHzzH2BWga/wLNY2CgeRwM0DQWBtqPhymuV28NLI+JgeZxMeCxMWZm/TACWaoLc2ZmZmZmZVFrdOueKLWCzdE1WpOTjX1mZupTnDA52ahEmt7ZOGB6Bt2flmP3VClVWya0Is9ft9tyJrbl7aW0aEIt886NQKlkqfVpaoKlVsnCXDHWqt/zn1lvHHCuW+RsfDh/qqjiQaY83gWax7xA07gXYO6xL9A0/gVoGgMDNI+DgaaxMEDb8TDF8Y1xdNTTkdLncTFmZn3nwpyZmZmZ2ZAqtcDF9FRTkCVNlMaitVYQkacWmczHFmPqytMKrFhOFNOiaALuzdOULFtWnyaFUvCliXyuFVsm0FS6zsQd9xAr8/QnExP1ee5iZqY+dUtM19wVe6FG4HZVujDnmm4zqyrnT0tstqAFkAIXpA3pZ36YagpgUDpPOYgBdA5kADQFMwCaAhoATUENgKbABkDb4AYAk9ummLx3j6Ztun5jTn+7IAfQFOig9HlseEj6c+APSY+RPwVeDhwEXATsD1wFvCQidnY8ifVHU0FOjWWpJbJlLjiVA6MUhboiLyLNTzexR4qkGzO15rG65XG/RR5yX85rVi5vavEvegEwOdGY827GrfaLJUajm2WHGM9mZmZm1kuSDgH+DDg2Ih5O6mB7GvBO4D0RcRRwF3DG4FJpNkZiAa+KqXTLnJktXuv8f/0+zqwSiha6dgEMYM4gBlCKmFkOZgDNAQ3KP6dbGlLyfu2CGwBoxSTaLXWfagpwUEpvMQ/dLi2RI9IiN6b50zJgN0lTwO7ALcDvAi/K2y8EzgY+NJDUjYuiW6ImGvlFsdyq3LVy585GfrJzqn6eRn4x0TwWd/v2xrEzjetoZRpTy+RkfRxuYeLXv6lHuI2pnbB8RT1txbmj3DJXi+bPYN1xABQzMzMz61ZE3CzpXcCvgPuBr5C6Vd4dEUX/vE3AIQNK4nhqV4Ara60Mmu1UUhoTB7sUEutj3CIa3TOlRlClHVP16wWlYE7FeWYrsBWfIdz9cl5cmDOzUdEandGs0lpa3mJ6qmm50BTAANoGMYDOgQyApmAGQHNAA2gOagBNgQ2AtsENADQ1w8Qd96RzlgIcNF87t+6VAx2kFTnhI/Ak0oVRyZ8k7QucChwJ3A18CjhpHsevAdYArGL3PqTQbMyMQBbqwpyZmZnZ0vhfwC8j4jcAkj4DPB7YR9Ky3Dp3KHBzu4Mj4lzgXIDV2m8EHkP7SKWu1LNVerS2nrXpgh07dzbtU3S9rnenpFQJtGI51FqmRCkUx2kSrUpTlLCsEQgl8ntNTqL6VCfL6xVDUQ7QUk5TKc0Rat/SWP6cxbmlsakQ6sTdLM0qbvOax43tPHjd1mSPSo23jbldWuTUvF4t497q8851F5UOqEeRa4pOB80R6srLhTaR6iDPT1dcsxStrnytUY1YN8b506+AEyTtTupmeSKwHvgG8FxSRMvTgc8PLIWjplxYaS1ctWgqyHXqelneZ2KiHgW3sW6yVJibaN8bIGqNrpuTk433+WesWoHum2mkv8hDJicgj7WLbdva5w9zdRmFOe/DWBn3wpzD61rVjWtBDjoHBuj24WhIAwvUOX8aUWoppLWOIen0INParbKo1Z4jkAE0TywONAU0gOagBtAc2ABoH9wAoBYpuAE0BTgoXzNaH9aKQChDHuxgXPOniLhC0qXAD4Fp4D9JLW1fBC6S9Pa87rzBpdJsTFQ0OuV8LbgwVwqve0xE3C/pElJ43VNI4XUvkvRhUnhdR2QysyXj/MnMqioi3gq8tWX1DcBxA0jO6Ct3JYxo3ypVb8FvqQzKlSblaLIqN7zXavXulfWxsjNbm8bLarfdGtcuTE7WK5Q0NYWKeSXLFT95jC47GvWNta33wn2NOTOjTW8AKXaNwAtErVMFWJfdUUeUu1k6vK7Z0BjB7kpzcf40DrrpUgTzikZXP3XxkNMuMl3p2k0R6tKbpuPbRaoDUrS6ehfNLqLVtbn2qESuG8P8yQZhtoJcq6YxZqX35WlC2hR+YqbWaN3XRCP/ACgKa6UCWkSgYhqTYt3yZU3dMOtBlyLq3TpjplYag6dG/lNO41w9GIa8hd+SBU8aHhE3A0V43VuALcwjvK6kNZLWS1o/xY6FJsPMbBfOn8zMzGxO4zxp+GLD6zoik1l/LbSme1jHopQ5fxpSrbXm3Uagg0YrWYfJwJui0ZX2nzMqHXSOTFcoRagD2kapg+ZIdUCKVrc8X6NNtDpoiVhXulYjrS1jCFt1imBXXh6Acc6fbAnMEqWxqdUMmv+mmroltp+IO2Zm6udoDZJUnK9+jWXLiFV5POzyZWjrtvR+xfJGt8yolQIyFdOWTDcmDb/vvkZaa6VJx5cva3TnlOppj51TjSBO9XswiSjyseWNsbitE6bXTz4+US7HvZvlosLrmpn1kfMnM7NxVq60aFcRUxRiao0CzS4Bh9oWaGr13s2anGwUBsvdL4vCXK1UOFw2Ablgp+mZRoFrutyFs4iQu6xxbDk67sREY59aqRDYFBFzjkiV7lrZbMwLcw6va1ZBHnsCOH8abvMIJV7frfUBZq6xdK37dwoxDs1hxqHjxOP1h6SJlv3ahB0HUujx4rOWQ49Dc/jxdtcqdDtmECoRjtz5k5lVRkW7Tc7XYsbMXQEU4XV/ms91LvAG4C8kbSCF/3Z4XTNbUs6fzMzGWETj1bIctZbX9BQxPU1MTzcf16mbYUSq4KnNEDMzxNR0++6Wme7fkV47plOL3PQMMTlB7Lk7sefuaPVexL6r02u3lem19V5ix4702jmVKnNmZtBuq9Cee6TXHrujVSvRqpVNwU8kpYqjiQkkpeWWz0zUSq82n3dculgu8DXneaWTJP1C0gZJZ7bZvlLSxXn7FZKOyOuPk/Sj/PqxpP/dzedYVDRLh9c1G7xe1XSP2lgU509DrNw9qnWc1y77dggpXmgTWhxawotD5xDj0BRmHGgfary0XznkONA+7DikcTM7msfE1bbem96Uwo+nH+0nJC8iabYLRQ6zhCNPBzcv9+EBzvmTVVY9guwivvdRaxMZchImS/lSfq8dOxut9BLK81PG5ARR5DFFoXBiskMEzZlGHlAa15YKcDl67sxEo7to60TltqseZ3tKg6c/CDyVFGjtSklrI+Ka0m5nAHdFxFGSTgPeCbwA+BlwbERMSzoI+LGkL5QCt7W14JY5MzMzMzOzYaWY/2sOxwEbIuKGiNhJGtZxass+p5KmR4LUi+hESYqIbaWC2yq6LGoudp45MxsQjz2xsTBXi1wnrbXQrcstLXWd5ouCNtHvlrX819lm7qh0ylxr3m4OKUi19EVrXnkeKWieSwqa5pMqn3uXzzPb/FFLOLeU8yerrFoP52aMAEotcsXq+7cDKe+I6fxsPjlRj3QrqRHtdsVytLNlvG7UoMh/li1rtLxLjb/jHTsa65cvq+eVmpyE7Sk/qZWj4bZrgSznr50iWI56ZMvef7RDgI2l5U3A8Z32ya1wW0hDPzZLOh44HzgceMlcrXLgljkzMzMzMxtHC5tn7oBiLtr8WtOz5ERcEREPA34HOEvSqrmOccuc9VxRI+sxDr3Xj9pu/55s4LqY92zWuaGgzVixIvBBcyvUPeseBMDqZ9zYdN5O80U1XTu3yJXnjQKa546C5vmjoP0cUpCCEtx3X/PnqjWntxin1zSfVOnzRr5Wu3mlgM5zS5XT12qB89A5f7KxVIxrnSm1wNcj2aoxZm758sbf1MxMIw8rjw0u/s5Xrqz/bTe1JGqiOe8r9xYoj5PraoqFlvWj3PrWSXfdJtvZHBHHdth2M3BYabndNEjFPpskLQP2Bu5oSlrEtZLuBR5OisbdkQtzZmZmZmaLUS7U1QtWk6iWu0zvnGoqiMU9KcCS7irNVVcUAnfbDRXdMCdX1CtYYss9jeOnp+vXUTkQiyZgeWvF0iK6lo56Ia/3H+9K4GhJR5IKbacBL2rZZy1peqTvkaZL+npERD5mY+56eTjw28CNc13QhTlbsLlqYd1C13vle7nQWnD/PqyyWluE5prot7TcOtlv0QLXyT1fPAJoaaFrN/kvtJ0AGPIkwNA0EXA6V5vJgKF5QuDifC3RKevR7soTA0Pz5MDl/eaaILie2P6Pk3P+ZGbDZoEtcx3lgtirgMtIXSTOj4irJb0NWB8Ra0nTIn0sT5N0J6nAB/AE4ExJU6TBmH8SEZvnuqYLc2ZmZmZmvRDRqBCamoIVuVv2RCN4Sezc2WjJm55OQU4Aim7R5dawZY2AJtBScVXsV4vGscuX1btj17uRF/PJ2a76cFsiYh2wrmXdW0rvtwPPa3Pcx4CPzfd6LsxZR70a/+AWuv4o7me3vyfff6usLsZztM4Td88XD2vZtXXOtO4u3dRCVwwpa20ta6H784NVMQYut8hFMbfUnrun9TnNsTJ3eSrGyt21JZ9Iu4x5026r6tvSwc0fJFqi3ql1PF6taKlsM7/eLhE+uxhHs0DOn2ycxVSKIhkzk/X55JhQo/tlazTeYqxcjnkb09ONAuH2rY3xutPTTa30jb9/NSLflgp+Rb5ClKJqWpNet8wNggtzZmZmZmY2XhrRKYeaC3NmizTolkfXaNtYaWld2qVFbr6nK44vBxCIlnmjJkuBBUrLKuaVK8avFV2bilaxYuxcUetetMxNTO76WeotaLnmvh69s7mFrlwTDxAz+RrlSHYl9647HIA9T/7lrh9+CTh/srEWNajnUZPt58ecUGP+yJx3TExMEMUY3Qk18oqWXgP1uSwnl9XnpUvLuXtlY8dG3uDuls1G4Ha4MGdmZmZm1msRje6N5S6SlLpzz7BLF8jaPVtL4+Fqu1QY1Y8vKnV2TjWCJc3M1At59Uqecnfr1sBSY1y4E+5maSOqH3MFFecd5lrabqJ3DvPnM6u0XEtd/zvs8X/A96x7EKtPuSEvtdR+378dKNV2Fw9eRQvdqjTOrd5qVoQUz/POqWWcW72WfabWCHxQRKUszlHU4O/Y0by9mHeuJcDB9CV7ALBtKl2zVm9xLH2+k69vvkZhrnnlFjjvnJmZ9Z8Lc2ZmZmZm/dRSiVIOTlQELqq3wEU09p+YaLS6lfaJCFSsrs0x9ciEmgI7NQeUKh07jhU2I/CRXZizJdU6vmzzmscBcMC53xtYmrrVTXS21m1uqTMbDhGqz01XtNDVH73qc7sVUeRyi1wxMW/xADQz07xfsb74WYydW7kyrd45tcvcdfUWudb544r1RUteyxi5Zc/4TVr+3MG7fK5CkT8p9yuqt9SN4wOc2VIrF9BaN7VGmiyNrdPyZc3zT5bmoKyVxtLWx8+tWNGIYjlTdOWcaSqzqCjX1WLX+TDHjEYg/3NhzszMzMzMxoujWZotXFFDfMAp1W+RazWf+ZMGHenSzLpXtGK1ttA1WsFyy1otjYGrz/nW0ooW92xN+93V2qKXl3fbLW1fNgmTeULhIprdlnuaz1XU2Oc0qBhvp5YIm7mVcM9TbkqfIc+fN+vn/NKD0+csWug6HzD7dtuFpPOBZwK3R8TD87r9gIuBI4AbgedHxF1Kgy3fB5wCbANeFhE/HES6bQmV/paLbo+aKAc4KbXGFSYmGq17MzONcbqUxuzOzOzaAjg5uUukW0tGIQBKmxipZmZmZrYIFwAntaw7E7g8Io4GLs/LACcDR+fXGuBDS5RGW2oRjVd9Xa30NohapILcRHppr70arxXL0eRkGv+2fHl9H4rl5cvRypVo993Sa3KiVCiMRlTL4n2MdxdLoNE6N59Xxbhlzur6FcWym2sOY8vVfFrozGxxBvF3VrTQ7f3sjWnFVG6JW5Fb0yaaI0/GzjzvXBEWPLeq1SNW0maepyLy5fYdTdeO1lr0KD2Elc+Vo1uqJbpl0aq45YsP7vj5FjtHn3UWEd+WdETL6lOBp+T3FwLfBN6Q13800qCn70vaR9JBEXHLEiXXbGyNQsucC3NmZmYlrYWcmEqFtCi6WRaT806UujVB+wmBoRH4JIdUqXedrAVsT10yi8Am9W1q7pq562ThOQ0thcB6LXwUx0Xbz2QDcWCpgHYrcGB+fwiwsbTfprzOhblRVlTQtAuKMjlZD3qilSuaK4iW54qbqWminD/kvEErV9SDoTTNL9caVKlTesbNCHxsF+bMzMzMllBEhDT/NgFJa0hdMVnF7j1Plw1ARD10bnn6gPrme+9tjLeNGkykngHafXk9om5s21bfv3bP1qZxdekSpS6WE6p3r4xajG8hDiDcMmfWM1XsbtnttAmzdbes0ucxs4WpBwopAqLUW7nyA1anFrmiplzNrWkTxTxRtZnSNAa5pa3NwxzQCDs+WUxN0DzWpT6heeOApjTfs+5B9da5XaYmsKVyW9F9UtJBwO15/c3AYaX9Ds3rdhER5wLnAqzWfiPwGGo2YCPwV+QAKGZmZmb9txY4Pb8/Hfh8af1LlZwAbPF4uTEVtUbFTjGn3LJljYAlmkhzU9ZmUsXP9HR6QWqFa4lkGRGNLpfFuXOQleaJw8eTSC1z831VjVvmrFIBPKrUQjfficz3PmXDQNNfpXtn1itVyJ+KFq0imEj9b6x1fFtW7ypVxDtpmRC4lqcuIKIRdryYFLjTuYqxcsV0CMVx5W5UlAKntDyorT7lhl2nXFhC45Y/SfokKdjJAZI2AW8FzgEukXQGcBPw/Lz7OtK0BBtIUxO8fMkTbDauRqCbqQtzZmZmZj0UES/ssOnENvsG8Mr+psgqrRjDVgSx3dEIbFSvwAG0amUjGNLyGSJH2I37GmPmYnp69la3mBqJAkyvVLGlbb5cmLNKGJXa2qX8HJ1aLMatBtys3zr+LbWLQge7PEiptfWtHMVuos3EwKV962Plis2t+3VSjNcrjcFb/Ywb83WLERYt5+rhA57zJ7OFi1o0TSBeNzVFFHnG1FS9BT46/e2WJib3nHJtVHTeuPlyYc7MzMzMzMaORqCM68KcDYRrZRenm3FErgE3W5iu/2ZmmyeqvFvLmLly9EvlSb93aaFr+VkrWuiKyJjFcp7AvD6/3EwxXi+PpStftmikK2rzp/vzFOP8yWyedmlZqzW6XJb7AZZa12rlOSaj1shXyi1w9eAnM1gHbpkzMzMzM7OBcjfKBfGYORt6SxkpzjWwi1f+fc02v127Y3z/bdgMbf5UHqdCYwxd6xgYTU50HCvXOumvWqNctmzfpXVwsrmFbik4fzIzW3pzzjMn6XxJt0v6WWndfpK+Kum6/HPfvF6S3i9pg6SfSHpMPxNvZuPN+ZOZmY2ciMarNlOfIy6mp+uv+nxztZnm/csvm13Q+d7N9qqYblrmLgA+AHy0tO5M4PKIOEfSmXn5DcDJwNH5dTzwofzTxlAvalpda5u01m6X57RbzDlGwAU4f7IF6Gv+VLSSdWihm1iR61EnJtAeezQfO7Uz/SzGtRUtda0tcHm9lqX/xrUyjZ1j+/Z03Eyt6TxMqPG+x92xnD+Z9VkFCxCjoh/dLCWdBLwPmAQ+EhHntGxfSXpueSxwB/CCiLhR0lNJ81GuAHYCfxkRX5/renO2zEXEt4E7W1afClyY318IPKe0/qORfB/YR9JBc13DzGwhnD+ZmZnZgsUCXrOQNAl8kFSBfAzwQknHtOx2BnBXRBwFvAd4Z16/GXhWRPwP4HTgY918hIWOmTswIm7J728FDszvDwE2lvbblNfdglXSQmpQZztXr7SmaVxb6PpZWz3C99T504gYlvzpni89GIDVJ1+fVswV5bIYzzYx0WhRiyLCZI58uTzPEzeVliMfUx8rV8wjl4+vzzPVOllwu7mqWi2w1t/5k5kNM9GXlrnjgA0RcQOApItIlcnXlPY5FTg7v78U+IAkRcR/lva5GthN0sqIKIUu3dWcLXNzifQ/yLxvhaQ1ktZLWj/FrGk0M1sQ509mZmbW1kLGy81d+dWp4rjtPhExDWwB9m/Z5/8H/HCughwsvGXuNkkHRcQtuZvS7Xn9zcBhpf0Ozet2ERHnAucCrNZ+7gw8QN1GHet0XD+0nnspo9pVUbt7vdDfW6sRrAF3/jRChjp/qrfQ5R9Fi1x5l3vvTW+KbcV4tok8f9zuy9PP5elnbNvWdHztnq355M3RLXdpqZtQ/dxRHzvXm6+28yczG1YLbJk7QNL60vK5+bmhJyQ9jNT18mnd7L/Qlrm1pL6c5J+fL61/aY4adwKwpdTdycxsKTh/MjMzs7ktbMzc5og4tvQqF+S6qTiu7yNpGbA3KRAKkg4FPgu8NCKu7+YjzNkyJ+mTwFNIpdBNwFtJkVYukXQGcBPw/Lz7OuAUYAOwDXh5N4mw4TDImtFxrZXt5nP3qhVzGGvAnT9ZYSjyp9YokrUaFGPmZpojX1LL0StXpBY5irF0hQ7zy0Vra1txzdpEo0WuR5w/mdmw68OYuSuBoyUdSSq0nQa8qGWfotL5e8Bzga9HREjaB/gicGZEfLfbC85ZmIuIF3bYdGKbfQN4ZbcXNzNbDOdPZmZmtiDBrkGjFnvKiGlJrwIuI01NcH5EXC3pbcD6iFgLnAd8TNIGUkTu0/LhrwKOAt4i6S153dMi4nZmsdAxczaCWsc4uAZ0OC22Jty/f6uioc6firFquTGNHY3x7LFzqmlXrVqZfhYtbsvzGLiptF/c1zxmroh+OWerW0xVYq4q509mVil9yBYjYh2pN1B53VtK77cDz2tz3NuBt8/3ei7MmZmZmZnZ2OnHpOFLzYU528Wo13he9usfAfD0gx810HT0m6PI2Sgahe9j0YqmdnPAFS1wExNNy0VXoF3GxO16cvLJm5crxvmTmVVCBXosLJYLc2ZmZmZmNnbcMmc2hEa9Ra7Qyyhyrv02W4Rdan5LY+hanyRaWtJq23e0396p5a24Vn2AXjU5fzKzgWtMNTDUXJgzMzMzM7OxIkDuZmlmw6JT7fVix66Y2QBUdCzcQjl/MrOBGIGs1IU5szHXq+5OZtalck1wNE/+HdPDX0vcS86fzMxmNzHoBJiZmZmNEknnS7pd0s9K6/6fpJ9L+omkz0rap7TtLEkbJP1C0tMHkmizMaSIeb+qxi1zZtbENeFmA1DBB4QqGqL86QLgA8BHS+u+CpwVEdOS3gmcBbxB0jHAacDDgIOBr0l6SETFo9iYDbsRCYDiljkzMzOzHoqIbwN3tqz7SkRM58XvA4fm96cCF0XEjoj4JbABOG7JEms2tiJVpM33VTEuzA3AlnVHVbk20ayJw36PF+dPNkyGOH/6A+BL+f0hwMbStk15nZn1mWL+r6pxN0szMzOzJSLpTcA08IkFHLsGWAOwit17nDKzMVTBlrb5cmFugIra737WLC7FNcxs9Dh/Mus9SS8DngmcGFF/irwZOKy026F53S4i4lzgXIDV2m/4n0LNBilAIzA1gbtZmpmZmfWZpJOA1wPPjohtpU1rgdMkrZR0JHA08INBpNFs7IzAmDm3zFVA6/iUhdRSdxrj4hpvM1sM509m8yfpk8BTgAMkbQLeSopeuRL4qtK8gt+PiFdExNWSLgGuIXW/fKUjWZotkeqVzebNhTkzMzOzHoqIF7ZZfd4s+78DeEf/UmRm7VRx3rj5cmGuQlxLbWZV5fzJzMxGjgtzZmZmZmZmQyaAEQiA4sLciHItuplVlfMnMzMbNBHuZmlmZmZmZjaUXJizhehlrXQvIs2ZmRWcP5kNEWkkHkbNFiVFh11YZMoR+PtxYc7MzMzMzMaLx8wN3pZ1R7mm18wqyfmTmfXTVu7a/LXap+4DNg86LW0cQDXTBdVNW1XTBdVNW0pXo3Ht8PmewGPmzMzMzGzJRcQDJK2PiGMHnZZWVU0XVDdtVU0XVDdtVU3XUhvqwtzep2zYZUxGedso81gUs2pz/tQw6p/XzMyGVB9a5iSdBLwPmAQ+EhHntGxfCXwUeCxwB/CCiLhR0v7ApcDvABdExKu6ud5ELxNvZmZmZmZWfZEKc/N9zULSJPBB4GTgGOCFko5p2e0M4K6IOAp4D/DOvH478FfA6+bzKYa6ZW6cuabbzKrK+ZPZkjl30AnooKrpguqmrarpguqmbXHpCvrRMnccsCEibgCQdBFwKnBNaZ9TgbPz+0uBD0hSRNwHfEdS+249HbhlzszMzGwIRUQlH7Krmi6obtqqmi6obtp6kq7aAl5wgKT1pdea0hkPATaWljfldbTbJyKmgS3A/gv9CEPfMlfUAHcam2JmNijOn8zMzKprgdEsN1cp8MqcLXOSzpd0u6Sfldb9P0k/l/QTSZ+VtE9p21mSNkj6haSn9yndZmbOn8zMzGzhejxmDrgZOKy0fGhe13YfScuAvUmBUBakm26WFwAntaz7KvDwiHgE8F/AWTlBxwCnAQ/Lx/xjHgi4ZPY+ZYPHa5iNjwtw/mRmY0bSSblSaoOkMweclsMkfUPSNZKulvTqvP5sSTdL+lF+nTKAtN0o6af5+uvzuv0kfVXSdfnnvgNI10NL9+VHku6R9JpB3LMOlaJt75GS9+fv3U8kPWYAaWtbYSvpCEn3l+7dh+e8QAC1mP9rdlcCR0s6UtIK0nPH2pZ91gKn5/fPBb4esfDBe3MW5iLi28CdLeu+kvt4AnyfVOqENKDvoojYERG/BDaQBgKamfWc8yczGzddRstbStPAayPiGOAE4JWl9LwnIh6VX+sGlL7/ma9fdIs7E7g8Io4GLs/LSyoiflHcF1J4+m3AZ/Pmpb5nF7BrpWine3QycHR+rQE+NIC0ta2wza4v3btXzH363kezzM8frwIuA64FLomIqyW9TdKz827nAftL2gD8BaXvoKQbgXcDL5O0qZu/7V6MmfsD4OL8/hDSw1Oh3aA/APJgwTUAq9h90YnodW13McbFtehmQ835k5mNmm6i5S2ZiLgFuCW/3yrpWjrkrRVxKvCU/P5C4JvAGwaVGOBEUiHkJklLfvGI+LakI1pWd7pHpwIfza1I35e0j6SD8ndgSdIWEV8pLX6f1LK1mIss6vD2p4x1wLqWdW8pvd8OPK/DsUfM93qLKsxJehOpRuYT8z02R6A5F2C19uv9nZxDtwEJ2u3nByiz6nP+ZGYjql20vOMHlJYm+cH70cAVwOOBV0l6KbCe1Hp31xInKYCvSArgn3LefmCp8HErcOASp6nVacAnS8uDvmfQ+R51itTYl8JcF8oVtgBHSvpP4B7gzRHx73OeoQ+FuaW24KkJJL0MeCbw4lI/z24G/ZmZ9ZXzJzOzpSVpT+DTwGsi4h5SF7wHA48iPez//QCS9YSIeAype+ArJT2pvDH//zCwp/k8purZwKfyqircsyaDvkedtKmwvQV4YEQ8mtR18V8lrZ71JP0ZM7fkFtQyJ+kk4PXAkyNiW2nTWtLNezdwMKlP7Q8Wncoe6kWIcHdxMqsu50/On8xGXOUqpiQtJxXkPhERnwGIiNtK2/8Z+LelTldE3Jx/3i7ps6QuqrcVXQMlHQTcvtTpKjkZ+GFxr6pwz7JO96gS371She2JRYVtROwAduT3V0m6HngIqYWzg4Co9Tu5fdfN1ASfBL4HPDQPxDsD+ACwF/DVcsSYiLgauITUb/vLwCsjYqZvqTezseb8yczGUDfR8paM0kCv84BrI+LdpfUHlXb738DPWo/tc7r2kLRX8R54Wk5DOZLg6cDnlzJdLV5IqYvloO9ZSad7tBZ4aY5qeQKwpV/j5TopVdg+u1xhK+kBOTgQkh5EqrC9Yc4T9n5qgiU3Z8tcRLywzerzZtn/HcA7FpOoYeEa8OHm39/wc/7Umb/fw82/P+skIqYlFdHyJoHzc2XVoDweeAnwU0k/yuveSIqy+ShSZ7YbgT9e4nQdCHw2BxVZBvxrRHxZ0pXAJbny7ybg+UucLqBewHwqzffl75b6nuVK0acAB0jaBLwVOIf292gdcAopGvQ24OUDSNtZwEpShS3A93PkyicBb5M0BdSAV0TEnW1PXCi6WQ65XkSzNDMzM7Ml0i5a3qBExHeAdmEYB5q+HO3zkW3W30GKIDlQEXEfsH/LupcMIB3tKkWhzT3KXRpf2d8UNV2v6wrbiPg0qavvfC8y70OqZmwKc70YizLXuV2DOhxavwv+/dmgOX+ygvMnM7MlNAKFuQVHszQzMzMzM7PBGZuWuaJWc9RrwKuQBjObH+dPZmZmS62aAU3ma2wKc2ZmZmZmZkAOgDL8UxO4MDciOo2z6IZryc2sn5w/mZlZJbllzszMzMzMbAi5MDd8+jk2ZRhrkIcxzYs1jp/ZFklakgzf+VOzYUzzYo3jZ15yUupeZWZjLjzPnJmZmZmZ2dAJiBj+MXOKCjQvSvoNcB+wedBp6eAAqpm2qqYLqps2p2v+Fpq2wyPiAb1OzFKreP40it+bfnO65q+qaVtMukYifzKzhdt72QPicaufM+/jLrvrI1dFxLG9T9HCVKJlLiIeIGl9lW5MWVXTVtV0QXXT5nTNX5XTthSqnD9VNV1Q3bQ5XfNX1bRVNV1mNkQq0Ki1WJUozJmZmZmZmS2ZCE9NYGZmZmZmNpTcMtdT5w46AbOoatqqmi6obtqcrvmrctqWSlXvQVXTBdVNm9M1f1VNW1XTZWZDIkagZa4SAVDMzMzMzMyWyt6T+8cJq54x7+O+su1jDoBiZmZmZmY2MMFIzDM3MegEAEg6SdIvJG2QdOYA03GYpG9IukbS1ZJendfvJ+mrkq7LP/cdUPomJf2npH/Ly0dKuiLft4slrRhQuvaRdKmkn0u6VtLjqnDPJP15/j3+TNInJa0a1D2TdL6k2yX9rLSu7T1S8v6cxp9IeswA0vb/8u/zJ5I+K2mf0razctp+Ienp/UzboDlvmlcaK5c/VTVvymlz/rSwdDlvMrPeidr8XxUz8MKcpEngg8DJwDHACyUdM6DkTAOvjYhjgBOAV+a0nAlcHhFHA5fn5UF4NXBtafmdwHsi4ijgLuCMgaQK3gd8OSJ+G3gkKY0DvWeSDgH+DDg2Ih4OTAKnMbh7dgFwUsu6TvfoZODo/FoDfGgAafsq8PCIeATwX8BZAPnv4TTgYfmYf8x/wyPHedO8VTF/qlzeBM6fFpmusc+bzKw3AohazPtVNQMvzAHHARsi4oaI2AlcBJw6iIRExC0R8cP8fivpP/5DcnouzLtdCDxnqdMm6VDgGcBH8rKA3wUuHXC69gaeBJwHEBE7I+JuKnDPSN2Id5O0DNgduIUB3bOI+DZwZ8vqTvfoVOCjkXwf2EfSQUuZtoj4SkRM58XvA4eW0nZRROyIiF8CG0h/w6PIeVOXqpg/VTxvAudPC0qX8yYz65kIt8z1yCHAxtLyprxuoCQdATwauAI4MCJuyZtuBQ4cQJLeC7weKL5F+wN3l/5TG9R9OxL4DfAvuYvVRyTtwYDvWUTcDLwL+BXpIWkLcBXVuGeFTveoan8TfwB8Kb+vWtr6qZKftYJ5E1Qzf6pk3gTOn3poXPMmM+sRt8yNKEl7Ap8GXhMR95S3RQr/uaS/SUnPBG6PiKuW8rpdWgY8BvhQRDwauI+WbksDumf7kmpqjwQOBvZg1+46lTGIe9QNSW8idfH7xKDTYtXLm3Kaqpo/VTJvAudPveC8ycwsqUJh7mbgsNLyoXndQEhaTnpY+kREfCavvq3oRpJ/3r7EyXo88GxJN5K6ev0uaSzIPrmLDgzuvm0CNkXEFXn5UtID1KDv2f8CfhkRv4mIKeAzpPtYhXtW6HSPKvE3IellwDOBF0djDpNKpG2JVOqzVjRvgurmT1XNm8D506I4bzKznhmBbpZVmJrgSuBoSUeSMt7TgBcNIiF5nMd5wLUR8e7SprXA6cA5+efnlzJdEXEWjUHeTwFeFxEvlvQp4LmkB6glT1dO262SNkp6aET8AjgRuCa/BnbPSN2XTpC0O3B/Ttd64BsM+J6VdPperQVeJeki4HhgS6m705KQdBKp29yTI2JbadNa4F8lvZvUonA08IOlTNsSct7UharmTxXOm8D504I5bzKzXtnKXZd9LS49YAGHbu55YhYjIgb+Ak4hRaW6HnjTANPxBFJXkp8AP8qvU0jjPy4HrgO+Buw3wDQ+Bfi3/P5BpP+sNgCfAlYOKE2PIj2I/AT4HLBvFe4Z8NfAz4GfAR8DVg7qngGfJI2NmSK1GJzR6R4BIkVRvB74KSni3VKnbQNp/Enxd/Dh0v5vymn7BXDyIL5zS/gdct40v3RWKn+qat6U0+b8aWHpct7kl19++VV6KaJS3eDNzMzMzMysC1UYM2dmZmZmZmbz5MKcmZmZmZnZEHJhzszMzMzMbAi5MGdmZmZmZjaEXJgzMzMzMzMbQi7MmZmZmZmZDSEX5szMzMzMzIaQC3NmZmZmZmZD6P8PNvIfjBTH9hYAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for i in range(10):\n", " plot_predicted_data(generator, collected_routes, i)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "pycharm": { "name": "#%%\n" }, "scrolled": false }, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3MAAAFGCAYAAADAYcZNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAB2XUlEQVR4nO3deZxkVX3//9enqpdZYAaYQYQBWWZARQ2gBvCriX5D3IgKv8QFRQXFEBONmsQoLlHiVxPM1z0YFQVBREFxIwbjgtvXKMgiiIjIzLANDMvA7NPTS9Xn98c5t/p2dVXX0rXcW/1+zqMeU3XXU7eqb91zP+d8jrk7IiIiIiIiki+FfhdAREREREREWqfKnIiIiIiISA6pMiciIiIiIpJDqsyJiIiIiIjkkCpzIiIiIiIiOaTKnIiIiIiISA6pMiciEpnZj83sdT3a11+b2QNmtsPMVjSx/Olm9rNelG2hiZ/BYV3c/qfN7J/mmH+2mX1xHtt/lpltaHf9qm2dambfa3PdQ8zMzWyoE2UREZHGVJkTkQXFzO40s7F4Af+AmV1oZnu0uI15XbSa2TDwEeA57r6Huz/cye03sf95VR6qtuVmtqYT2+qX+Bms7+L2X+/u/wc6U/Hq5jF390vc/Tnd2Ha7zOw78e81eUyY2c39LpeISBaoMiciC9EL3X0P4MnAU4F393j/+wGLgFt6vF9JUQQpH9z9+bHCvUf8u/058NV+l0tEJAtUmRORBcvd7wW+Azyxep6ZFczs3WZ2l5k9aGZfMLPlcfZP4/9bYqTgaTXWHzWzj5nZffHxsTjtCOC21Po/rFG0uts3sw+Z2WYzu8PMnp+avtzMzjezjWZ2r5m938yKNcr1POCdwMvitm9qtL6ZrTGzn5jZVjPbZGaXxelJOW+K23pZjf3VXDfOe4KZfd/MHolR0nemjv1ZZrbOzB42s6+Y2T5xXhK1PM3M7o7bfFdqm8ea2S/MbEt8L+ea2UhqvpvZG8zsduD21LQ1qePwBTN7KH727zazWb+VZrYoRnhXxtfvMrMpM1sWX/8fM/tYfH5hPJ5LCd+3A1JRpgPiJkfifreb2S1m9tRZ34oGx9zM/iF+Vzea2WtS00fj9+bueJw/bWaL62x/RnPeeGxeb2a3x2P6STOzOK8Yt7vJzNYDf1a1rZrfKTMbMbMbzexvU9v5HzN7T60yVW3zEOCPgC80WlZEZCFQZU5EFiwzOwg4EfhVjdmnx8f/Bg4D9gDOjfP+OP6/V4wW/KLG+u8CjgeOBo4CjgXe7e6/B56QWv9Paqxbb/vHESqCK4F/A85PLqyBC4EpYA1wDPAcYFb/P3f/b+BfgMvito9qYv3/A3wP2Bs4EPj3uK2knEfFbVUqaik11zWzPYEfAP8NHBD3e1Vc52+Bk4FnxnmbgU9WbfcZwGOBE4D3mNnj4/QS8HfxGD0tzv+bqnVPJhzLI2uU99+B5YTP/JnAq4HXVC/k7ruBa+MyxP/vAp6eev2TqnV2As8H7ktFmu6Ls18EXArsBVzB9Heter/1jvmjY7lXAWcAnzSzveO8c4AjCN/FNXGZhhWnlBcAfwj8AfBS4Llx+l/GeccQItwvrlrvQmp8p9x9Angl8L74uZ0FFIEPNFGWVwP/z93vbKH8IiIDS5U5EVmIvmlmW4CfES64/6XGMqcCH3H39e6+A3gHcIo13zTvVOB97v6guz8E/DPwqnmW+y53/6y7l4CLgP2B/cxsP0Kl9C3uvtPdHwQ+CpzSzEabWH8SOBg4wN13u3sriVjqrfsC4H53/3Ccvt3dr4nzXg+8y903uPs4cDbw4qpj/8/uPubuNwE3ESrMuPv17n61u0/FC/7PMF3hSvyruz/i7mNVx6EY3/M7YnnuBD5M/c/tJ8AzY7n+APhEfL2IUPn5aZ31avmZu18ZP9uLk/fTgknC923S3a8EdgCPjZX9M4G/i+95O+H73tR3IzrH3be4+93AjwiVQggVu4+5+z3u/gjwr8kKjb5T7v4b4P3AN4G3Aq+K772RVxMqiSIiAqi/gIgsRCe7+w8aLHMAIdKSuItwztyvyX3UWv+AOss26/7kibvvikG5PYB9gGFg43SgjgJwT5PbPbjB+m8jRNh+aWabgQ+7+wVNbrveugcB6+YozzfMrJyaVmLmsb8/9XwX4ThgoRnrRwiRoiWEz+z6qu3XOy4rCceh+nNbVWf5n8R9PRm4Gfg+cD4hIru2OrFNA9XvZ5GZDbn7VJPrP1y1bHJM9iUch+tTn60RImHtli1JGHQAM49l+rg1+k5BuCHxAeBr7n57o0KY2TMIEcjLWyi7iMhAU2VORKS2+wgXpInHEJqMPUD9i/ta6ydJTh4TpzXDm1wucQ8wDqxs8uK/evtzru/u9xOa1CUX1D8ws5+6+9qGO6qzbtxnvejQPcBr3f1/qmfEPlNz+RSh2ezL3X27mb2F2c3/6h3fTUxHEn8bpz0GuLfO8j8nNPX8/4CfuPtvzewxhIjUT+qs0+pnO1+bgDHgCbGPaCdtJFTKE49JPW/mO/kfwLeB55rZM5qI+J4GfD1GykVEBDWzFBGp58vA35nZoRaGLkj6mU0BDwFlQr+qudZ/t5ntG5NkvAdodjiAZrZf4e4bCf3SPmxmyywkEFltZtXNCxMPAIckiT0arW9mLzGzA+O6mwkVknJqW3XLOce63wb2N7O3xAQde5rZcXG5TwMfMLOD4zb2NbOTmjkWwJ7ANmCHmT0O+Osm1yM28/tK3Peecf9/T53Pzd13EaJ+b2C68vZzQjPRepW5B4AVNp1Mpx1zHvOqMpaBzwIfNbNHAZjZKjN77txrNuUrwJvM7MDYP++s1H4bfadeBTyF0C/1TcBFNscQITFhy0tRE0sRkRlUmRMRqe0CQt+lnwJ3ALsJiTmSi/gPAP8TM/wdX2P99wPXAb8mNMG7IU5rqMntV3s1MEKIKG0mNEXbv86ySVr3h83shibW/0PgGjPbQUjO8ebUuGxnEy7Et5jZS2vsq+a6se/Ws4EXEprx3U5INgPw8bjs98xsO3A1IWFJM94KvALYTqjE1ErKMpe/BXYC6wl9Kr9E+C7U8xNCc8Jfpl7vSZ3+cu7+O0JFf308Zu00vT2buY95tbcDa4GrzWwbIfHMY9vYb7XPAt8l9Fm8Afh61fya36kYvfwY8Gp33+HuXyL8rXx0jn2dDGwh9NkTEZHI3Hvd4kNERERERETmS5E5ERERERGRHFJlTkREREREJIdUmRMREREREckhVeZERERERERySJU5ERERERGRHFJlTkREREREJIdUmRMREREREckhVeZERERERERySJU5ERERERGRHFJlTkREREREJIdUmRMREREREckhVeZERERERERySJU5ERERERGRHFJlTkREREREJIdUmRMREREREckhVeZERERERERySJU5ERERERGRHFJlTkREREREJIdUmRMREREREckhVeZERERERERySJU5ERERERGRHFJlTkREREREJIdUmZMZzOydZva5Ti/bxLbczNZ0Ylsikm1mdqGZvT8+/yMzu63N7XzazP6ps6Xr7LlNRESkm8zd+10G6RIzOx34B2A1sA34BvAOd9/Sx2LVZGYOHO7ua/tdFhEBM7sT2A8oATuB7wBvdPcdHdj2hcAGd393C+ucDrzO3Z8x3/3njZk9C/iiux+YpW2JiEj/KTI3oMzsH4APAv8ILAeOBw4Gvm9mI3XWGepdCUUkB17o7nsATwaeCsyqfOm8ISIi0j+qzA0gM1sG/DPwt+7+3+4+6e53Ai8FDgFeGZc728wuN7Mvmtk24PQ47Yupbb3azO4ys4fN7J/M7E4z+9PU+l+Mzw+JTSVPM7O7zWyTmb0rtZ1jzewXZrbFzDaa2bn1KpU13s+Pzez9ZvZzM9thZv9pZivM7BIz22Zm15rZIanlP25m98R515vZH6XmJe/5MjPbbmY3mNlRbR9skQXA3e8lROaeCJVm0W8ws9uB2+O0F5jZjfFv/Odm9gfJ+mZ2TPxb225mlwGLUvOeZWYbUq8PMrOvm9lD8bxzrpk9Hvg08LR4DtgSl60014yv/9LM1prZI2Z2hZkdkJrnZvZ6M7s9lvGTZma13m8r57Ya6y42sw/H8+ZWM/uZmS2O815kZrfE/f84vq9kvTvN7K1m9uu43mVmtsjMlsZjf0B87zvM7AAzK5jZWWa2Lh6nr5jZPnFbnzKzr6W2/UEzu6retup+8CIiA+y5/3upP/WoRS0/zOy/+132NN1RHUz/i3Cx9PX0RHffYWZXAs8GLoiTTwJeArwaGAXenixvZkcC/wE8D/gl8C/Aqgb7fgbwWOAI4Jdm9nV3v5XQVOvvgOuAAwkXFH8DfKzJ93QK8FxgE/CL+Pgb4LT4Xt4LvCYuey3wPmAr8Gbgq2Z2iLvvTr3nlxMqtW8GvmlmR7j7ZJNlEVlQzOwg4ERmnlNOBo4DxszsGMLf4QsJf+OvBK4ws8cCDnyT8Ld+LuHv78uElgPV+ykC3wZ+CLyKcN54qrvfamavZ45mlmb2J8C/As8BbgE+BFwK/HFqsRcAfwgsA64H/hNo9ke53rmt2oeAJxDOw/cTjlHZzI6I7/tk4MeE8+F/mtmR7j4R130p4Xy7G/gf4HR3/7SZPZ+qppFm9ua4rWcCDwGfAD5JOLf9A3Cjhaap64AzgKPdfWetbYmILESbHilxzXdbPxUO779uZReK0zZF5gbTSmCTu0/VmLcxzk/8wt2/6e5ldx+rWvbFwH+6+8/ixcZ7CBdmc/lndx9z95uAm4CjANz9ene/2t2nYpTwM4SLkGZ93t3XuftWQkVwnbv/IL7HrwLHJAu6+xfd/eG4rw8TKqmPTW3rene/PFbePkKo+B7fQllEFopvxijYz4CfEG7oJP7V3R+J540zgc+4+zXuXnL3i4Bxwt/V8cAw8LHYSuBywg2XWo4FDgD+0d13uvtud/9Zk2U9FbjA3W9w93HgHYRI3iGpZc5x9y3ufjfwI+DoJrcNdc5taWZWAF4LvNnd743H4uexPC8D/svdvx/PPR8CFhMqfYlPuPt97v4IoaI5V/leD7zL3TfE7Z8NvNjMhtx9F6Ey/BHgi4RWGhvqb0pEZCFySl5u+ZE1iswNpk3AyvijXl2h2z/OT9wzx3YOSM93911m9nCDfd+fer4L2AMg3pX+CKHfzRLCd+/6BttKeyD1fKzG6z2SF2b2VsKd6AMIlc9lzKzApt9TOTbxUlMjkdlOdvcf1JmXPnccDJxmZn+bmjbC9N/gvT4z29ZddbZ5EHBXnRtRjRwA3JC8iC0RHia0JrgzTq55fmpSM+uuJNwcWlenfJX3Hc899zCztUP1PuY6Lx0MfMPM0lcWJULSmnvd/RozWw88CvjKHNsREVmQHCg3jFFknyJzg+kXhLvif56eaGZ7AM8HrkpNnutbvJHQJDJZfzGwos0yfQr4HSFj5TLgnUDN/irzEfvHvY3QXGlvd9+L0Nwyva+DUssXCO/xvk6XRWTApc8d9wAfcPe9Uo8l7v5lwnlkVVX/tMfU2eY9wGOsdlKVRr+49xEqOADE/mErgHsbvZEO2kRoIrm6xrzq8hnhXNRM+Wq993uA51cd80WxfyNm9gZCq4T7COfEubYlIrIgldv414iZPc/Mbot9uM+qMX809otea2bXJC1IzOxUC33Pk0fZzI5utD9V5gZQbIr4z8C/xy/UcPyifAXYAFzc5KYuB15oZv/LQrKSs2m/ArYnYXiEHWb2OOCv29xOM/uZIvQhGTKz9xAic2lPMbM/jxeMbyFUfK/uUnlEFoLPAq83s+MsWGpmf2ZmexJuLk0Bb4rnoj8nNKes5ZeEyt85cRuLzOzpcd4DwIFWP3HSl4HXmNnRZjZKaBJ6TWzW3RPuXib0HfxITFJSNLOnxfJ8BfgzMzvBzIYJ/drGgZ83sekHgBVmtjw17dPAB8zsYAAz29fMTorPjwDeT+i7+CrgbakLglrbEhFZcByn5K0/5hL7fn+SEDw5Enh5zEGRdgaw2d3XAB8l9iF390vc/Wh3P5pw7r7D3W9s9D5UmRtQ7v5vhOjXhwiVqGsId3JPiP0rmtnGLcDfEpIIbAR2AA8SLkBa9VbgFcB2woXfZW1soxnfJSQ0+D2hSdNuZjcl/Rah/8pmwh/Lnyv5iUj73P064C8JCU42A2uB0+O8CUIrgdOBRwh/e1+vs50SIYnKGuBuws2nl8XZPyQkNrnfzDbVWPcHwD8BXyOcr1YTEif12luBmwn9Ah8h/EgX3P02QuXq3wkRvBcShn6YqLehhLv/jlBZXW8hE+YBwMeBK4Dvmdl2wg2p4+JNqi8CH3T3m9z9dsJvwcVmNlpnWyIiC1IZb/nRwLHAWndfH8/vlxISf6WdBFwUn18OnFDVegVCMqtLm3kPGjRcmhabaW4hNJW8o8/FaYuZnQ2scfdX9rssIiIiItIfRx814ld951Etr7dy1b13MTP/xHnufh6Amb0YeJ67vy6+fhVwnLu/MVnYzH4Tl9kQX6+Ly2xKLbMOOMndf9OoPEqAInMysxcS+tgZIcp3M9PJBEREREREcqnNBCib3P2pnS5LwsyOA3Y1U5EDNbOUxk4idKC/DzgcOMUVzhURERGRHHPoeJ85QlKrg1KvD2R2oqvKMrFp/HIgnS3+FEJz+KZ0rTLXKJOL5IO7vy5mSVvu7ifEfh+55e5nq4nlwqZzk4iIiACU23g0cC1wuJkdGhN2nULo35x2BXBafP5i4IdJoCRmWX8pTfaXgy41s0xlcnk2oQP7tWZ2hbv/thv7ExFphs5NIiIiAjGbZYdHa3H3KTN7IyEhXxG4wN1vMbP3Ade5+xXA+YSkVGsJibLSybr+GLjH3dc3u89u9ZmrZHIBMLMkk4sumESkn3RuEhERka5x9yuBK6umvSf1fDfwkjrr/hg4vpX9dasyt4qZ6eA3AMfVW3jERn0RS7tUlPwoHT4KQPH2djL/Sy3JMU3T8e2N3exkwsc7PjD8PLV0bgKdnxLjhy4GYPSOsT6XZHAkxzRNx7c3Mnp+EpFecigNQBaIvmWzNLMzgTMBFrGE4+yEfhWl77ZeuWbWtOUnru1DSQbP1k/MPrbVdKy74xq/qt9FaJvOT9PWXXLMrGmrT/1VH0oyeNZ9YPaxraZj3R15Pj+JSGc4TfWBy7xuVeYaZnKJ4zGcB7DM9hmAerGI5EAzWaZ0fhIRERl4Ron8B+i7VZmrZHIhXCidAryiS/vKrVoROemMVo5tsqwidAuCzk1NqhWRk85o5dgmyypCJyLSWQ6UB+B2bVcqc/UyuXRjXyIizdK5SURERBKKzM2hViYXCZqJGila1DvNHmN9JoNB56a5rfvS0eHJHHcrk2VWv+LGbhdnwZsVkSsUw/8ee3pYGC52/RefBMBhp940c/nGA9yKiCxIjipzIiIiIiIiuVR2VeakBeojlw2tRtaqPzdF6GQQVSJy0lcN+8YlEbkYcVt3yZNmzF53yVFhO4qaiojMSZE5ERERERGRHHKMEoV+F2PeVJnrok5E4hQFak87x6vVY63PRvJsVkbFVrpWxWYpyrTYnnaOV8N+ilVNhdSvUQaVmV0IbHD3d5vZHwGfc/fH9mC/Dhzu7rN+9M3sx8AX3f1z/SyHtG4QmlnmvzoqIiIiIplhZnea2ZiZ7TCzB8zsQjPbo9P7cff/10xFzsxON7OfdXr/km9JM8tWH1mjyFwXqG9cvrX6+SlCJ3mi8ePyrem+jUn0VBE66Z8XuvsPzGwVYTiYdwNnpRcwsyF3n+pL6UQwSp7/uFb+34GIiIiIZJK73wt8B3gihGaCZvYGM7sduD1Oe4GZ3WhmW8zs52b2B8n6ZnaMmd1gZtvN7DJgUWres8xsQ+r1QWb2dTN7yMweNrNzzezxwKeBp8VI4Za47KiZfcjM7o7Rw0+b2eLUtv7RzDaa2X1m9tom3urBZvY/sZzfM7OVqW0dH9/XFjO7ycyelZr3GjO7Na633sz+Kr3RVsphZj82s/fHfe0ws/80sxVmdomZbTOza83skNTyHzeze+K862Oz1WTe2WZ2uZldFst2g5kd1cRxyA0HyhRafmRN9kqUY1uvXNO1qFw3ty3B8hPXKromA2vdJcd0LSq37ktHKxtmp5iFR9Xr1a+4sXZ0zW1WfzmRLDGzg4ATgXRn0ZOB44AjzewY4ALgr4AVwGeAK2JlawT4JnAxsA/wVeAv6uynCHwbuAs4BFgFXOrutwKvB37h7nu4+15xlXOAI4CjgTVx+ffEbT0PeCvwbOBw4E+beKuvAF4DPAoYiesTI5P/Bbw/voe3Al8zs33jeg8CLwCWxfU/amZPnkc5TgFeFd/PauAXwOfjvm8F3pta9tr4/vcBvgR81cwWpeafRDjmyfxvmtlwE2XIjUFoZqnKXAeoojUY5vs56jsgWdSTipYqFF3X9ucYPxtVtqUPvhmjYD8DfgL8S2rev7r7I+4+BpwJfMbdr3H3krtfBIwDx8fHMPAxd59098sJFZBajgUOAP7R3Xe6+253r9lPzsws7vfvYjm2x/KdEhd5KfB5d/+Nu+8Ezm7i/X7e3X8f39NXCJUkgFcCV7r7le5edvfvA9cRKri4+3+5+zoPfgJ8D0giZO2WY527byVERNe5+w9ic9avApW7eu7+RXd/2N2n3P3DwCiQ7oN4vbtf7u6TwEcIUdHjmyhDLriHZpatPrJGfeZEREREpNNOdvcf1Jl3T+r5wcBpZva3qWkjhIqZA/e6ezrf7l11tnkQcFeTffD2BZYA19t0JNyAYnx+AHB9E/tMuz/1fBeQJHw5GHiJmb0wNX8Y+BGAmT2fEC07ghBkWQLcPI9yPJB6PlbjdSURjZm9FTiD6WO9DFiZWr7yObl7OTZpPaCJMuRGOYORtlapMjcP/YjEKNlG53Xyc9TnI1lRaVLZypADHdqnhironGQQ8Ip2I6Bu+nwkS9JnpnuAD7j7B6oXMrNnAqvMzFIVuscA62ps8x7gMXWSqlSfCTcRKjZPiH36qm0kVA4Tj6n/Vhq6B7jY3f+yeoaZjQJfA14NfMvdJ83sm1CpYXSyHNX7/iPgbcAJwC2xsrY5tW/S+zazAnAgcF+nyiCdkb1YoYiIiIgsFJ8FXm9mx1mw1Mz+zMz2JPT3mgLeZGbDZvbnhOaUtfySUPk5J25jkZk9Pc57ADgw9sHD3ctxvx81s0dB6NtmZs+Ny38FON3MjjSzJczsZ9aqLwIvNLPnmlkxlutZZnYgIQI5CjwETMUo3XNS63ayHNX2JBzbh4AhM3sPITKX9hQz+3MzGwLeQmj+enUHy9BXYWiCQsuPrMleiXJAfeQGgxLWyCDqZqIT6Z1ZfeQ62C9RCWskS9z9OuAvgXOBzcBa4PQ4bwL48/j6EeBlwNfrbKcEvJCQzORuYENcHuCHwC3A/Wa2KU57e9zX1Wa2DfgBsb+Yu38H+Fhcb238v933dw8hkcg7CRWne4B/BAqxr96bCJW2zYQkKlek1u1YOWr4LvDfwO8JzTd3M7P5K8C3CMdwMyGpyp/H/nMDYjD6zNnMZsj9scz28ePshH4Xo2lZuEhXM77568XnuJA/p2v8Krb5I7lvjJ6381MWKnJqxjcPsf9Ox5pX1txH+N1fyGPPDcr5SaRbzOxsYI27v7LfZemWNU9a4h/+1hEtr3fy6puud/endqFIbVGfuRZkoRKXUN+sfNDnJL1SibT0//6c+mbNR/UN1m5kCU0GFNfnJCILXGkAMjFnL1YoIiIissCY2fPM7DYzW2tmZ/W7PCKDzrGB6DOnyFyTshSVk/lRFlIZNOu+dLTGeRsQ/ejLpghd/8UBrz9JGBx6A3CtmV3h7r/tb8lkoXL3s/tdhl4oZ7APXKtUmRMRERHpr2OBte6+HsDMLiUkzVBlTqRLkmyWeafKXB15icQp4tO8vHymsgBZnahanQRVs6I3GY3KKeJTR/rzjp/xdJ/HbH6W0nWrmJlJcANw3FwrjNioL2JpVwslkifb2bzJ3fdtdnnHBqLPnCpzIiIiIjlgZmcCZwIsYgnHFf607k0fkQUj3iD7Qfmrd7W6almRucGj6M3gydJnqkiqzIeiNwPGPVPjvSmS2lf3AgelXh8Yp83g7ucB50EYNkUVORHavqHhTibHjWuVKnMiIiIi/XUtcLiZHUqoxJ1CGEBaRLrGKJP/m6P5r452yNYr12QqgtOqvJe/W7J6TOb7eWX1fUmTzKoehfCoY92Xjs5UBKdV6y45JhODmWeG+3RUzm3mIwPm+3nps26du08BbwS+C9wKfMXdb+lvqUQGmxMic60+Gmk0zIiZjZrZZXH+NWZ2SGreH5jZL8zsFjO72cwWNdqfInMiIiIifebuVwJX9rsckiNJMiUrgJennyeSaaC+lXV0Optlk8OMnAFsdvc1ZnYK8EHgZWY2BHwReJW732RmK4DJRvtc8JU5RTgGU14+11b70OXlfUmL0j+4MDvDYWV6NiI3Mj95ibK22odOETmRHqtU0Mp1nstcHKPc+d/VZoYZOQk4Oz6/HDjXzAx4DvBrd78JwN0fbmaHamYpIiIiIiILTolCy48Gag0zsqreMrGJ9VZgBXAE4Gb2XTO7wcze1sx7WLCRuUGNcAxKtsR238egfq7V8v75ytwGNSI3KNkSk89n9StubGu9Qfk868n75yuSO+lInKJyTXOg3F42y5Vmdl3q9Xkx0+x8DQHPAP4Q2AVcZWbXu/tVjVYSERERERGRxja5+1PrzGtmmJFkmQ2xn9xy4GFCFO+n7r4JwMyuBJ4MqDIn+VEdWRuUSGM9rb6vQT0OInlQHTFtN0KXF61G2BSRE5F8MUqdH5qgmWFGrgBOA34BvBj4obu7mX0XeJuZLQEmgGcCH220w7b7zJnZQWb2IzP7bUyf+eY4fR8z+76Z3R7/37vdfYiItEPnJxERyZ1Zw9bY7Pm1lpW2JM0sW33Muc06w4yY2fvM7EVxsfOBFWa2Fvh74Ky47mbgI4QK4Y3ADe7+X43eh3mbbWvNbH9gf3e/wcz2BK4HTgZOBx5x93Pi2Ap7u/vb59rWMtvHj7MT2irHfA16H6u8RnLqfS713k9eP8e8fj7NuMavYps/0pdfmdydn6p/jKuzWQ5oH6u8RnLqZaOsROiSzzPnWUnz+vk0o5/np07p57WTdEnVuaPm/GRe+rkA8AO//Po5mj/OcuATl/sbvvL0lvfzzid8p6X9dFvbkTl33+juN8Tn2wm1z1WEdJsXxcUuIlxAiYj0jM5PIiIiMhd363hkrh860mcujlx+DHANsJ+7b4yz7gf268Q+RGB2H7q8RuSkd3R+kl6p7kOX14iciPRJK5E2ReU6opTBylmr5l2ZM7M9gK8Bb3H3bZZqLhQ789X8tpnZmcCZAItYMt9iiIjMovOTiIiI1OJAufMJUHpuXpU5MxsmXChd4u5fj5MfMLP93X1j7LfyYK1143gM50Fo9z2fckh9ec0G2SjylveIXN4+jzzK9PlJHdaB/GaDrBt5i/IekRvkvnIiItNsICJz88lmaYRsLLe6+0dSs5J0m8T/v9V+8bpv+YlrdWEtMmAG5fy0+hU3hoqDeXgMGrfcVXSAxhnkkveV1/cnItmkppUdFbJZWsuPrJlPZO7pwKuAm83sxjjtncA5wFfM7AzgLuCl8yqhiEjrdH4SERGROZXaj2tlRtuVOXf/GdRtaNpSrtzS4aNs/cQaRci6aFCbW8r85fW7MZdOnp/GD13Mug8c05mmZ0k0x+KPh5fj/w3utmbwTmAn9aS5ZZ3hH+ZjVnPLvH9OGYz+rrvkGEBNP0Wk85xsRtpa1ZFsliIiIiKSY7WaDqtZX2/pM+i58kKOzHVDdfRlkCIFItD6d1oRyexIIgSJnkYKkojJANxBHAgDenHVamR0YCKSIrIguUNpAM5fmarMiYiIiIgsSMmNImU87hk1sxwgC6VvVt76Rw365yELxKz+WuWWVq/0zaqKDg6M+GPaVt+5Zi96qvspdsCCj0wt1Pc9SNJ/P5ZqbtbBvxNpw4BG/7Mm9JlTM0sREREREZHcKS30QcNFukUROZEFaD6RnmYjb5XoQ5OZRGtY8BE5GUyKxskCk4wzl3eqzImIiIjINDXz6750E1cdb5kHVeYWqKz2nVNELujHccjqdyK3zKZ/oKt/qNW5fU4t9Z2rJAyIr+tF6OpFHZLPoomLKUXkgn703dR4cyLSeYPRZy7/70BEREQkB8zsIDP7kZn91sxuMbM3x+n7mNn3zez2+P/evS9cYWYSFOku9+mH9E0Za/mRNZmIzBVvH1c0IAOajQbps2qdjll+jd4xlp1owEIZb67G+5sVFbOZF0DJGoedelNPyzUIMvP9XhimgH9w9xvMbE/gejP7PnA6cJW7n2NmZwFnAW/vSYkqlQn1meupFloFSHdonDkRERERaZq7bwQ2xufbzexWYBVwEvCsuNhFwI/pVWVOZAEbhGaWqsxVWSjjzSWy8j6zUg6Rjql1t3WefeUGfry5KpVoXBMqR7vRoLtVzcisEJbzUqnuHfLpqGDTxRFpyMwOAY4BrgH2ixU9gPuB/fpVLkBRo17Qse27MM6cInMiIiIi0gIz2wP4GvAWd99mqZsP7u5mVvNK38zOBM4EWMSSThVm9jRVNGSByGIfuFapMict62TWQ0Xkamvn2OpYZkw6m2Uzy0ILyy/cvnN158VjUsmEmfSdq5PQIYnIzWXgs1fWri801E4fu4USTW6GmQ0TKnKXuPvX4+QHzGx/d99oZvsDD9Za193PA84DWGb7qMYlMg+DMs5c/huKioiIiOSAhRDc+cCt7v6R1KwrgNPi89OAb/W6bNIlZhqOJsPKXmj5kTWKzElfLJQoUi+zWLa7L40v1yW1omwN+3Opn0qnVfrElWce08rrZPy51DFvpa9enjU1jl+n9tVmxswBHF/u6cCrgJvN7MY47Z3AOcBXzOwM4C7gpV0rQfr8Y4WafwNSRQN8DyZXnzkRERERaZK7/wzqdtI5oZdlEVnoHPWZG2gLLatlr+h4Zo8icvmz0LJazkd1RC41Y9akhRKRy5MBisjJQqVIXqYpMiciIiIi+VTjpsaCU93sNOHlOgmUyqqgDYhBSYCS6cqc+vI0r/oYZTUClpdyijQyr748nb4QMM9GxsU62RGr+2Z1JaKYvP9Kps+kf2Iyv+qitfozMJsuZ7y4W3fJUVXbzMAxFhGRjlFlTkREREQkr9yrEpw0Ea1UsqqBoEHDe6g6etOvDIFZiCLVe+/9KFsnIqeDGqlTNHnhqI4y9bKPT3pfs/p79fIHKkau6mVH7Md4bZXx5l55c/MrVWUZrYxV5zPHsMt7hE790EREgkFIgJK9wRJEREREpHNaGedsIY6J5p5qml2Y7ivn5dkPGRwemlm2+mjEzJ5nZreZ2VozO6vG/FEzuyzOv8bMDonTDzGzMTO7MT4+3czbyEVkrlqt6E0vIiG9iiK18l4GJZKVGNRInSwctfqD9SIS0pN+abT2XvoSyaruO1cuNbmez76IrWpC1atj3DV1+jSKSKTKmsyTmRWBTwLPBjYA15rZFe7+29RiZwCb3X2NmZ0CfBB4WZy3zt2PbmWfuazMZcV8hy9opwK60Co3earcqWmlZElS6Wq3wtFOBTTXqf3NWu7/Un2Msly5U9NKEZGZupTN8lhgrbuvBzCzS4GTgHRl7iTg7Pj8cuBcs/ZD4mpmKSIiIjLI0jcqZmVyjc0KzRZmE8u0usdm+mHFYuVROWYL/bjlWBeaWa4C7km93hCn1VzG3aeArcCKOO9QM/uVmf3EzP6omfeQ+8hcFqIh9aJHWShbL/Ty/WYpUrdQPl9pXxaiIfWiR10tWxYShMQyVBKh1EnOMr38/JsgZilS19HPt9nMffXG64LGQ0NId9WLPFtBn021St+5Js9jC/145dg8slmuNLPrUq/Pc/fzOlCkjcBj3P1hM3sK8E0ze4K7b5trpdxX5kRERERERFrl7VXmNrn7U+vMuxc4KPX6wDit1jIbzGwIWA487O4OjIdy+fVmtg44AriOOagy1wXdiNhkua9YP/UyUqdInAyCbkTkBiVlf6f1MlLX1OfaIMFL3eVrDLDeUBLtSSJ06eyA0nvVn2HldY3Po43+owPJClghfNe97DOeV+g45V4Xhia4FjjczA4lVNpOAV5RtcwVwGnAL4AXAz90dzezfYFH3L1kZocBhwPrG+1QlTkRERERkTQvA8XUS6+aJ3nn3vkEKO4+ZWZvBL5L+AJd4O63mNn7gOvc/QrgfOBiM1sLPEKo8AH8MfA+M5sk3Gl5vbs/0mif867MxRSc1wH3uvsLYk30UkJHvuuBV7n7xHz3I9KMTkbqFInLP52feiTLEbkMla2TkbqORFib7RNUb7mqfnFJ5AKAYrgItvi/T04lM+Jr/dmJSP+12cyywTb9SuDKqmnvST3fDbykxnpfA77W6v46kc3yzcCtqdcfBD7q7muAzYSxFERE+kHnJxGRepLBsqsfAoRonJd95qDh6bErZyT9UVbL/Gk9k2UXhjKYt3lF5szsQODPgA8Afx/HSPgTptuGXkQYR+FT89mPzH9Mu17IYhbPdFkaHbsslVvmT+en3pnvmHa90JMsni1Kl6XRsctSuWdlqqziZcfiAOVOKZnY7VKJdJY7dbtTpTNeJhW4yt9FWRXiHOlGZK7X5tvM8mPA24A94+sVwJY4ZgLUHltBRKQXPobOTyIiIlJDlwYN77m2K3Nm9gLgwZg681ltrH8mcCbAIpa0VQZFUqQVg/Z9yWIkNCuycH7KVCRFMq8v35cGEbZZiw/PvGSwGJHwUjn+P938bN1FRwJw2Kk3zWufIn2VblLZiKLP+TMgrYrnE5l7OvAiMzsRWAQsAz4O7GVmQ/Hud62xFQCIg+udB7DM9hmAQykiGaLzk4iI9M4g1AoWoC4MTdBzbVfm3P0dwDsA4p3vt7r7qWb2VcKYCZcSxlD4Vrv7yEM/MZFeqvW3oAjdbL04P+Whn1jPxX5SWcogKa2bkZUyPT2JxMWL1uqI3PovPim8Tq2z7pKjAFj9ihvjFEUvRCQbnMHoM9eN9g5vJyQbWEvoo3J+F/YhItIOnZ9ERKQzBjkDaJLcJf1otFyhOP1otF4mKJtlhbv/GPhxfL4eOLYT2xWRoJnodPUyitQFOj/1UAZ/5Bac6gunVB+16ojbjEGQa8yftY1C/D+OGedTk8B09G3mxmduqxLBjtHb6UidiPRc+jzRSkW0iYpZch7xciEX/QgHoR6unsgiIiIiPWRmRTP7lZl9O74+1MyuMbO1ZnaZmY30u4wiC4G7tfzImo5E5qR38tCPUH245q8Tn68+B+m1PPQjzOJ4cy2pvjOeHu9qrtUKNh1hq3O3PInUzYrglWZm9Fv/hccDc6T0nutiJ85b96WjgQUdoXszcCshORPAB4GPuvulZvZp4Aw0Bqb0QivNIGtko7VisXJOsaEhKBbDjIlJIDz3yYn5lrIrQgvZ7FXOWqXInIiIiEiPmNmBwJ8Bn4uvDfgT4PK4yEXAyX0pnIjkTi4jc4o0yKDqRsRVfel6K3cRn3qRnkFlfXx/7SQCaDgu29x9UipRtmIx3EFPi33fKqoidtURubUXPSnOSBaYxx3tJEJXFcXN3d9Pez4GvA3YM75eAWyJQ6YAbABW9aFcIrVVnYesYNN9br2ceu4wNVWZXlnPLLO/LVlMaNKqXFbmRERERPLGzF4APOju18dhU1pd/0zgTIBFLOls4WRhanjDCAojw1UTChBv9njqppBPVDWntFQlL6MyXLSmqTIn0kf96PuovnQypyR6NJ9fuCyPN5fFMvVC2cFi5G04XphNpu6gpyQRuZpZKrss930aG3s68CIzOxFYROgz93FgLzMbitG5A4F7a63s7ucB5wEss30G4DJUpL/UZ05EREREmuLu73D3A939EOAU4IfufirwI+DFcbHTgG/1qYgy6CwmQ0oe6VkFm/0oFkMkrjC9rE9OVR54OfXwqkc508MTOK1nssxi5U+RuZxSVst8ytLnpc9HgK60MUkyFCYZC7MYDetpBGiOsd9mLVrdry3p85bqn5Lm5RbuycaLsaQpVKVPXNzmrEhcHz+3BRChq/Z24FIzez/wK+D8PpdH8iTdJ636eWWZ+mNO1t5m6twSt+Nxu14qTZ+L5voNyUEbxuyXsDFV5kRERER6zN1/DPw4Pl8PHNvP8ogsOAMyNIEqcyI9kKWIXDVlu5SWpO/05uCua+5UN0lqEHlL7rBXsslVTU9vtxKJaxSRy5AFmu1SBlUn+iQ32nb18+rF0mNOMjNrbc2InZfxmKHS0/1sB+X8PwBvQ5U5EREREem+hTYUSrV677eTw5bU6qNmhZnDlAylLv+nqppyJ5tJ3/SZqhrGZIAoMtdjihhIXmQ5EteI+tK1J3MRg3oXB40unur076pEgNJjj1XfZS7E/l7VFxNZzm45H+1cgMH08UlfYNVbtCqK1rhMsV9crT52cVvrYyRu1jchB5/PAuxLJyJdNAj3E3JVmWtH9YWpLlR7T8c8n/S5dV/1hWlHL1Q79QsVKwNNJdrIcNayerJSOUgqX5XKXeWue2nm61aPcY318liJq5aVz02kK6zQ0t96+qaQlwszbhjNUvbK74MNDVWaUMaV43/eXIKTnHMUmRMRERERac5cFYNake708t3sb9ZvtW7W1Mo+mZ5WLFSyS6abSFqhXLn51jDyPzExPfh32aFcmnP5gePk8mZWtVxU5tqJDFQ3c2v0upu6GdnIwxAFC9EgfS7p96Ao3WztRAaqkzrMep2k9O+BZBiBhmrc6a1OvlEYCYNReyksu/rUm4BUgo1BbW7ZSPXFZ+WidPZF13QikxabVyaauBibNXREjqX/dhSlE5FWDcK9gVxU5kRERERkgNUaG63W/EFU46bNzKaTSTPs8nS/2PTx8HLNJpUzmm/XymBZ9lS/3AE+vnMZgLc9cJW5LEZCslgmkXaoH938VEfg6ppP5Ko6+mVVv1RV2241OmNM//ZVl9IKSR+LZIDZOu9joUboapgVkWu332EzF2KD3EwN9aPrm0aJgJrJ4JhKBjSjv1atQa/zlha/zURJ6YpYrWaWlWECwsJUovlVy01vq060P0/HsuNMfeZERERERERyaQDqsgNTmVP0S6R3NNB4a5qOyHVC9V3GRncd60Xw6i1e5zk0mfEyrdE+s3bH1GbfHQcaR9PmuDPv7Y7f1E6UbYHcga+ONjfdL1Ta06Gx06oHvp+1/fTmBjzK3AwrWO1jZg2yWco0VzZLEREREZHZmmr6W6eyYbOzMSYDXXupjE9Nzrt4fdHsECMz5henJ6fH+ay3jfT0hZadcoFSlV16RtHTwbX1yjX6fPPObWYkrPp1P5hPP5pdtk1z9h00mxFlmNGHZb6qtt308u4LOioxp6rv7rpLjultdFxE8sPbeGSMKnMiIiIi0nvl0vTDy7MeXiqlHuX4GOBoU52bNF72mYlh5qvVm0gDzdp4NNii2fPM7DYzW2tmZ9WYP2pml8X515jZIVXzH2NmO8zsrc28g9w3s1Q0QLJskMabk9blMhrQap+7Vswjctb0uvPaR73sm1VNvpLXwzH73mRVv7fqpk3V251PRK2D0bjKeHN5/J6KiHRChyNtZlYEPgk8G9gAXGtmV7j7b1OLnQFsdvc1ZnYK8EHgZan5HwG+0+w+FZkTERERkd6qvjGRRKXqPHxyAp+ciFG8nDQztji+W/JIoo5zLj/z5o+X0lFLT0Uy0+PMVT1vdHzycvx6ofPNLI8F1rr7enefAC4FTqpa5iTgovj8cuAEs/DBm9nJwB3ALc2+hdxH5mRuyjIovaDvmbSjkmWwOnJVK0lAqxcezWa7qzWOVR2VcZ+KM6dXxseq1/xLF019pXHnpOfSfWyL8YTh5emMv51qLpkeaF3nmdY53egbvgq4J/V6A3BcvWXcfcrMtgIrzGw38HZCVK+pJpagypyIiIiIiCxAbdaBV5rZdanX57n7eR0oztnAR919h7XQpzHXlTn1Q2osOUaKnDRPY6hJJ6z70tGZzHqVJetjBsnDXnlz6ysXZobHCiPDAHj8ZU5+CJPXVEfN4t3yyg9msQiFcNfcd4/PWDSJuFX6zMX/K6nSqyJ2bY8d14Tq/m2KOonU0Ml+qs3uK92iIN3PNjlf2BA+MTm97FzRuVbLq6hc+9o7dJvc/al15t0LHJR6fWCcVmuZDWY2BCwHHiZE8F5sZv8G7AWUzWy3u587V2FyXZkTEREREQHmaDbdRpPtek0XGzXNTk3zsk/f5HGvNLkM/eBUAcuEzjezvBY43MwOJVTaTgFeUbXMFcBpwC+AFwM/9HDn8Y+SBczsbGBHo4ocqDInPZCX6KAicq3TMZpDv8doy6Hqsds8HUyrutM+a5y3JNKWXCAlryfDnXAvzozkJRG5Oy8+HIBDTl8PcdlG/Vmm+8D4jOWTCF03ogKKyLWu0idTRKSO+SRAriX2gXsj8F3CiO8XuPstZvY+4Dp3vwI4H7jYzNYCjxAqfG2bV2XOzPYCPgc8kRCofC1wG3AZcAhwJ/BSd988n/2IiLRK5ycRkYXJisXpmyuzZs5MjmRDQ5Vlk+ba4UUByvFGTSmVhbJYnG6ePTyMFUMkrjy2O0wr+4ym2ellfWIiTHfDvckkTdI9XRoE3N2vBK6smvae1PPdwEsabOPsZvc338jcx4H/dvcXm9kIsAR4J3CVu58TB8o7i5CZRfooC9GxWn0cF0pkR+PN9UV/z0/mis41kPyG3nHJEwE47NW3AmDD8acpXvgA05G22E+tsO+KML2UXGDFpk1TpZqvfa89ASiPhPVtV+gXt2LPnQDceeFhs66pDj3jzrCvRYvCOkvC/6X7HwwLxAvAvA9inET5NN6cDJJ09N5r/Ymm+rpVMtQOD1cqcFYsVvq/hiXjOaUws2llpR9ceWakPtl2cu6iVJrddzcphyp0fWID8Tvd9jhzZrYc+GNCqBB3n3D3LcwcO+Ei4OT5FVFEpDU6P4lIVpnZXmZ2uZn9zsxuNbOnmdk+ZvZ9M7s9/r93v8spsiB0fpy5nptPZO5Q4CHg82Z2FHA98GZgP3ffGJe5H9hvfkWcTdGN/mo1ypf1qJT6yg2kvp2fpqMbGTzjZ5RX3xktV93ZBmxkZOa8GJGrZJ6Md+Hv/UyI2K0644EwPck4ORTvXRZjlsvRMH3567YD8NCH96jcHC8UZn52k0ceCMDI2rjN6jvw6bGeOkx95QaSWjX1QhLNLxano/xWmM4uGc8ptt9KLIngDxWxyanK+hYjaeW996S8KJwzitvHsV2hSWX5gYdSkbzkXLUIW7I4bGPJYsr33R+2XSrnPoo/kAbgp7rtyByhIvhk4FPufgywk3DyqYiZWWoeJjM708yuM7PrJhmvtYiISLt0fhKRzFGrgS5zB/fZ/eWsULkxVBgdpTA6Oj2rVIbd47B7HNs5hu/cFR7btsPEJExM4kMFvGB4wSgvjk0xy6FyFippqRtQBWPsDw5i7A8OglIJdw9DpHRqoHDprAUemdsAbHD3a+LrywkXSw+Y2f7uvtHM9gcerLVyHFzvPIBltk8GD81gmk/fueoIVr1IW71tLz9xbSb67sn85eDz69/5KUmNNQDt8Duu+pjEY5Uc4PUXPw6A1WesA6C413JYFC+6kqhXjMT52Fj4fzzccV933mFhfpjM2nMPmrGP5XuEO+n7/kPMbrkoJjqI/fMOe81a7rhwNQCHnh72bweEwO3wQ6FfXaWvTA+yV0r7Mh69nFerATM7EzgTYBFLul9akUHmDMRvdduVOXe/38zuMbPHuvttwAnAb+PjNOCc+P+3OlJSEZEm6fwkIhmVtBr4W3e/xsw+To1WA2a1E6brRniTyiUox8QjI0WKe+8FgC8erdyUKYzFG0M7dsFkuClU3rFzRlQvGYakWChgey8Lyy8awmPWSysWcZKkS3F/+z+K0QdSN4DqNa3UTZ9M6PTQBP0w32yWfwtcEtt8rwdeQ2i6+RUzOwO4C3jpPPdRkdU+V4Oq3eM9V/QtixGdrPfpk7b19Py07ktHhycDcJevaxr9asZjl2SQ8/GJ2Rc88ULrrs88GoCpqWQQ3rhusquqbe4aDxdfG/8tXFgd8KYdccHYf2ZoqBKRq1yUbdkW5g3HKF6lb1zsG1NI0pbP/bbmQ5kmB868Wg1IC5JmjZOT+O4Qmbfy7MHDfffuypAB1X3afCosX962HRsPlb/Csj3xSr+7YZiMz5PzxqYtFEZHpveRzmYp0gXzqsy5+43AU2vMOmE+2xURmS+dn0Qka9RqQCRjFJmTvJoretapCJX6x82W9yigPkuZU3V/wRbbr5TjeusvDH3n1vzVHTAVtxXvat/x2ccAMDUeI3J1tlU9fSJG5pL/138o/H/oX94dirpkMUxOztgXcfyo8o6qPnPJXflivPses99V7up3M1TXRXmPAma8r1xaT1sNLFTJ36OXneKi+HxycjoTbozGlXftSq1U+4xS3rEDdsbmmbvGsMVx7MmlSyvnCWKLAqamKG8PWXJnDDA+Ojq9rJcrLRBE5isXlbm8XvjmRbePb14qdVkvn2RM/IFed8lRfS5IhlQ3Ma2T8KSRpFJXHh/nrvMPDpuKq5armlM2XbSkCPH/UtzO+vNC5fCwv9owPYxBvOBKUo173HmliVYl8cnM7HTJIMXdSFqXo4qKNNDVVgMagHpa5TiUKY/FZpbFYmUogRmVqWaOWZL4aHIKyrECaDa7GWXyOm63cv5QM8tMUp85ERERERGRPBqAfu6qzC1wvYx6br1yjaJfOaXPrQarGqZzAH4QuqbNW59rP/94mOxsUSolKYfPLxke6s7PruKQvw6DgttB+wMwtTwM/Dt890Nh3Rix8y1bw/9JAgXdce8rRS1TrJDbZr5d447H5CXebuQytY5PTeIxqFcoFinsuQcA5UNXATC592JG18X8NZOTlDZviZtwnSuyJqPjxrVKlTkREREREVl4VJkTEVmgutExKu9yNGB6dR+6ctnwnSHRSZJW/JGnrQBg76E4ePjGLWHhbdviSlVXAfpOSL/pOzi3TvQndK/0maZUorw9DHNSWDQKwKZn7s0+8Zyx6J6tECNzs84XCfVz7Cv1mRMRERERWUhi5as8MYkNx+bWS0KGy9EtzpJ33wfAhq8dyqrLQ2XPd49PZ7kEPAc3vRYEVea6S1ksB09eMlt2W96HKBBlsZxTjm51VvLdlQqs/3z4e1z9TyFCN7otRDnu//vQ32b022Gg8kd9Ow5VEDPkleLQBcmgwdNDFOTnOKTlfYgCEZGm5fM0PUOmK3MiIiIi0qSc3kDItXjzxnaOAbDX73dyzzcOBaBQAkbCmJZWKGBjYRmNMZcN5rm691iXKnPSF/UidJvOfBoAK8/7Rc/LJLUt9CiqtKHNQcP7yX2638rmj4dpy98ZmkeNX7kciBdmgA0Pxydx8PCYKQ9lqus5ZbKUviqXKs0lyw+ErLfFHTtZ9cjeAPiSUZiK54VCoZIBN2QdVYUuEwaguasqcyIiIiIisvDk555jXZmszKkf0cKliJxk3XQ/ovxkbpyhOuNkqxkom4m25Sgil3DA49hzm7cvAWDvOzcAsN/W0CfOF4dsdSRNpGIfudlZLfP3/kWkTZXxJkMfW5ucwsYnZi9XMCxG830QahADIoc/V7NksjInIiIiIpIXSeIjn5quzFmxiMehC8IycegIDSGRHarMicigUl+5nKoXaWt0+7F6fqvrD5ByOd49jxE4m5gM/1cvmIw1VUjGnOpB4QRQXzkR6QAlQBERERERkYrydNTNF43AaGye7WWoJEvqQ7mkNlXmROan3XHnBiXrpcaby7F+9pVrpZ9bvduOzd6OHITblq2Kx/XOCw4B4NC/uR8AK4Y+deWk2VQhvE6yWHp137mc03hzIq1zd3x3GIPSNhvlXbuSGTMz3pqlV+phCWXQqDInIiIiIiILzwDUozNVmVN0QpqV94hcP6kvXHsyFZ2YKyLXbsQwh2PDdVv1zXIfHQHAFsVmUzES5xYidFYIzauU26B96gsneWWx76wVi9OZbmH6RFLWiSGLBuEnL1OVORERERGR3EiaS8abOl4q4Tt2xlm7KomUknmzqImlzJMqcyIZMJ++c4q0yZwaRdyqI3l5GzevCzweg+T/StKCrdvD9LHQH6Zyp706FGfVxzTfF2vz6TunSJuIZFq+T8+AKnMiIiIiIq0zq0TkkgRJDA/DZBjOhEKhMh8vz3wu/aehCTqndPgoWz+h/nILkaJKM1VH6HR8+m/80MWs+5ejs3H3rpUsllneRxbMEbG0OO2w034fJiR95BJ1+r4MWjbLatUROkXdRCT3unDaNrPnAR8HisDn3P2cqvmjwBeApwAPAy9z9zvN7FjgvGQx4Gx3/0aj/WWiMiciIiIiknmpaFxh8aLphEhJf7iyU06aVpdKM6NwM54P9s2f3Ojwx2BmReCTwLOBDcC1ZnaFu/82tdgZwGZ3X2NmpwAfBF4G/AZ4qrtPmdn+wE1m9p/uPsUcVJmTvqgXcRqU8ePmSxE5qak6mlQrelY9r1EbkvmOQ5dXtSJy8f/DXnULAB4z0vnOsTB/d+g7lyQ0qETiqptMDfhFmiJy82Nmfwe8jnAZeTPwGmB/4FJgBXA98Cp3n+hbIWVOFs8NtmRJpTLn20OfWgqp83IqWj8jcj/g54i8MLryU3cssNbd1wOY2aXASUC6MncScHZ8fjlwrpmZu+9KLbOIJquahfmWWEREREQaM7NVwJsId9+fSGiGldyZ/6i7rwE2E+7ci0i3eRsPWGlm16UeZ6a2uAq4J/V6Q5xGrWVi1G0r4UYOZnacmd1CuNHz+kZROVBkTnqsUcRpoUfkRJrSjXHmBlWTEUoDDnvlzeHFcPhptKHwfzq1+Ix14h14LyupgbRkCFhsZpPAEmAj8CfAK+L8iwh37T/Vl9LJnGxkhMJobFrpZUjOD8m4k6XSdJPL5HXlhSJymdJ+ApRN7v7UDpcGAHe/BniCmT0euMjMvuPuu+daR5E5ERERkR5w93uBDwF3EypxWwnNKrek7sDXupMv/WYGZqGJ5fAQDA+FGz7udStpg54UaSC0F5mby73AQanXB8ZpNZcxsyFgOSERynSx3G8FdgBPbLRDVeakZ9QPTHIra9Euq7qdOOj927rAqh5rXvd7isv2oLhsDwqjo+HOe6EQHmWHsuOlcniUfcaDcik85rioEwEws70J/WUOBQ4AlgLPa2H9M5OmXZOMd6mUIgtI5ytz1wKHm9mhZjZCaEZ9RdUyVwCnxecvBn7o7h7XGQIws4OBxwF3NtqhmlmKiIiI9MafAne4+0MAZvZ14OnAXmY2FKNzte7kA+Du5xFTly+zfXTnoFcKRSxpfj0yDIWQAKW8ZSuezlwJYAU1rcyRTt8LjZko3wh8l9An9gJ3v8XM3gdc5+5XAOcDF5vZWuARQoUP4BnAWbEJdhn4G3ff1GifqsyJiOSd2+xfpKxFE3utyfdvhdDPzRYvwkZGwqo7Q0Ixs9gnruFG4r500SaN3Q0cb2ZLgDHgBOA64EeEO/SXEu7Yf6tvJZRZrFisnB8oFisDhHuhMKN/XJiovrO50oXTtrtfCVxZNe09qee7gZfUWO9i4OJW9zevZpZm9ndmdouZ/cbMvmxmi2KI8BozW2tml8UQoyxgy09cy/IT11aGHRDphQVxfkqaW6qZ5WzpY1Pj+BSKZQrFMqv/8g5W/+UdoRlluRwe7uExNRWSn3gZvIyXSuGOe3w9/VDzSmlOTG5wOXADIVtdgRBpezvw9/FO/QrCnXsR6aZ2mlhm8FTfdmVO6XVFJKt0fhKRrHL397r749z9ie7+Kncfd/f17n6su69x95e4uzrE9ZtZaE5ZKFLYYymFFXtTWLE3AL57N757N+Xd4/jERHjEmz3hho/rJk9OVN/3a+aRNfNNgJKk1x1iZnrdy+P8i4CT57kPyakkIpfQsAPSYzo/ySxJwpPDTvs9h532e6xYCE2mhoYqiU5ERLACVrDwWLIYHx3BR6ebWhIHDhfpt7Yrc/NNr5vOyDS1dVetRURE2tLJ81Np+85eFFlERER6bQCaWbadAKUqve4W4Ku0kF43nZFp6RH7Z/DQiEhedfL8NHrYKsc8mwlFapUpaQOSxbYgnVTv86jzvgtxuhXi/0kygzgweHnbtumFqxIa+KwEBwN+bEXyxFo4N7vPXt5qxzUKI8PT54lSCZuYBKA8thvKMclJ0mdWcmsQfirnk81yXul1RUS6SOcnEZFB0qjSlqqUWWHmspXBuy01L1m+YJXMtRSLlcqZjQxjixaF6cPD0xU4GSwLvDKn9LpSkwYHlwwYzPPTINxC7JR6xyKJ2FXNTyJyh712XZiQ3HGPF3RmhpfCxZqr35yIyODLaLPJVrVdmXP3a8wsSa87BfyK0Czpv4BLzez9cZrS64pIT+n8JCKSQenoWp3mjTMWTyUZKSxdDICXytPjvJXKMyNmhXhzZih1eVsqTQ/sDakmknFaatw4yg4xcmdLl1ZW8bExiM0sfXKK6RkDUBNYwJKEWHk3r0HD3f29wHurJq8Hjp3PdiWfFJGTLMn0+SmJGjXbD69RRE4Ru2lJ37jkZXy9+q/uCq/33gsA37EjLDAeMsCXJybrD/arCzaR5s3VHDLdFHK49iWopdcfHq5UtGzPPcP/xcJ0U0ioVLKI40OGbQ/DVKx0FYuwe/fcZUv+xs0r/WgpFCoVO6aKMByXGR+Ey3+pGIDT+7wqcyIiIiIiInk0CPdCVZmTjtt05tMAjSsnUle7mTHr9AdrOG8hSyJ1MZGBL4p95XYmyQ+U1EBk3gqhOaQVbGYTR5gxHpstWVKJtE2u3h+Pka8dB41SHA9/q0O7ywzviFG1kuPDYfnxvYfDriad4nj4u7UpZ9Edm8KyE5OVCJxPTU1H4aampsePLIDvjuOxJ1G8dHlHRytPfff4dHPOHTsry89oZin5NwA/marMiYiIiIjIwqPKnMi0rVeuAWDliYrIyYDp9xhzzew/hxG56nc11zuoXtYavN/iULhzX4iRt/s/uxyAR//NLgDKsa+cxosSmb8kUYkND2GLYzr/GPGykREYCvN9ZJjSkhD9eujoJexeGRadWDOGFcPf38itSyjH/nSPur7E2IqwbikGzQoTsGhrPCM4jD4Q9zcyDLvGpgs1NB0RJGazZXIq1T8udVYp1DjHVk/T0ASDx3P50zmLKnMiIiIi0h4zbCQ0gbTFi0LyEahU5iYO2ZfSaGiu+NAxo3isY409cYzy9rDsuU/7Mt/ZfBQArzz+53zmwWcB8JODD4ctYd1H/TKsV5xwhndMZ6ScWh4qc4XxKQrlvUM5xsZDBkoITUBLpeniVjcDXbxoen65VGmS6WNjlRtM5STJCujGz6AZgI9SlTmpS9kpRbqkXjbLRlkuM34LsdX4ZSW6lvRrmysCWfXeDzvttriNuE4hSS0e+9Ik48jFPjDJxWblbnxcznVRJiKyYGX8Z7UpqsyJiIiISNsqzSzTUa+YPGR8xTBTo0kSE2doZ7ihMrpokt2bw02Xt//6LxjbGZ4//2k3VTbhpQLFGBQrjYb1ysPGyLbpSFt5pFj5f2RHbD49MgzjE3Ej5VQCFJtOyFKJxvn0tPHx6chc6kaPFQxPtqEbQINlAD5OVeYGnKJrIhlWfUuwOiNlowyVHbilWK8vmsd913ud3n8S7BoeLsVlw+tC7KdSLtuMbSQOPu2OsH4y3lT1RVS6j0r1BVTSHydehHlqAGAAW7J4xuKeZLlLBv4tpfrK1dq+iMwt+cO3Ah4rRmapylKcPzVaqFTEHv2U+9m+O0TLt2zag8UPhmVXXrkEi3//7//O6Ww9LEbaV03hjw4VtCU/mc5mufjurQBM7LcnNhWj8SXHR8N5wYeHsK3bwjbKqfHnrDhd7nQFLirv2lV57qXS9Lh49caglNxTZE5ERERERCRvHEXmpHeyFGHTOHIiHVKvj1gXs2fW23IScbMYTTN8xvRakbkk8nbIa+8O8/bcI9nYzI0n4zNNxfGZkuZKMetdkjDBp+Ld/SSL3Pj49J3xZFq5OpoZ78onN86TKF91X7q478q4UZU3XkBkYKT/9tLRZrPa0efqv9VamohaV6LrhQI+GaLfFrNJDu0uM7EsPC8WyiwZDc0ft0wZix4K215y9zZs204AFu29ByPblgGwqTTErkPCMpNLw99qJfkJMLR1HJI/4VTwzNynzyulMpY0qSyVpptXJpG5YnH63GSFmVE4Re4H3wB8tKrMiYiIiAyKmpW2AjNqO+npSYUldWOjUjlLZYH0UqmqghiWt2IRW7oUgPLK5fhIWLc8FLdnUAytnNlzZJybb30MACuuL7LyV9vDsmvvnN7X/QWW/D6se9C9q9l++J4AFKbCvou7S9j4ZHw+EYYbgDAUQdJ8eqhYqVRSKs0cZqDS9y0mQUoPAu7l6b5xMvAMNbOUFmUpujYfisiJzFOzkbdm+86lFKqWKRRnXsAl0bTE6HC4kNn/dQ8DUD7k0WGXxbDPXQcsAaA4HrZT3F2muCusU4h9VaaedBgAE8tjn5bkoms8Rtri6+G7N4WdxossHw/b8dJ4Uvjw/1TSZ84h3jz3sXg1mPSjS9KfRzYas1cmUcDkQi5ecHoy/lSSxTJ1kSoiIpJXqsyJiIiIDIqk6aT7zGaUNZoUhybNxdnTk/WSIT4AJiamb4LUaXbohcL0vap4c2Z8WRGPu775toMYeTjsb+nGEsVHdgBQKpVSEbFSJWpWvP9hlsV9ja8MSY2Kk+WZ0bhk2BH36Zs9kz4dgUslQIHpptbThfbazU3rTZfBosjcwjQoETYR6ZNmx5NrFMGrEbE77NW3hknxgsUWhcxxDIeLssp4a7EvmY/G14/aB4CHnxj6ve1eEba985CYoTJemO25dhQfCttc+esQ/dq9d7g4SzLWFSdCeUa3ziz/0KbYR24y/vTEDJPT7zvpd5NMSPVlSS7SqseTK86+EJ2LxpWTgZf+jidZHIvWuPlgurKXRL7LZQr7hIG4fWwMj+n+fWKiMpajjQyz+3H7A/DI40bZ+rhwzhjdFP42CxMwtTSWY1GJ0UfCtpdevY7yjtBPrvJ3nhQ7/rlP3f8A9lCI6C/aIzTltOXLppcbGca2bo9v1aczU1ohNWh46n0Vi5R3js3YiZd9Oitu+hipIrcg2AD8JqgyJyIiIiIiC4uyWUq7spYNMmvlERl47WarnKPvXLLFJHGBjcaIXIzEJYP5Th20EoDycLgTvelJcTy2ePM6uate3BEm/MX/+iUA1z0ckhY8+xm/4z/vfSIAdx0YtjUcI3D73hibRo2H8g3tSppkhf/Ke8bI3ETsc+d7hRmxP1xy17/Sdy7dr61eH7kk4UH1nf3d4Q69V/rQxfnKTic9YGYXAC8AHnT3J8Zp+wCXAYcAdwIvdffNFto0fhw4EdgFnO7uN3StbIW5m16mszna8BDlvUO03pYsohCjWr579/SYjotGmdwzPJ9YBsWxsM2pxTEaNwpTB4S/7cKDIyxfH6P94xOzInK1JE07K3/TpS2VFgbmPmMbyfiRNpR6X+VyJTpnQ0Mko5B7KTkE5emsuIUmIpgyUJQARURERESqXQicC3whNe0s4Cp3P8fMzoqv3w48Hzg8Po4DPhX/n5+q/nJWmJ21srB08fQie8ShRdzxZLoZkytCEqTCRJktx+8LwNC489DRYfuFEiw7KiRQOmafB7l7e2iWWYo3n+67cyWUw/NDvj3B6LoHAZjasaPxTZXU/HLSLHt8HBuKN3d27Jw9XAlUKmyVzUwmyZZKs2/sNLFvGWAD8DGrMtcHrUTA2o2atbKeInLSKVuvXFN5XnrTz/pYkh5otl9bO+azzeQCbmio5vSJvULELunfNrE8Lp50I1kUblcXtoYLvst/9ZSw+q5w533zirunN1mKF3NT4f/SSPi/PBz+H94xswjlkdi/LUYFbVe4016JsiV32JNomvt0IobqceaSC60kmcFEqbIOTN+hn5GSHKYvZF3ZLBeadZccU3k+/q6fd3Vf7v5TMzukavJJwLPi84uAHxMqcycBX/Dw5b3azPYys/3dfWNXCykiisxJZyUXwukEK+1WtFRBk35If3eLPt7HkvRQP38JUvuuDOadVHIKySDfsTITKz1JJa40Gl5PHhVqXGPj4eeg8EioWI0+HJY78KqwvSd/KDS3vPr9x7Lr4JjYYL+YZe5RoWK0+BexIjgZKlIj924BYGplGCfKkiZQSUUrNgn1JInJ1m0z31+5nBoEPFbCkqEKJqruvCfJDpLX1UMP6C77grf61F9Vnm/ysTmW7Jr9UhW0+4H94vNVwD2p5TbEaa1X5up9zwsGyZ9EdUSqMnh2khRphMlHhb/Z4niJ4liMaplVEiOVh42n/NHvQuEXbWEoRv0+uN+NHP7j04Hpc9LwliJLN4S/45EHHsK3b2/5bc3gPv33nf4z93LtZqPJPIByan562fRYe40idjJYBuCnQZU5ERERkR5ydzdr/U6QmZ0JnAmwiCWNdlKJyKcHxraCTQ/4vXhxpV9taWXMElkoMLQ93Iwrjw7hxaQPXJHtjw03UQpLpzj3Mf8JwGXbH8fHb/oTAH7x4KGUNofofxLlf8zPSiy5M9yoKa+9q+HwBk0p14msx4i7l0u1s1Gmb4DFG15e9umKXb3tymDy7tyPNbPnEfrBFoHPufs5VfNHCU2wnwI8DLzM3e80s2cD5wAjwATwj+7+w0b7y0Rlrnj7eFvp/tNNuvKs+n1svXKNhj8QyYjRO8Zm3NGvqL5QqLowWfelo+tuM1nTq17X08pvTZLoJInIJckBkunF3eGu88Se8WIuCdwNJ+MzhQmLN8W76u++H4CfPRAGBl/xprsZ/pdVYd1SiOLtOChe7C2JiU92zIwSFrcng4LHQiY3vivjYSWRutjcMrmgKpWnm0vGlZL3VXl/pUoWg5nbEsmeB5Lmk2a2P/BgnH4vcFBquQPjtFnc/TzgPIBlts8AxBRE+qzDf0VmVgQ+CTybEGW/1syucPffphY7A9js7mvM7BTgg8DLgE3AC939PjN7IvBdQpR+TpmozImIiIgMuCuA0wh33k8DvpWa/kYzu5SQ+GRrx/rLJTeZqruOVg+cDZUIXHHHeOVGS3FqAi8mTbOXYpNhmfJ4kfc/+EwAbt3yaEr3h4Qp9+wYYWhnWGZ4Y2i+ufj+bRS2hKaVU6VS72641Iz8Te97RrfZ5GaQmZpkLyBGVyJzxwJr3X09QPy7PglIV+ZOAs6Ozy8HzjUzc/f0neNbgMVmNuo+d7+VXFfmOhm9ylqUr1b/ORHJoDo//KtPvan+OtVRpKp+HpWBsOP89Rc/Ie4qRqOqNweVXyRbEi6qfJ+Q2cRjspHyUOpihenhA0ZGQuRu9+/D8ituDtP3vnkrAHdsCoOJl2M2uoe3LqXwuvC7cuj7Qn+7natD86zCZIzEjccoWmzalfxP0s+t8v7iOxmKA/bGKGI61XglAld1nGelNE8NADxzui7MpPfM7MuEZCcrzWwD8F5CJe4rZnYGcBfw0rj4lYRhCdYShiZ4Tc8LLLJQdf43olYf2OrstJVl3H3KzLYCKwiRucRfADc0qshBzitzIiIiIlnj7i+vM+uEGss68IauFijdF2x4dHrfy/YIiYaAoU0xMUlVcqHdR+4PwK59hxjeHG6ujN5R5Lfnh5tMxW3jPHY8tBj1xSPsPiAkTylMhO0W7txIedfY7HL0Q70Ld2W3XbDajMytNLPrUq/Pi02gO8LMnkBoevmcZpZXZS5KImDzidC1OoxAM/tShE4k4yr9vqp+EeplVSM9aG+x9gKVTJThFL36NbcBsO7zj427mtlmysxnd+ErzIzEJYPmTiyLUbA4eWxtiMiNbgkTltwfBvfd+W9hTKfy5nBh5qkscOX4XjedEy7W9n17iOJN7hMSMthUMpB3aca+k0QLFckg4eVkIO+q5lfl8nQUslCYNa/m9ESleVmdz0dkoSqVKhlkCxOT038j8e/Rk/HcABseZnhbmL4EKMW/4eV37Gb47k3Ty8fzXWFHkcXJ338yTMj4xIwELCKZ4bTbZ26Tuz+1zrxm+sAmy2yw8EO/nJAIBTM7EPgG8Gp3X9dMYepfbYiIiIiIiAwoK7f+aOBa4HAzO9TMRoBTCP1i05L+swAvBn4YM9zuBfwXcJa7/0+z70GRuSrVEbBWInXdHNtNETqRjOlApCfp32VVA1snmSeTfRT23guANW8MzfB9d2hCv/6C1QAMDZUoFmOWyjWhSdSWw0PfuW3xFDYSm0cVYzBsKmY1L8f04aObw729Rb+6I2x7a0igVS7XSJQQw3pbtoWNbP2nsO81Z4abiLZsz5nLj8aI3NYdyRsP/yfNuZIU4btrdA2IEYRknlf1jZuRXlxEGvKyY3GANn94c6U/qsWom09MUthjaVh4ZJjxfWKzTId9f/5QeP7wZkpbtla2l7CCwaZHwovkb3NioptvR2R+OvzTEfvAvZGQibIIXODut5jZ+4Dr3P0K4HzgYjNbCzxCqPABvBFYA7zHzN4Tpz3H3R9kDqrMiYiIiCxQSWZLW7w4TjB875DUyIcKlEeSpspgyU2VicnpSlyqebSXmO6ENGNAb91skWzqxjhz7n4lIbFRetp7Us93Ay+psd77gfe3uj9V5hroRF+6avPZVrLu5Df3BbobDRSROTToi2UFmxUtqkTgkgF7C3O310gidL4s3iVfHO6QF3aGZAKrXx8idfdf9ChGh0OflKk99gBgYlmMxMXuL6VF4f9yDJKNPTrse/ThEPladleIkt11XojslScbjX6XuqEZo3frP3cwAIe+Zn0o/0gYN85i/7bqDJTJGHFW1XewEikwq2T29EJct06egkqErqREBiIisnA07DNnZheY2YNm9pvUtH3M7Ptmdnv8f+843czsE2a21sx+bWZP7mbhRWRh0/lJRKRFXsZLJbxUorxrF+WdY5R3juGTk/jkZLiJ5A7uWMlZfN8Yi+8bY4/fb8Z37MR37KQ8Ph4icl6uLFt5lEszH4rKSVY5s7+/zTwyppnI3IXAucAXUtPOAq5y93PM7Kz4+u3A84HD4+M44FPMHlshl5afuHbe0blORveGT47t1uskQlUfO+mGVr7DpTf9rIslqbiQfp2fZmWvrB6VtzA78pZkfFu8aObkpUtmbjPOTwbxndonROYKEyHqtP3J+wFQjKm/d18zxA1v+A8AVr8yDFG1Yu8tAIxtj+POJX3f7gmvk07cB313Z9jme0J/tokke2XNN11bsuzUVIiirf3s4aEsp8UxUnfsnLH8HV98HACHvvJ3Yf3CzIhdZWy5Uhkf2x2fx4hbVcbLhhnFM/jDK92x7pJjmlvQnPF3/ry7hcky99nnK4Akcj48THlRjKpPlijsTg1VEFsLWLE4fY7QQNuSY91oZtlrDSNz7v5TQue8tJOAi+Lzi4CTU9O/4MHVwF5mtn+HyioiMoPOTyIiItI2b+ORMe32mdvP3TfG5/cD+8XntUY9XwVsZABkMcrVKFKiCF19OjZBJyPGGZGN81PBZvfvSjIxJn3CkqyVyVhpo+Fu+NTKZNDdsIHCriTrY/hv997hrnp5OKy/+PhNPPvWFwJw+AEh6dVbHvMDAP76+zH7cbwRv3hbeLI0vuuhh7YBsH0sRAc78TuVbGPdRUeGXcdbn5Xx8eJxWf+FxwNw2Ktuqbm+l73SZ64iGb+veky6AZNEmVaf+qs+l6S/mo62tcIb9wcdeMkYcGWvtCAoxwi6FYvYzl3xeQGGw3mJgmHDsdPt4kVYHDvOSyUNui25ZAxGZG7eCVDiuAgtHwozOxM4E2ARS+ZbDBGRWXR+EhGZW2WYj3jfxEslCkOpy8MkcdPEZKUJuO2xtJLZkvHyoN9bkUGV0T5wrWq3MveAme3v7htjM6Vk/INmRj0HwN3PI/b4Wmb75P9IziELkY96ZRj0qFQzx35QInRZ+J5lRH/OT8kPQtLfa3K6H9isLJZLYr+1eJe7vE8cly1G6Io7wkVSORmfbShMLy0Kp+yt8aMuLwl3w//PEVfxnYefBMD//PoIAN704MsAGN4SrtBKo6F8j74mbHvRvSEit+4DsY/ceAvRiiYjG14dkaueH6ev/cITZ0xPlj7s1Jsa3/BPInXl/EUGmok6DUqErisRNhGReRqEyFzDPnN1pEcuPw34Vmr6q2PWuOOBranmTiIivaDzk4hIMyoZKctQKkGphE9OUR7bHR47x2Bsd3gAPlTEh4pgFqJzeywNzcXNaidVEcm6hdBnzsy+DDwLWGlmG4D3AucAXzGzM4C7gJfGxa8ETgTWAruA13ShzLmRh0hJuox5jkzl4Vi3Y1DfV6dk8vxUidDVmFeoc7ETI3K2Mw4KFy+KClMx2hSzWpZGY1bL2IUuiaZdcM/T2bglDPI7+mCIxE3tCM1DR3aFZRY/aHF+6BfzyIdj1GxrjCRmqB9Rpc/dJUdVpq0+9aY6C8f2XQ3G/eunQY1KDer7WqhmjIuZOldV+viWSpD0YZ0qhWaXyfwM/t2JNGMQInMNK3Pu/vI6s06osawDb5hvoUREmqHzk4iIiLTFgXL+a3PzToDSCaXDR9n6iTV9iQy1019qUPpYVctyv7peRKiy+LlWl0WRut4bP3Qx6z5wTOt9ltJ9uIojM+ftGSJslEJUqbB5e9zZRPg/RpkePn+PsPgHw/+7V4TtjMSMlCN3h8WHv7SMQ7aHqN7YuWFbi98Ulp14dOgTZ5NhX3Zv6EK4deeqUMxWI3KtLN/BaF86Spe2+hU3dmwf7epFhCqLfeeqy6JIXQ7NiKilspiUps9fPjF9XrKN4fxRnpqajoaXq8atU5RO8mQAvq6ZqMyJiIiISDakm1xascYCZtNDqhSsMoSIl13DFEiuLIhmlr3UatShnQhKq/uYa/l6kRxFT6Rbku+avmO912rUYUbUopK3O1zwWCrTJYDHFN8bPrMizI/Th+Ld8eKOcGd8UZy+7M5RAJ7096Ef2U0vWsXYRMh8WRwL+9jxf8Mv1L5/tyXuJLy+67wwTnppMjt95JpSHeUbhF/gAZN85xWhE5HcGIBIcqYqcyIiIiLSB3WaXCbDrDhgU/F52bHheAlZ9ukhQijNzmo5ABfLMrgG4b5gLitzWerTBNnsazUo+hGBysPnmS6bonTZUqtPU2VQXkKkrfzIljCjHPuxjYT+bVNTIaq2dFGIxI0MhQunyb3iOHTxR2ev6x4A4Or7DgFgbKyqT17KtveHlQ595e8AmBg/Mr2p2TKU1XJOGShnPyJQWew7Vy1dNkXpRCSzMjrUQKtyWZkTERERkS5JIm3JGHQkL2sMT1ywMNYcwJRVkqdM97srKzonmWSADcB3M1eVuU5ESupt47v33QjAcw84Gmgv2jGoEZLq95XliNVCpH502dBOpCRJGmCLQh+4Q/7y3jBj333C/3H+2MHJhVX40dn873HcubFCnDxHlCpeh63/wuPTm2hd0hYlAxGxtHVfOnrG6yxkt5Rp6keXU+V6SUxmN7+c8dzLMyuCEF4npw1XxU4yptx4kazLRWWuF5WHpBIn2ZCFikkemlumKRFPfzRViYsXRpVK19hYeB1nF0ZC8hKSu9vJxU68s73o/jDQdzKo+CPjofI3OTmdZq7e5VHDwcCT+Y06DmSsElfRh3JloWKSh+aWaRrGQESkO2rEy0VERESkXWZ2gZk9aGa/SU37v2b2OzP7tZl9w8z2Ss17h5mtNbPbzOy5fSn0PFjBsIKlEqEQI3IFrFisPLACFIrhUZ0oxWz2Q6TLzL3lR9ZkOjLXz4iImq5J3uk73F3ziYhUEqIU4sVKHDycodhscjQkNEmGMLDxybhiWG/V68LAvXd86gBgjhZRzRiEVF6SOwug+eWFwLnAF1LTvg+8w92nzOyDwDuAt5vZkcApwBOAA4AfmNkR7hkcsC25kDWb2ZcuXVKrihOklrOCzRjDrmaFLYMXyzKgBiQBiiJzIiIiIh3k7j8FHqma9j13TzqaXQ0cGJ+fBFzq7uPufgewFji2Z4UVWbA83Dxo9ZExmY7MZYGiG/2Rhf5feekr10gWjqVElbva8WW8Q13etStMTu5Sx9dJAhSSPnWRDYfXh565AYB1nz4IL4d1y632IctqX7gMy0L/r7z0lWskC8eyT14LXBafryJU7hIb4rTsSUfSqiNwlclhmUoErmo5S7r6+nSzTJ+a7GgxRZo1CI1TVJkTERER6REzexcwBVzSxrpnAmcCLGJJh0vWhHRUolYrULNZk32yNGP+TKXZ2xXppQH47mWyMpfFiMjyE9cqotFHipB2jiJ189PNiIh70peuTgv4qdhCa/HisNzScIv7sNesZd0F8XNUpK3nFkD/r54Z9EidmZ0OvAA4wb1yFXkvcFBqsQPjtFnc/TzgPIBltk/+r0JF+snBBmBoAvWZExEREekyM3se8DbgRe6+KzXrCuAUMxs1s0OBw4Ff9qOM81YrypGOxqXn12mmKdJT6jPXWVmMyKUpOtR/iip1nr7XzeloRM5n3gpMD74LQCmOSxdfmsdIXDFe/CT/T4XlbMliDjvtNgDWfv7xTZZBEbxOG/SoUj/kNeppZl8GngWsNLMNwHsJ2StHge/H/rFXu/vr3f0WM/sK8FtC88s3ZDKTZbPmutidUbEbgJCI5F/26mYty1RlTkRERCTv3P3lNSafP8fyHwA+0L0SiUgtWRw3rlWZqMwVbx/PfFQOFLnIom5E6vLwXewGRT1rG71jrPP95NJjNdWSZLFMssGVwx3syk/OUJhvMTLH1BR3XnxEfN7Zokr7uhGpG5Qslq1S1FNEukKVORERERGRJgzAhbMMEAcGoLXvwFTmkijCQo2qdFK9Y5iHSI2iS52jvnSdk0QRZkVVkgubpHtMjNTN6kM3ZXF2nP/AprhcHJupUKAcx5lLYn0NL5mSwXVy1neuXmQqD5EaRZc6J6996UQkOwzvSjPLmOzo40AR+Jy7n1M1fxT4AvAU4GHgZe5+p5mtAC4H/hC40N3f2Mz+lEpIREREREQWng5nszSzIvBJ4PnAkcDLzezIqsXOADa7+xrgo8AH4/TdwD8Bb23lLQxMZC5RHUXoVKRuIUQnGh2rPEY9WxkfMI/vrxfSx2Uh/B10U3UUoen+T0nfuWJVFC2OR3f3xYdiSTCvKuI2KI2aGh2rPPYlW33qr5qOLOXx/fVC+rgsyCidmZouihRiP/N2csB2/u/nWGCtu68HMLNLgZMI2WoTJwFnx+eXA+eambn7TuBnZtbSxZYicyIiIiIisrAkfeZafcxtFXBP6vWGOK3mMu4+BWwFVrT7NgYuMtcttaI2ilLkQ71+dIrEtU796Lqscoewahy6qZjNMom+TUzOmH/QKbfPGrNp/cVPiCs3iNBZ1ZzqPnTV8+daVlpWrx+dInGtW2j96LazedMPyl/dCWzqd1lqWEk2ywXZLVtWywXZLVso13RE7uBWN9Bmn7mVZnZd6vV57n5eOxvqBFXmRERERHLG3fc1s+vc/an9Lku1rJYLslu2rJYLslu2PpZr0xz7vRc4KPX6wDit1jIbzGwIWE5IhNKWganM9SPKkuesjwuZInLzp6yhrelJlCWOTWfx/8NO/x0wOzPmui8+ae7tzBWJk65TRG7+lDVURJrW+T5z1wKHm9mhhErbKcArqpa5AjgN+AXwYuCH7u0XZGAqcyIiIiIiIs1pnJ2y5S26T5nZG4HvEoYmuMDdbzGz9wHXufsVwPnAxWa2FniEUOEDwMzuBJYBI2Z2MvAcd/8tc1BlrgvmG7XodfZARapkvhSp67CGPy5VfepKVLJ4+cTEzEUt5rmKfepWv/LmGa/XX3JUeNlk0Va/4sbK83VfOnrmzC70oVOkSuZrwCN1feun00BWywXZLVtWywXZLdv8yuV0JRusu18JXFk17T2p57uBl9RZ95BW96fKnIiIiEgO9TPpwlyyWi7IbtmyWi7Ibtk6Uq7G2SkzT5W5HmjUt26uyJiyB0oeLT9xLUUf73cxBldyJ9FqRMKsasSZJMtlnG6FZJ3Yt64qUpdYFyN26Uhc9T6TeZUIXdUYdyJZtPrUX7HJx/pdDBHJgDazWWZKw3HmzOwCM3vQzH6TmvZ/zex3ZvZrM/uGme2VmvcOM1trZreZ2XO7VG4REZ2fREREpH3urT8ypplBwy8Enlc17fvAE939D4DfA+8AMLMjCZ34nhDX+Q8zK3astANm+Ylrm+6vlizbyjoiC8CF6PwUomr1HlaYEa3zsuPl6R8jK1iI1lUtt/rUm1h96k3T+zALjxo/aKtfceOMR8cos6ZITWb2vHhTaq2ZndXnshxkZj8ys9+a2S1m9uY4/Wwzu9fMboyPE/tQtjvN7Oa4/+vitH3M7Ptmdnv8f+8+lOuxqeNyo5ltM7O39OOY1bkpWvMYWfCJ+L37tZk9uQ9lq3nD1swOMbOx1LH7dMMdOFD21h8Z07Ay5+4/JWRaSU/7XhyxHOBqwhgKACcBl7r7uLvfAawFju1geUVEKnR+EpGFJt6E+iTwfOBI4OXxZlW/TAH/4O5HAscDb0iV56PufnR8XFl/E131v+P+k3HBzgKucvfDgavi655y99uS4wI8BdgFfCPO7vUxu5DZN0XrHaPnA4fHx5nAp/pQtpo3bKN1qWP3+sabbyMql9PIXCOvBb4Tn68C7knN2xCnzWJmZ5rZdWZ23fijSuoT1qJOReiqM2fqc5ABM+/z066V5exlv0uiZDOmFWo/qlctFsNYdFXzk+nVj5r7amD1qb8KGQTN5xVdm5E585Jjsvc5iPTHscBad1/v7hPApYSbVX3h7hvd/Yb4fDtwK3XOrRlxEnBRfH4RcHL/igLACYRKyF392Hmtm6LUP0YnAV/w4GpgLzPbv5dlm+OGbbs7yX1lbl4JUMzsXYQ7Mpe0um7MQHMewNIj9ndoLiGIzNSNlPDV29Dn0Tp9l/uvU+en0cNWOUynM89EqvxaPyZeqr1sVUXMJ5tbbs59Nam6yWUnKmPV28jE55EzmfouSztq3Zg6rk9lmcHMDgGOAa4Bng680cxeDVxHiN5t7nGRHPiemTnwmXhu38/dN8b59wP79bhM1U4Bvpx63e9jBvWPUb2bohvpj9cCl6VeH2pmvwK2Ae929//XcAsZrJy1qu3InJmdDrwAODU1avm9wEGpxQ6M00REekbnJxGR3jKzPYCvAW9x922EJnirgaMJF/sf7kOxnuHuTyY0D3yDmf1xemb8fejb1byZjQAvAr4aJ2XhmM3Q72NUT40bthuBx7j7McDfA18ys2VzbmRA+sy1FZkzs+cBbwOe6e67UrOuIBy8jwAHENrU/rLV7Suq0b52hjJotGy9+Qv981GT1Gzq9vkp91GNJALXh7uRyTFrJULXaNl683P7+XSImqQOtMzdmDKzYUJF7hJ3/zqAuz+Qmv9Z4Nu9Lpe73xv/f9DMvkFoovqAme3v7htjE8EHe12ulOcDNyTHKgvHLKp3jDLx3UvdsD0huWHr7uPAeHx+vZmtA44gRDjr8FnD8uRRM0MTfBn4BfBYM9tgZmcA5wJ7At9PZ4xx91uArwC/Bf4beIN7vbY/IiLzo/OTiCxA1wKHm9mhMbJzCuFmVV+YmQHnA7e6+0dS09N9qf4/4DfV63a5XEvNbM/kOfCcWIYrgNPiYqcB3+pluaq8nFQTy34fs5R6x+gK4NUxq+XxwNZUc8yeSN2wfVH6hq2Z7RuTA2FmhxFu2K5vuMEB6DNnnoFCLT1if3/CJ06fNX2hR346qZl+cM1Gmhb65zKfiNxCOnbX+FVs80dyP3r06GGrfNUH3jBr+sBFfqr7zPXwt6GZfnDNRpoG7nNp0Xwicgvp2OX9/BRT1n8MKAIXuPsH+liWZwD/D7gZSMIc7yRUVI4mNGa7E/irXl74xwv6JEPkEPAld/+Ama0g3Nh7DHAX8FJ3r04A0ovyLQXuBg5z961x2sX0+JjFm6LPAlYCDwDvBb5JjWMUK+7nEjJM7gJe4+5zRL66UrZ3AKPAw3Gxq9399Wb2F8D7gEnC9/C97v6fc21/+ch+/r8e/fKWy/Xf93z8+lR21L6bVwIUEREREemtmLK+X6n+Z3D3nwG1KsZ9LZ+7rweOqjH9YUIGyb5y953Aiqppr+pDOerVZmYdo9ikcfbdzS6pU7bz6yz7NUJT31Z30vIqWZPJytxCil70Sjt96epto5/6mWlTmUIFBjB6US+LZQ/71lX60n3p6Hlvo5/6mWlTmUJFRNowAJW5TowzJyIiIiIiIj2WycicdM9ckaBORO+6JQtlyvLxEWlbhu5KVo9LN2NeG5kweyULZcry8RERyaZsJjRplSpzIiIiIiKysDhQzv/QBJmozBVvH1ffoQzJwmeR5eiXInQLy+gdY+o7lCFZ+CyyHP1ShE5EpAWKzImIiIiIiOSQKnMinZWnse4UoVtAzAbihC/zk6ex7hShm4NZaF4lIgucQzn/JwNV5kREREREZGFxcM9/nznzDNxtNrOHgJ3Apn6XpY6VZLNsWS0XZLdsKlfr2i3bwe6+b6cL02sZPz8N4vem21Su1mW1bPMp10Ccn0SkfcuH9vWnLTu55fW+u/lz17v7UztfovZkIjLn7vua2XVZOjBpWS1bVssF2S2bytW6LJetF7J8fspquSC7ZVO5WpfVsmW1XCKSIxkIas1XJipzIiIiIiIiPeOuoQlERERERERySZG5jjqv3wWYQ1bLltVyQXbLpnK1Lstl65WsHoOslguyWzaVq3VZLVtWyyUiOeEDEJnLRAIUERERERGRXlleXOHHL/qzltf73q6LlQBFRERERESkb5yBGGeu0O8CAJjZ88zsNjNba2Zn9bEcB5nZj8zst2Z2i5m9OU7fx8y+b2a3x//37lP5imb2KzP7dnx9qJldE4/bZWY20qdy7WVml5vZ78zsVjN7WhaOmZn9Xfwcf2NmXzazRf06ZmZ2gZk9aGa/SU2reYws+EQs46/N7Ml9KNv/jZ/nr83sG2a2V2reO2LZbjOz53azbP2mc1NLZczc+Smr56ZYNp2f2iuXzk0i0jlebv2RMX2vzJlZEfgk8HzgSODlZnZkn4ozBfyDux8JHA+8IZblLOAqdz8cuCq+7oc3A7emXn8Q+Ki7rwE2A2f0pVTwceC/3f1xwFGEMvb1mJnZKuBNwFPd/YlAETiF/h2zC4HnVU2rd4yeDxweH2cCn+pD2b4PPNHd/wD4PfAOgPj3cArwhLjOf8S/4YGjc1PLsnh+yty5CXR+mme5Fvy5SUQ6wwEve8uPrOl7ZQ44Fljr7uvdfQK4FDipHwVx943ufkN8vp3ww78qlueiuNhFwMm9LpuZHQj8GfC5+NqAPwEu73O5lgN/DJwP4O4T7r6FDBwzQjPixWY2BCwBNtKnY+buPwUeqZpc7xidBHzBg6uBvcxs/16Wzd2/5+5T8eXVwIGpsl3q7uPufgewlvA3PIh0bmpSFs9PGT83gc5PbZVL5yYR6Rh3ReY6ZBVwT+r1hjitr8zsEOAY4BpgP3ffGGfdD+zXhyJ9DHgbkHyLVgBbUj9q/TpuhwIPAZ+PTaw+Z2ZL6fMxc/d7gQ8BdxMukrYC15ONY5aod4yy9jfxWuA78XnWytZNmXyvGTw3QTbPT5k8N4HOTx20UM9NItIhiswNKDPbA/ga8BZ335ae5yH9Z08/STN7AfCgu1/fy/02aQh4MvApdz8G2ElVs6U+HbO9CXdqDwUOAJYyu7lOZvTjGDXDzN5FaOJ3Sb/LItk7N8UyZfX8lMlzE+j81Ak6N4mIBFmozN0LHJR6fWCc1hdmNky4WLrE3b8eJz+QNCOJ/z/Y42I9HXiRmd1JaOr1J4S+IHvFJjrQv+O2Adjg7tfE15cTLqD6fcz+FLjD3R9y90ng64TjmIVjlqh3jDLxN2FmpwMvAE716TFMMlG2HsnUe83ouQmye37K6rkJdH6aF52bRKRjBqCZZRaGJrgWONzMDiWceE8BXtGPgsR+HucDt7r7R1KzrgBOA86J/3+rl+Vy93cw3cn7WcBb3f1UM/sq8GLCBVTPyxXLdr+Z3WNmj3X324ATgN/GR9+OGaH50vFmtgQYi+W6DvgRfT5mKfW+V1cAbzSzS4HjgK2p5k49YWbPIzSbe6a770rNugL4kpl9hBBROBz4ZS/L1kM6NzUhq+enDJ+bQOentuncJCKdsp3N3/2BX76yjVU3dbww8+HufX8AJxKyUq0D3tXHcjyD0JTk18CN8XEiof/HVcDtwA+AffpYxmcB347PDyP8WK0FvgqM9qlMRxMuRH4NfBPYOwvHDPhn4HfAb4CLgdF+HTPgy4S+MZOEiMEZ9Y4RYIQsiuuAmwkZ73pdtrWE/ifJ38GnU8u/K5btNuD5/fjO9fA7pHNTa+XM1Pkpq+emWDadn9orl85Neuihhx6ph7lnqhm8iIiIiIiINCELfeZERERERESkRarMiYiIiIiI5JAqcyIiIiIiIjmkypyIiIiIiEgOqTInIiIiIiKSQ6rMiYiIiIiI5JAqcyIiIiIiIjmkypyIiIiIiEgO/f/xOVFrsOFCUwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "for i in range(10):\n", " plot_predicted_data(generator, collected_routes, -i - 1)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Vergleich der Experimente" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Wie sehr schnell deutlich wird Skalieren die Ergebnisse der Liniendarstellung viel besser als die Ergebnisse der Punktdarstellung. Der Fehler ist aber bei der Punktdarstellung viel geringer. Dies ist auf die wesentlich mehr Werte die mit Routen markierungen versehen worden zurückzuführen. Welches der Beiden ergebnisse sich nachher besser für den Einsatz geeignet ist will ich nicht sagen. Beide Varianten müssten aber vermutlich neu Skaliert werden.\n", "Beide Modelle Zeigen aber sowohl bei Trainings als auch Validierungsdaten einen guten Instinkt für die Richtung der gefolgt werden muss." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Reflection und Ausblick\n", "\n", "Es konnte gezeigt werden das, das Schätzen einer Segelroute gut Funktionieren kann. Es fehlen sicherlich noch viele Faktoren wie lokale Winde, Strömungen und dynamische Hindernisse. Trotzdem bin ich zufrieden mit den Ergebnissen, die in diesem ersten Schritt erzielt werden konnten. Enttäuschend war wie sehr mich meine eigene GPU limitiert hat. Die Ergebnisse werden sicherlich mit mehr Daten etwas Robuster. Die limits an der Rechenleistung haben leider auch behindert, das ich mit Werkzeugen wie dem `KerasTuner` mit dem ich bisher sehr gute erfahrungen gemacht habe das neuronale Netz noch etwas optimiere und evtl. reduziere. Da das Tensorflow beispiel Netz, an dem sich hier Orientiert wurde, für ein 3 Kanaliges Bild mit höherer Auflösung ausgelegt war. Das Netz wurde darauf zwar angepasst aber nicht auf ein minimum reduziert. Auch war bedauerlicherweise keine Zeit um sich mit Datenaugmentierung zu beschäftigen. Sonst wäre sicher möglich gewesen einen Random Flip einzuführen der zufällig Label/Farbkanal 3 und Situation horizontal spiegelt. In Anbetracht dessen das ich aber mehr Daten besessen habe als genutzt wurden, hätte dies das Ergebnis aber nur unwesentlich verfeinert, auch wenn es zufällige Asymmetrien aus dem Netz genommen hätte. Diese konten zwar nicht beobachtet werden. Sind aber natürlich trotzdem möglich. Auch muss noch geprüft werden, ob der Kurs estimator auch auf einer echten Karte valide Ergebnisse liefert.\n", "Entweder durch mehr Daten oder durch ein Nachgelagertes verfeinern der Ergebnisse durch ein weiteres Netz oder den Discriminator des PIX2PIX Papers [3][4].\n", "\n", "Welches der beiden Modelle sich in der Praxis als Überlegen erweisen wird und ob die Netze einen Mehrwert bringen können, muss sich erst noch zeigen und kann an dieser Stelle von mir nicht beantwortet werden. Sicher ist das sie nicht nur prinzipiell, sondern auch mit dieser Implementierung Funktionieren.\n", "\n", "An dieser Stelle muss ich mich bei den anderen Mitgliedern des Sailing Team Darmstadt e.V. bedanken. Ohne die existierende experimentelle Wegfindung des `Pyrate` projekts wäre diese Arbeit nicht möglich gewesen." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Literaturverzeichnis\n", "\n", "[1] Jang, Hoyun and Lee, Inwon and Seo, Hyoungseock: *Effectiveness of CFRP rudder aspect ratio for scale model catamaran racing yacht*, 2017\n", "\n", "[2] Aurélien Géron: *Praxiseinstig Machinen Learning mit Scikit-Learn, Keras und TensorFlow*, 2020, O.Reilly Verlag\n", "\n", "[3] Jun-Yan Zhu: *Image-to-Image Translation with Conditional Adversarial Networks*, 2018, Available: https://arxiv.org/abs/1611.07004\n", "\n", "[4] Tensorflow: *pix2pix: Image-to-image translation with a conditional GAN* Available: https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/pix2pix.ipynb Commit: df4485e052523e0f852e83cea30ad319808bd97b\n", "\n", "[5] Keras: *Keras* Available: https://keras.io/" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%%\n" } }, "source": [ "## Eigenständigkeitserklärung\n", "\n", "![](Eigenstaendigkeit.jpg)" ] } ], "metadata": { "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": "3.10.5" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }