{
 "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",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Inhaltsverzeichnis\n",
    "\n",
    "    1. Einleitung\n",
    "        1.1. Situation\n",
    "        1.2. Vorgehen zur Unterstützenden KI\n",
    "    2. Vorbereitungen\n",
    "    3. Senarien und Routen Generieren\n",
    "    4. Daten betrachten und Filtern\n",
    "    5. KI Modell erstellen\n",
    "    6. Training\n",
    "    7. Analyse der KI\n",
    "    8. Ausblick\n",
    "    "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "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 Wendpunkt 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",
    "<img src=\"https://www.researchgate.net/profile/Hoyun-Jang-2/publication/320062151/figure/fig2/AS:667648312483865@1536191170832/Polar-diagram-of-yacht-speed-according-to-diverse-wind-direction-and-speed.ppm\" alt=\"Ship speeds relativ to the wind\" style=\"width: 400px;\"/>\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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "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.\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.\n",
    "\n",
    "5. `numpy`\n",
    "   Eine Bibliothek um Mathematische operations an multidimensionalen Arrays auszuführen.\n",
    "\n",
    "6. `matplotlib`\n",
    "   Wird genutzt um Diagramme zu plotted.\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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Definiert den Pfad an dem der Jupyter notebook ausgeführt werden soll."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:16.223924Z",
     "start_time": "2022-07-11T18:34:16.212695Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/pyrate\n"
     ]
    }
   ],
   "source": [
    "%cd /pyrate/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Imports\n",
    "Importiert die Imports the necessary packages from python and pypi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.461827Z",
     "start_time": "2022-07-11T18:34:16.227692Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-12 22:52:53.814974: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2022-07-12 22:52:53.818543: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-07-12 22:52:53.818569: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "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",
    "import tensorflow as tf\n",
    "import humanize"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "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": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.574805Z",
     "start_time": "2022-07-11T18:34:19.465509Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "if os.getenv(\"PYRATE\"):\n",
    "    import experiments\n",
    "    from pyrate.plan.nearplanner.timing_frame import TimingFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.587273Z",
     "start_time": "2022-07-11T18:34:19.580531Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Enables a tqdm progress bar for pandas apply\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Einige umgebungsvariablen werden gesetzt, wenn innerhalb des Pyrate Containers ausgeführt."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.609069Z",
     "start_time": "2022-07-11T18:34:19.603573Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Paramter settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.620571Z",
     "start_time": "2022-07-11T18:34:19.613072Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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 on 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] = 1000\n",
    "\n",
    "#\n",
    "NO_SHOW = False\n",
    "GENERATE_NEW = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.633565Z",
     "start_time": "2022-07-11T18:34:19.625445Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# https://stackoverflow.com/questions/16444719/python-numpy-complex-numbers-is-there-a-function-for-polar-to-rectangular-co\n",
    "def polar_to_cartesian(\n",
    "    radii: np.ndarray,\n",
    "    angles: np.ndarray,\n",
    "):\n",
    "    \"\"\"Transforms polar coordinates into cartesian coordinates.\n",
    "\n",
    "    Args:\n",
    "        radii: A array of radii.\n",
    "        angles: A array of angles.\n",
    "\n",
    "    Returns:\n",
    "        An array of cartesian coordinates.\n",
    "    \"\"\"\n",
    "    return radii * np.exp(2j * angles * np.pi)\n",
    "\n",
    "\n",
    "def cartesian_to_polar(\n",
    "    x: np.ndarray,\n",
    "):\n",
    "    \"\"\"Transforms cartesian coordinates into polar coordinates.\n",
    "\n",
    "    Args:\n",
    "        x: A set of complex number to be separated into polar coordinates.\n",
    "\n",
    "    Returns:\n",
    "        An distance array and an angle array.\n",
    "    \"\"\"\n",
    "    return abs(x), np.angle(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.664193Z",
     "start_time": "2022-07-11T18:34:19.639850Z"
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-16.37816427364242 53.572469871489815 6.660645097041407 6.473724419318636\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,113.61866416229827)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.13321290194082813\" opacity=\"0.6\" d=\"M -9.964209735750694,58.12346496725632 L -10.380908764033304,58.90225055101728 L -10.981914756343256,59.53091446131591 L -14.62510766338472,59.79950373165877 L -16.051330505870038,58.08054532765174 L -16.13147371449274,57.81979780226843 L -15.655931768323725,55.1495345171628 L -14.888014501362616,54.37062796397931 L -12.399620374135152,53.8191604306395 L -9.964209735750694,58.12346496725632 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x7fe5d9dd3bb0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def random_polygon(\n",
    "    radius_mean: float = 2,\n",
    "    radius_sigma: float = 1.5,\n",
    "):\n",
    "    \"\"\"Generates the simplest of polygons, a triangle with a size described by a random polygon.\n",
    "\n",
    "    Args:\n",
    "        radius_mean: The average radius defining a circumcircle of a triangle.\n",
    "        radius_sigma: The variance of a radius defining a circumcircle of a triangle.\n",
    "\n",
    "    Returns:\n",
    "        A single triangle.\n",
    "    \"\"\"\n",
    "    number_of_corners = np.random.randint(3, 10)\n",
    "    array = polar_to_cartesian(\n",
    "        np.random.lognormal(radius_mean, radius_sigma),\n",
    "        np.sort(np.random.rand(number_of_corners)),\n",
    "    )\n",
    "    offset = np.random.randint(low=-SIZE_ROUTE, high=SIZE_ROUTE, size=(2,))\n",
    "    return_values = np.zeros((number_of_corners, 2), dtype=float)\n",
    "    # return_values[1, :] = np.real(offset)\n",
    "    return_values[:] = offset\n",
    "    return_values[:, :] += np.array((np.real(array), np.imag(array))).T\n",
    "    return Polygon(return_values)\n",
    "    # return np.array( + offset[0], np.imag(array) + offset[1])\n",
    "\n",
    "\n",
    "np.random.seed(42)\n",
    "random_polygon()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.682424Z",
     "start_time": "2022-07-11T18:34:19.674749Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def generate_obstacles(\n",
    "    seed=None,\n",
    "    number_of_polygons: int = 40,\n",
    "    radius_mean: float = 2,\n",
    "    radius_sigma: float = 1,\n",
    ") -> 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",
    "    if seed is not None:\n",
    "        np.random.seed(seed)\n",
    "    polygons = []\n",
    "    for _ in range(number_of_polygons):\n",
    "        poly = random_polygon(radius_mean, radius_sigma)\n",
    "        if poly.contains(Point(0, 0)):\n",
    "            continue\n",
    "        if poly.exterior.distance(Point(0, 0)) < 1:\n",
    "            continue\n",
    "        polygons.append(poly)\n",
    "    polygon_list = list(unary_union(polygons).geoms)\n",
    "    return {str(i): p for i, p in enumerate(polygon_list)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.712930Z",
     "start_time": "2022-07-11T18:34:19.687742Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (-61 31)\n"
     ]
    }
   ],
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.733293Z",
     "start_time": "2022-07-11T18:34:19.718254Z"
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def plot_situation(\n",
    "    obstacles: dict[str, Polygon],\n",
    "    destination: Point,\n",
    "    obstacle_color: str | None = None,\n",
    "    route: TimingFrame | np.ndarray | None = 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",
    "    # x.figure(figsize=(8, 8))\n",
    "    # plt.axis([70.9481331655341 - 5, 70.9481331655341 + 5,  43.24219045432384-5, 43.24219045432384+5])\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 obsticles.\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",
    "    # 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, TimingFrame):\n",
    "            plt.plot(route.points[:, 0], route.points[:, 1], color=\"BLUE\", marker=\".\")\n",
    "        elif isinstance(route, np.ndarray):\n",
    "            plt.plot(route[:, 0], route[:, 1], color=\"BLUE\", marker=\".\")\n",
    "        else:\n",
    "            raise TypeError()\n",
    "\n",
    "    # Plots the estination\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.749664Z",
     "start_time": "2022-07-11T18:34:19.740070Z"
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if not NO_SHOW:\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",
    "        # noinspection PyBroadException\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_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",
    "            legend=seed == 0,\n",
    "        )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.769250Z",
     "start_time": "2022-07-11T18:34:19.756225Z"
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def generate_image_from_map(\n",
    "    obstacles: dict[str, Polygon],\n",
    "    destination: Point,\n",
    "    route_type: Literal[\"line\", \"dot\"],\n",
    "    route: np.ndarray | TimingFrame | None = None,\n",
    "    seed=None,\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",
    "    \"\"\"\n",
    "    img = Image.new(\n",
    "        \"RGB\",\n",
    "        (IMG_SIZE, IMG_SIZE),\n",
    "        \"#000000\",\n",
    "    )\n",
    "    draw = ImageDraw.Draw(img)\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",
    "    if isinstance(route, TimingFrame):\n",
    "        route = route.points\n",
    "    route = ((route + SIZE_ROUTE) / (2 * SIZE_ROUTE) * IMG_SIZE).astype(int)\n",
    "    if route_type == \"line\":\n",
    "        draw.line([tuple(point) for point in route], fill=(0, 0, 0xFF))\n",
    "    elif route_type == \"dot\":\n",
    "        for point in route[1:]:\n",
    "            try:\n",
    "                img.putpixel(point, (0, 0, 0xFF))\n",
    "            except IndexError:\n",
    "                if seed:\n",
    "                    print(f\"Seed: {seed}, Point: {point}\")\n",
    "                return None\n",
    "        pass\n",
    "    else:\n",
    "        raise ValueError(\"Route type unknown.\")\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.784712Z",
     "start_time": "2022-07-11T18:34:19.774368Z"
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def generate_example_image(route_type: Literal[\"line\", \"dot\"]):\n",
    "    obstacles = generate_obstacles(42)\n",
    "    destination = generate_destination(obstacles, 42)\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 as e:\n",
    "        route = None\n",
    "    return generate_image_from_map(\n",
    "        obstacles=obstacles,\n",
    "        destination=destination,\n",
    "        route=route,\n",
    "        route_type=route_type,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.798844Z",
     "start_time": "2022-07-11T18:34:19.789736Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if not NO_SHOW:\n",
    "    generate_example_image(route_type=\"dot\").resize(\n",
    "        (IMG_SHOW_SIZE, IMG_SHOW_SIZE), Image.Resampling.BICUBIC\n",
    "    ).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.810949Z",
     "start_time": "2022-07-11T18:34:19.804587Z"
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if not NO_SHOW:\n",
    "    generate_example_image(route_type=\"line\").resize(\n",
    "        (IMG_SHOW_SIZE, IMG_SHOW_SIZE), Image.Resampling.BICUBIC\n",
    "    ).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.822370Z",
     "start_time": "2022-07-11T18:34:19.815008Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if not NO_SHOW:\n",
    "    seed = 42\n",
    "    wind_dir = 180\n",
    "    generated_obstacles = generate_obstacles(seed)\n",
    "    generated_destination = generate_destination(generated_obstacles, seed)\n",
    "    route_generated = None\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",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.834989Z",
     "start_time": "2022-07-11T18:34:19.827279Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if not NO_SHOW:\n",
    "    for seed in tqdm([42]):\n",
    "        plt.figure(figsize=(8, 8))\n",
    "        wind_dir = 180\n",
    "        generated_obstacles = generate_obstacles(seed)\n",
    "        generated_destination = generate_destination(generated_obstacles, seed)\n",
    "        route_generated = None\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",
    "        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",
    "            legend=seed == 0,\n",
    "        )\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.849543Z",
     "start_time": "2022-07-11T18:34:19.839918Z"
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "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",
    "    obstacles = generate_obstacles(seed)\n",
    "    destination = generate_destination(obstacles, seed)\n",
    "\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 as e:\n",
    "        print(\"Error\")\n",
    "        print(e)\n",
    "        route = None\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",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:19.864373Z",
     "start_time": "2022-07-11T18:34:19.853992Z"
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if not NO_SHOW:\n",
    "    df = pd.DataFrame(\n",
    "        [generate_all_to_series(i, image=False) for i in tqdm(range(2))]\n",
    "    ).set_index(\"seed\")\n",
    "    df.to_pickle(\"test.pickle\")\n",
    "    df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "https://programtalk.com/python-examples/PIL.ImageDraw.Draw.polygon/)\n",
    "https://stackoverflow.com/questions/3654289/scipy-create-2d-polygon-mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "```python\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",
    "files = glob.glob(\"data/*.pickle\")\n",
    "seed_groups = {int(file[9:-7]) for file in files}\n",
    "for next_seeds in range(start_seed, 10_000_000_000, save_frequency):\n",
    "    if next_seeds in seed_groups:\n",
    "        continue\n",
    "    print(f\"Start generating routes for seed: {next_seeds}\")\n",
    "    tmp_pickle_str: str = f\"data/tmp_{next_seeds:010}.pickle\"\n",
    "    pd.DataFrame().to_pickle(tmp_pickle_str)\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",
    "    pickle_to_file = f\"data/raw_{next_seeds:010}.pickle\"\n",
    "    df.to_pickle(pickle_to_file)\n",
    "    os.remove(tmp_pickle_str)\n",
    "    if not continues:\n",
    "        break\n",
    "```"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:22.957476Z",
     "start_time": "2022-07-11T18:34:19.869252Z"
    },
    "pycharm": {
     "is_executing": true,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a640acf6a79740149c6f477e9889fd75",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     50000 maps collected\n",
      "     43400 routes collected\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obstacles</th>\n",
       "      <th>destination_x</th>\n",
       "      <th>destination_y</th>\n",
       "      <th>image</th>\n",
       "      <th>route</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'0': POLYGON ((-17.62168766659423 -98.3692662...</td>\n",
       "      <td>-66.0</td>\n",
       "      <td>-54.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-6.514627334268863, -5.502693040...</td>\n",
       "      <td>100.151629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>{'0': POLYGON ((-97.82715137072381 -82.2211677...</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-38.0, 65.0]]</td>\n",
       "      <td>75292.761936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'0': POLYGON ((-46.23706006792075 -76.7569948...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [43.20648551245758, 31.2114102262...</td>\n",
       "      <td>18967.522925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>{'0': POLYGON ((-7.4210414351932155 -83.111096...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [5.303962239032221, 10.6856391688...</td>\n",
       "      <td>63200.630758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'0': POLYGON ((-77.97638439917915 -70.2390972...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [4.691900284503645, -5.4114328014...</td>\n",
       "      <td>28914.654143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50045</th>\n",
       "      <td>{'0': POLYGON ((-86.63193290264695 -93.5319244...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-9.17985022292322, 0.74185570341...</td>\n",
       "      <td>695.38234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50046</th>\n",
       "      <td>{'0': POLYGON ((2.518895755683328 -96.87282498...</td>\n",
       "      <td>-71.0</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-54.61671323674942, -33.84002165...</td>\n",
       "      <td>67.928607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50047</th>\n",
       "      <td>{'0': POLYGON ((-4.460598846031621 -99.2649725...</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-47.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-36.0, -47.0]]</td>\n",
       "      <td>36.544878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50048</th>\n",
       "      <td>{'0': POLYGON ((-90.6998307775452 -75.58510795...</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-48.0, -42.0]]</td>\n",
       "      <td>37.990761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50049</th>\n",
       "      <td>{'0': POLYGON ((-73.30908588454162 -74.1477834...</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-8.34785332097252, 2.56320973960...</td>\n",
       "      <td>34269.035908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43400 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               obstacles  destination_x  \\\n",
       "seed                                                                      \n",
       "0      {'0': POLYGON ((-17.62168766659423 -98.3692662...          -66.0   \n",
       "1      {'0': POLYGON ((-97.82715137072381 -82.2211677...          -38.0   \n",
       "2      {'0': POLYGON ((-46.23706006792075 -76.7569948...           73.0   \n",
       "3      {'0': POLYGON ((-7.4210414351932155 -83.111096...           31.0   \n",
       "4      {'0': POLYGON ((-77.97638439917915 -70.2390972...           47.0   \n",
       "...                                                  ...            ...   \n",
       "50045  {'0': POLYGON ((-86.63193290264695 -93.5319244...           69.0   \n",
       "50046  {'0': POLYGON ((2.518895755683328 -96.87282498...          -71.0   \n",
       "50047  {'0': POLYGON ((-4.460598846031621 -99.2649725...          -36.0   \n",
       "50048  {'0': POLYGON ((-90.6998307775452 -75.58510795...          -48.0   \n",
       "50049  {'0': POLYGON ((-73.30908588454162 -74.1477834...          -48.0   \n",
       "\n",
       "       destination_y image                                              route  \\\n",
       "seed                                                                            \n",
       "0              -54.0  <NA>  [[0.0, 0.0], [-6.514627334268863, -5.502693040...   \n",
       "1               65.0  <NA>                        [[0.0, 0.0], [-38.0, 65.0]]   \n",
       "2               49.0  <NA>  [[0.0, 0.0], [43.20648551245758, 31.2114102262...   \n",
       "3               56.0  <NA>  [[0.0, 0.0], [5.303962239032221, 10.6856391688...   \n",
       "4               54.0  <NA>  [[0.0, 0.0], [4.691900284503645, -5.4114328014...   \n",
       "...              ...   ...                                                ...   \n",
       "50045          -61.0  <NA>  [[0.0, 0.0], [-9.17985022292322, 0.74185570341...   \n",
       "50046          -58.0  <NA>  [[0.0, 0.0], [-54.61671323674942, -33.84002165...   \n",
       "50047          -47.0  <NA>                       [[0.0, 0.0], [-36.0, -47.0]]   \n",
       "50048          -42.0  <NA>                       [[0.0, 0.0], [-48.0, -42.0]]   \n",
       "50049           72.0  <NA>  [[0.0, 0.0], [-8.34785332097252, 2.56320973960...   \n",
       "\n",
       "               cost  \n",
       "seed                 \n",
       "0        100.151629  \n",
       "1      75292.761936  \n",
       "2      18967.522925  \n",
       "3      63200.630758  \n",
       "4      28914.654143  \n",
       "...             ...  \n",
       "50045     695.38234  \n",
       "50046     67.928607  \n",
       "50047     36.544878  \n",
       "50048     37.990761  \n",
       "50049  34269.035908  \n",
       "\n",
       "[43400 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA_COLECTION_PATH: Final[str] = \"data/collected.pickle\"\n",
    "if os.path.exists(DATA_COLECTION_PATH) and not GENERATE_NEW:\n",
    "    collected_data = pd.read_pickle(DATA_COLECTION_PATH)\n",
    "else:\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",
    "    number_of_maps = len(collected_data.index)\n",
    "    print(f\"{number_of_maps: 10} maps collected\")\n",
    "    collected_data.dropna(subset=[\"route\"], inplace=True)\n",
    "    number_of_routes = len(collected_data.index)\n",
    "    print(f\"{number_of_routes: 10} routes collected\")\n",
    "    collected_data.to_pickle(DATA_COLECTION_PATH)\n",
    "collected_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# find and drop all routes that exit the map!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:23.421419Z",
     "start_time": "2022-07-11T18:34:22.961997Z"
    },
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3f9920fc8f3e48858390d61beb5025f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/43 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43400 - 188 = 43212 sets of data remaining.\n"
     ]
    }
   ],
   "source": [
    "def check_route_in_bounds(route):\n",
    "\n",
    "    # easier to debut in multiple lines\n",
    "    if route is None:\n",
    "        return False\n",
    "    if route is pd.NA:\n",
    "        return False\n",
    "    if not isinstance(route, np.ndarray):\n",
    "        return False\n",
    "    if np.array(\n",
    "        abs(route) > 100,\n",
    "    ).any():\n",
    "        return False\n",
    "    return True\n",
    "\n",
    "\n",
    "data_before = len(collected_data.index)\n",
    "\n",
    "df_filter = collected_data[\"route\"].mapply(check_route_in_bounds)\n",
    "filtered = collected_data[~df_filter]\n",
    "collected_data = collected_data[df_filter]\n",
    "\n",
    "data_after = len(collected_data.index)\n",
    "\n",
    "print(\n",
    "    f\"{data_before} - {data_before-data_after} = {data_after} sets of data remaining.\"\n",
    ")\n",
    "del data_before, data_after, filtered, df_filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# find and drop all routes with errors!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:23.847712Z",
     "start_time": "2022-07-11T18:34:23.424364Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d2f493b8c75c46cebad8e4e5025fe47c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/43 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43212 - 2759 = 40453 sets of data remaining.\n"
     ]
    }
   ],
   "source": [
    "def check_route_self_crossing(route):\n",
    "    if isinstance(route, float):\n",
    "        print(float)\n",
    "    return not LineString(route).is_simple\n",
    "\n",
    "\n",
    "data_before = len(collected_data.index)\n",
    "collected_data = collected_data[\n",
    "    ~collected_data[\"route\"].mapply(check_route_self_crossing)\n",
    "]\n",
    "data_after = len(collected_data.index)\n",
    "print(\n",
    "    f\"{data_before} - {data_before-data_after} = {data_after} sets of data remaining.\"\n",
    ")\n",
    "del data_before, data_after"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# distribution over costs and points in rutes routes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:23.863906Z",
     "start_time": "2022-07-11T18:34:23.852977Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022 entries over the 0.95 quantile at 39839.307\n"
     ]
    }
   ],
   "source": [
    "QUNATILE_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",
    "        QUNATILE_LIMIT\n",
    "    )\n",
    "    OVER_QUANTILE: Final[int] = int(len(collected_data.index) * (1 - QUNATILE_LIMIT))\n",
    "print(\n",
    "    f\"{OVER_QUANTILE} entries over the {QUNATILE_LIMIT} quantile at {DATA_UPPER_LIMIT_QUANTIL:.3f}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:24.022327Z",
     "start_time": "2022-07-11T18:34:23.867460Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAD4CAYAAAAtrdtxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAebUlEQVR4nO3de5QV5Znv8e+Pm3jBcOshHNABlRiRQKMdMTpGR08QiSeYYKJmouBwZI7xEmNiRMxSB3FFEw1KNF6IiHgweIm3Y8gQvM3ERIUmdhREhhaJNKKQRiMZvPucP/bb7abtht1F7727h99nrVpd9dRbVU/Vpvuh6q1dpYjAzMwsi07lTsDMzDouFxEzM8vMRcTMzDJzETEzs8xcRMzMLLMu5U6g1Pr27RuDBg0qdxq2PStX5n7uv3958zAzAJYuXfqXiKhoGt/pisigQYOorq4udxq2PUcdlfv5xBPlzMLMEkl/bi7uy1lmZpaZi4iZmWXmImJmZpntdH0iZtZ+vf/++9TV1fHOO++UO5WdVvfu3Rk4cCBdu3YtqL2LiJm1G3V1dfTo0YNBgwYhqdzp7HQigvr6eurq6hg8eHBBy/hylpm1G++88w59+vRxASkTSfTp06dVZ4IuImbWrriAlFdrj7+LiJmZZeY+ETNrtwZN+XWbrm/NlV9u0/U1VVNTw6uvvsrYsWOLup32xEWkFdr6H3Shiv0P38zaRk1NDdXV1TtVEfHlLDOzPHPnzmX48OGMGDGCU089lTVr1nD00UczfPhwjjnmGF555RUA7rnnHoYNG8aIESP44he/yHvvvccll1zCXXfdRWVlJXfddVeZ96Q0fCZiZpYsX76c6dOn84c//IG+ffuyadMmJkyY0DjMnj2bc889lwceeIBp06axcOFCBgwYwJtvvkm3bt2YNm0a1dXVXH/99eXelZLxmYiZWfLYY4/x9a9/nb59+wLQu3dvnnrqKb75zW8CcOqpp/Lkk08CcPjhhzNx4kRmzZrFhx9+WLacy81FxMwsg5tuuonp06ezdu1aDj74YOrr68udUlm4iJiZJUcffTT33HNPY0HYtGkThx12GPPnzwdg3rx5HHHEEQC89NJLjBo1imnTplFRUcHatWvp0aMHmzdvLlv+5eA+ETNrt0p9Z+KBBx7IxRdfzJFHHknnzp0ZOXIkP/vZzzj99NP5yU9+QkVFBbfddhsAF1xwAatWrSIiOOaYYxgxYgR77703V155JZWVlVx00UWcdNJJJc2/HFxEzMzyNHSi53vsscc+0e6+++77RKx3794sWbKkaLm1R0W7nCWpu6TFkv4kabmkf03xwZKekVQr6S5J3VJ8lzRdm+YPylvXRSm+UtKxefExKVYraUqx9sXMzJpXzD6Rd4GjI2IEUAmMkXQocBUwIyL2A94AJqX2k4A3UnxGaoekocDJwIHAGODnkjpL6gzcABwHDAVOSW3NzKxEilZEIudvabJrGgI4Grg3xW8HTkjj49I0af4xyj0JbBwwPyLejYiXgVrgkDTURsTqiHgPmJ/amplZiRT17qx0xlADbAAWAS8Bb0bEB6lJHTAgjQ8A1gKk+X8F+uTHmyzTUry5PCZLqpZUvXHjxjbYMzMzgyIXkYj4MCIqgYHkzhw+W8ztbSOPWyKiKiKqKioqypGCmdl/SyX5nkhEvAk8DnwB6Cmp4a6wgcC6NL4O2Asgzf8UUJ8fb7JMS3EzMyuRYt6dVSGpZxrfFfgSsIJcMTkxNZsAPJjGH0rTpPmPRUSk+Mnp7q3BwBBgMbAEGJLu9upGrvP9oWLtj5ntHK677jqGDRvGgQceyLXXXtsYv+yyyxgwYACVlZVUVlayYMECAH7/+98zfPhwqqqqWLVqFQBvvvkmo0eP5qOPPmp2G++99x7nnXce++23H/vttx/HH39844Md29KaNWu48847G6erq6s599xzAZgzZw5nn332Dm+jmGci/YHHJT1H7g/+ooh4GLgQOF9SLbk+j1tT+1uBPil+PjAFICKWA3cDLwD/BpyVLpN9AJwNLCRXnO5Obc3MMlm2bBmzZs1i8eLF/OlPf+Lhhx+mtra2cf53v/tdampqqKmpaXzc+zXXXMOCBQu49tpruemmmwCYPn06U6dOpVOn5v/ETp06lc2bN7Ny5Upqa2sZP34848aNa7HoZNW0iFRVVTFz5sw23UYx7856LiJGRsTwiBgWEdNSfHVEHBIR+0XE1yPi3RR/J03vl+avzlvXFRGxb0TsHxG/yYsviIjPpHlXFGtfzGznsGLFCkaNGsVuu+1Gly5dOPLII5v9UmG+rl27smXLFrZs2ULXrl156aWXWLt2LUcddVSz7bds2cJtt93GjBkz6Ny5MwCnn346e+yxB4888ghr1qxh2LBhje2vvvpqLrvsMgBmzZrF5z//eUaMGMH48ePZsmULABMnTuTcc8/lsMMOY5999uHee3M3wE6ZMoXf/e53VFZWMmPGDJ544gmOP/74HTxKW/M31s2sfTrvPKipadt1VlZC3iWqpoYNG8bFF19MfX09u+66KwsWLKCqqqpx/vXXX8/cuXOpqqrimmuuoVevXlx00UWcdtpp7Lrrrtxxxx18//vfZ/r06S1uo7a2lr333ps999xzq3hVVRUvvPACn/nMZ1pc9mtf+xpnnHEGAD/84Q+59dZbOeeccwBYv349Tz75JC+++CJf+cpXOPHEE7nyyiu5+uqrefjhhwF44okntnOAWs8PYDQzSw444AAuvPBCRo8ezZgxY6isrGw8WzjzzDN56aWXqKmpoX///nzve98DoLKykqeffprHH3+c1atX079/fyKCk046iW9961u8/vrrbZbfsmXLOOKII/jc5z7HvHnzWL784yv4J5xwAp06dWLo0KFtus3t8ZmImbVP2zhjKKZJkyYxaVLuQRpTp05l4MCBAPTr16+xzRlnnPGJy0IRwfTp05k/fz7nnHMOP/7xj1mzZg0zZ87kiis+vtq+77778sorr7B582Z69OjRGF+6dCnjx4+nS5cuW/WNvPPOO43jEydO5IEHHmDEiBHMmTNnqzOLXXbZZatcSsVnImZmeTZs2ADAK6+8wn333df4Qqr169c3trn//vu36reA3Gt1x44dS+/evdmyZQudOnWiU6dOjf0WDXbffXcmTJjA+eef3/gyq7lz59K9e3cOP/xw+vXrx4YNG6ivr+fdd99tvBQFsHnzZvr378/777/PvHnztrsvpXg0vc9EzMzyjB8/nvr6erp27coNN9xAz549AfjBD35ATU0Nkhg0aBA333xz4zJbtmxhzpw5/Pa3vwXg/PPPZ+zYsXTr1m2ru6Ma/OhHP+KCCy5g//335+2336aiooKnnnoKSXTt2pVLLrmEQw45hAEDBvDZz378He3LL7+cUaNGUVFRwahRo7ZbIIYPH07nzp0ZMWIEEydOZOTIkW1whLamUp72tAdVVVVRXV2dadlBU37dxtkUptTvVGgXGu5sKUJHoLVfK1as4IADDih3GiX12muvcdxxx3HmmWcyefLkcqcDNP85SFoaEVVN2/pMxMysjD796U/z7LPPljuNzNwnYmZmmbmImFm7srNdYm9vWnv8XUTMrN3o3r079fX1LiRlEhHU19fTvXv3gpdxn4iZtRsDBw6krq4Ov/enfLp379743ZhCuIiYWbvRtWtXBg8eXO40rBV8OcvMzDJzETEzs8xcRMzMLDMXETMzy8xFxMzMMnMRMTOzzFxEzMwsMxcRMzPLzEXEzMwycxExM7PMXETMzCyzohURSXtJelzSC5KWS/pOil8maZ2kmjSMzVvmIkm1klZKOjYvPibFaiVNyYsPlvRMit8lqVux9sfMzD6pmGciHwDfi4ihwKHAWZKGpnkzIqIyDQsA0ryTgQOBMcDPJXWW1Bm4ATgOGAqckreeq9K69gPeACYVcX/MzKyJohWRiFgfEX9M45uBFcCAbSwyDpgfEe9GxMtALXBIGmojYnVEvAfMB8ZJEnA0cG9a/nbghKLsjJmZNaskfSKSBgEjgWdS6GxJz0maLalXig0A1uYtVpdiLcX7AG9GxAdN4mZmViJFLyKS9gB+BZwXEW8BNwL7ApXAeuCaEuQwWVK1pGq/7MbMrO0UtYhI6kqugMyLiPsAIuL1iPgwIj4CZpG7XAWwDtgrb/GBKdZSvB7oKalLk/gnRMQtEVEVEVUVFRVts3NmZlbUu7ME3AqsiIif5sX75zX7KrAsjT8EnCxpF0mDgSHAYmAJMCTdidWNXOf7Q5F7CfPjwIlp+QnAg8XaHzMz+6Rivh73cOBU4HlJNSk2ldzdVZVAAGuAfwGIiOWS7gZeIHdn11kR8SGApLOBhUBnYHZELE/ruxCYL2k68Cy5omVmZiVStCISEU8CambWgm0scwVwRTPxBc0tFxGr+fhymJmZlZi/sW5mZpm5iJiZWWYuImZmlpmLiJmZZeYiYmZmmbmImJlZZi4iZmaWmYuImZll5iJiZmaZuYiYmVlmLiJmZpaZi4iZmWXmImJmZpm5iJiZWWYuImZmlpmLiJmZZeYiYmZmmbmImJlZZi4iZmaWmYuImZll5iJiZmaZuYiYmVlmLiJmZpaZi4iZmWVWtCIiaS9Jj0t6QdJySd9J8d6SFklalX72SnFJmimpVtJzkg7KW9eE1H6VpAl58YMlPZ+WmSlJxdofMzP7pGKeiXwAfC8ihgKHAmdJGgpMAR6NiCHAo2ka4DhgSBomAzdCrugAlwKjgEOASxsKT2pzRt5yY4q4P2Zm1kRBRUTS51q74ohYHxF/TOObgRXAAGAccHtqdjtwQhofB8yNnKeBnpL6A8cCiyJiU0S8ASwCxqR5e0bE0xERwNy8dZmZWQkUeibyc0mLJX1b0qdauxFJg4CRwDNAv4hYn2a9BvRL4wOAtXmL1aXYtuJ1zcSb2/5kSdWSqjdu3Nja9M3MrAUFFZGIOAL4J2AvYKmkOyV9qZBlJe0B/Ao4LyLearLeAKJ1KbdeRNwSEVURUVVRUVHszZmZ7TQK7hOJiFXAD4ELgSOBmZJelPS1lpaR1JVcAZkXEfel8OvpUhTp54YUX0euSDUYmGLbig9sJm5mZiVSaJ/IcEkzyPVrHA38r4g4II3PaGEZAbcCKyLip3mzHgIa7rCaADyYFz8t3aV1KPDXdNlrITBaUq/UoT4aWJjmvSXp0LSt0/LWZWZmJdClwHY/A34BTI2ItxuCEfGqpB+2sMzhwKnA85JqUmwqcCVwt6RJwJ+Bb6R5C4CxQC2wBTg9bWOTpMuBJandtIjYlMa/DcwBdgV+kwYzMyuRQovIl4G3I+JDAEmdgO4RsSUi7mhugYh4EmjpexvHNNM+gLNaWNdsYHYz8WpgWEF7YGZmba7QPpFHyP1vv8FuKWZmZjuxQotI94j4W8NEGt+tOCmZmVlHUWgR+a8mjyE5GHh7G+3NzGwnUGifyHnAPZJeJdfP8WngpGIlZWZmHUNBRSQilkj6LLB/Cq2MiPeLl5aZmXUEhZ6JAHweGJSWOUgSETG3KFmZmVmHUFARkXQHsC9QA3yYwg0PPTQzs51UoWciVcDQ9F0OMzMzoPC7s5aR60w3MzNrVOiZSF/gBUmLgXcbghHxlaJkZWZmHUKhReSyYiZhZmYdU6G3+P67pL8HhkTEI5J2AzoXNzUzM2vvCn0U/BnAvcDNKTQAeKBIOZmZWQdRaMf6WeQe7f4WNL6g6u+KlZSZmXUMhRaRdyPivYYJSV0owWttzcysfSu0iPy7pKnArund6vcA/694aZmZWUdQaBGZAmwEngf+hdxbCFt6o6GZme0kCr076yNgVhrMzMyAwp+d9TLN9IFExD5tnpGZmXUYrXl2VoPuwNeB3m2fjpmZdSQF9YlERH3esC4irgW+XNzUzMysvSv0ctZBeZOdyJ2ZtOZdJGZm9t9QoYXgmrzxD4A1wDfaPBszM+tQCr076x+LnYiZmXU8hV7OOn9b8yPip80sMxs4HtgQEcNS7DLgDHLfOQGYGhEL0ryLgEnk3px4bkQsTPExwHXkHvj4i4i4MsUHA/OBPsBS4NT8b9WbmVnxFfplwyrgTHIPXhwA/B/gIKBHGpozBxjTTHxGRFSmoaGADAVOBg5My/xcUmdJnYEbgOOAocApqS3AVWld+wFvkCtAZmZWQoX2iQwEDoqIzdB4RvHriPhWSwtExH9IGlTg+scB8yPiXeBlSbXAIWlebUSsTtudD4yTtAI4GvhmanM7uXee3Fjg9szMrA0UeibSD8i/VPReimVxtqTnJM2W1CvFBgBr89rU8fFZT3PxPsCbEfFBk3izJE2WVC2peuPGjS01MzOzViq0iMwFFku6LJ2FPEPuf/+tdSOwL1AJrGfru76KJiJuiYiqiKiqqKgoxSbNzHYKhd6ddYWk3wBHpNDpEfFsazcWEa83jEuaBTycJtcBe+U1HZhitBCvB3pK6pLORvLbm5lZiRR6JgKwG/BWRFwH1KW7o1pFUv+8ya8Cy9L4Q8DJknZJ6x0CLAaWAEMkDZbUjVzn+0MREcDjwIlp+QnAg63Nx8zMdkyht/heSu4Orf2B24CuwP8l97bDlpb5JXAU0FdSHXApcJSkSnIPc1xD7rHyRMRySXcDL5D7MuNZEfFhWs/ZwEJyt/jOjojlaRMXAvMlTQeeBW4tdKfNzKxtFHp31leBkcAfASLiVUkt3dpLanNKM+EW/9BHxBXAFc3EF5B7f0nT+Go+voPLzMzKoNDLWe+lS0gBIGn34qVkZmYdRaFF5G5JN5PrzD4DeAS/oMrMbKe33ctZkgTcBXwWeItcv8glEbGoyLmZmVk7t90iEhEhaUFEfA5w4TAzs0aFXs76o6TPFzUTMzPrcAq9O2sU8C1Ja4D/AkTuJGV4sRIzM7P2b5tFRNLeEfEKcGyJ8jEzsw5ke2ciD5B7eu+fJf0qIsaXICczM+sgttcnorzxfYqZiJmZdTzbKyLRwriZmdl2L2eNkPQWuTOSXdM4fNyxvmdRszMzs3Ztm0UkIjqXKhEzM+t4WvMoeDMzs624iJiZWWYuImZmlpmLiJmZZeYiYmZmmbmImJlZZi4iZmaWmYuImZll5iJiZmaZuYiYmVlmLiJmZpZZ0YqIpNmSNkhalhfrLWmRpFXpZ68Ul6SZkmolPSfpoLxlJqT2qyRNyIsfLOn5tMxMScLMzEqqmGcic4AxTWJTgEcjYgjwaJoGOA4YkobJwI2QKzrApeRez3sIcGlD4Ultzshbrum2zMysyIpWRCLiP4BNTcLjgNvT+O3ACXnxuZHzNNBTUn9yr+VdFBGbIuINYBEwJs3bMyKejogA5uaty8zMSqTUfSL9ImJ9Gn8N6JfGBwBr89rVpdi24nXNxJslabKkaknVGzdu3LE9MDOzRmXrWE9nECV5W2JE3BIRVRFRVVFRUYpNmpntFEpdRF5Pl6JIPzek+Dpgr7x2A1NsW/GBzcTNzKyESl1EHgIa7rCaADyYFz8t3aV1KPDXdNlrITBaUq/UoT4aWJjmvSXp0HRX1ml56zIzsxLZ3jvWM5P0S+AooK+kOnJ3WV0J3C1pEvBn4Bup+QJgLFALbAFOB4iITZIuB5akdtMioqGz/tvk7gDbFfhNGszMrISKVkQi4pQWZh3TTNsAzmphPbOB2c3Eq4FhO5KjmZntGH9j3czMMnMRMTOzzFxEzMwsMxcRMzPLzEXEzMwycxExM7PMXETMzCwzFxEzM8vMRcTMzDJzETEzs8xcRMzMLDMXETMzy8xFxMzMMnMRMTOzzFxEzMwsMxcRMzPLzEXEzMwycxExM7PMXETMzCwzFxEzM8vMRcTMzDJzETEzs8xcRMzMLDMXETMzy6wsRUTSGknPS6qRVJ1ivSUtkrQq/eyV4pI0U1KtpOckHZS3ngmp/SpJE8qxL2ZmO7Nynon8Y0RURkRVmp4CPBoRQ4BH0zTAccCQNEwGboRc0QEuBUYBhwCXNhQeMzMrjfZ0OWsccHsavx04IS8+N3KeBnpK6g8cCyyKiE0R8QawCBhT4pzNzHZq5SoiAfxW0lJJk1OsX0SsT+OvAf3S+ABgbd6ydSnWUvwTJE2WVC2peuPGjW21D2ZmO70uZdruP0TEOkl/ByyS9GL+zIgISdFWG4uIW4BbAKqqqtpsvWZmO7uynIlExLr0cwNwP7k+jdfTZSrSzw2p+Tpgr7zFB6ZYS3EzMyuRkhcRSbtL6tEwDowGlgEPAQ13WE0AHkzjDwGnpbu0DgX+mi57LQRGS+qVOtRHp5iZmZVIOS5n9QPul9Sw/Tsj4t8kLQHuljQJ+DPwjdR+ATAWqAW2AKcDRMQmSZcDS1K7aRGxqXS7YWZmJS8iEbEaGNFMvB44ppl4AGe1sK7ZwOy2ztHMzArTnm7xNTOzDqZcd2dZKwya8uuybXvNlV8u27bNrP3zmYiZmWXmImJmZpm5iJiZWWYuImZmlpmLiJmZZeYiYmZmmbmImJlZZv6eiG1Tub6jsqYsWzWz1vKZiJmZZeYiYmZmmbmImJlZZi4iZmaWmYuImZll5iJiZmaZuYiYmVlmLiJmZpaZi4iZmWXmb6xbu/T06noATi7DN+b9NkezwvlMxMzMMnMRMTOzzFxEzMwsM/eJmDVRticXuy/GOqAOfyYiaYyklZJqJU0pdz5mZjuTDn0mIqkzcAPwJaAOWCLpoYh4obyZmbVeuc6AwGdBll2HLiLAIUBtRKwGkDQfGAe4iJi1gi/hWVYdvYgMANbmTdcBo5o2kjQZmJwm/yZpZcbt9QX+knHZcupweX8h96MvVx3fofJOOtzxTkqet65qs1X5mBff3zcX7OhFpCARcQtwy46uR1J1RFS1QUol5bxLy3mXXkfNvaPmna+jd6yvA/bKmx6YYmZmVgIdvYgsAYZIGiypG3Ay8FCZczIz22l06MtZEfGBpLOBhUBnYHZELC/iJnf4kliZOO/Sct6l11Fz76h5N1JElDsHMzProDr65SwzMysjFxEzM8vMRaQA7eHRKpL2kvS4pBckLZf0nRTvLWmRpFXpZ68Ul6SZKefnJB2Ut64Jqf0qSRPy4gdLej4tM1OS2jD/zpKelfRwmh4s6Zm0rbvSjRFI2iVN16b5g/LWcVGKr5R0bF68KJ+PpJ6S7pX0oqQVkr7QEY63pO+mfyPLJP1SUvf2erwlzZa0QdKyvFjRj3FL29jBvH+S/q08J+l+ST3z5rXqWGb5vMomIjxsYyDXYf8SsA/QDfgTMLQMefQHDkrjPYD/BIYCPwampPgU4Ko0Phb4DSDgUOCZFO8NrE4/e6XxXmne4tRWadnj2jD/84E7gYfT9N3AyWn8JuDMNP5t4KY0fjJwVxofmo79LsDg9Jl0LubnA9wO/O803g3o2d6PN7kv4L4M7Jp3nCe21+MNfBE4CFiWFyv6MW5pGzuY92igSxq/Ki/vVh/L1n5e5RzKuvGOMJD78vTCvOmLgIvaQV4Pkntm2Eqgf4r1B1am8ZuBU/Lar0zzTwFuzovfnGL9gRfz4lu128FcBwKPAkcDD6df6L/k/cI1HmNyd9p9IY13Se3U9Lg3tCvW5wN8itwfYzWJt+vjzcdPceidjt/DwLHt+XgDg9j6j3HRj3FL29iRvJvM+yowr7ljtL1jmeX3oy1+T7MOvpy1fc09WmVAmXIBIJ3CjgSeAfpFxPo06zWgXxpvKe9txeuaibeFa4EfAB+l6T7AmxHxQTPbaswvzf9rat/a/dlRg4GNwG3KXYb7haTdaefHOyLWAVcDrwDryR2/pbT/452vFMe4pW20lX8md+bDdvJrLp7l96NsXEQ6GEl7AL8CzouIt/LnRe6/J+3qnm1JxwMbImJpuXNppS7kLlfcGBEjgf8id9mjUTs93r3IPYR0MPA/gN2BMWVNageU4hi39TYkXQx8AMxrq3W2Zy4i29duHq0iqSu5AjIvIu5L4dcl9U/z+wMbUrylvLcVH9hMfEcdDnxF0hpgPrlLWtcBPSU1fNk1f1uN+aX5nwLqM+zPjqoD6iLimTR9L7mi0t6P9/8EXo6IjRHxPnAfuc+gvR/vfKU4xi1tY4dImggcD/xTKk5Z8q6n9Z9X+ZTzWlpHGMj9j3Q1uf/ZNXR+HViGPATMBa5tEv8JW3cQ/jiNf5mtOyEXp3hvctf6e6XhZaB3mte0E3JsG+/DUXzcsX4PW3ccfjuNn8XWHYd3p/ED2bpzcjW5jsmifT7A74D90/hl6Vi36+NN7inWy4Hd0npvB85pz8ebT/aJFP0Yt7SNHcx7DLnXUFQ0adfqY9naz6ucQ1k33lEGcneF/Ce5OykuLlMO/0DulPs5oCYNY8ldD30UWAU8kvfLI3Iv7HoJeB6oylvXPwO1aTg9L14FLEvLXE8bd9ixdRHZJ/2C16ZfmF1SvHuark3z98lb/uKU20ry7mQq1ucDVALV6Zg/kP5AtfvjDfwr8GJa9x3pj1e7PN7AL8n13bxP7uxvUimOcUvb2MG8a8n1V9Sk4aasxzLL51WuwY89MTOzzNwnYmZmmbmImJlZZi4iZmaWmYuImZll5iJiZmaZuYiYmVlmLiJmZpbZ/wdTqC57EgJUXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "collected_data[\"cost\"].plot.hist(bins=10, log=False)  # find a drop limit\n",
    "plt.axvline(x=DATA_UPPER_LIMIT_QUANTIL, color=\"RED\", label=\"95% Quantil\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:26.440028Z",
     "start_time": "2022-07-11T18:34:24.025987Z"
    },
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d004186c4993453f82a23d5461b335ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/12 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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.generated_obstacles,\n",
    "        obstacle_color=\"RED\",\n",
    "        route=row.route_generated,\n",
    "        title=f\"Cost: {row.cost}\",\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:26.499749Z",
     "start_time": "2022-07-11T18:34:26.450483Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obstacles</th>\n",
       "      <th>destination_x</th>\n",
       "      <th>destination_y</th>\n",
       "      <th>image</th>\n",
       "      <th>route</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'0': POLYGON ((-17.62168766659423 -98.3692662...</td>\n",
       "      <td>-66.0</td>\n",
       "      <td>-54.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-6.514627334268863, -5.502693040...</td>\n",
       "      <td>100.151629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'0': POLYGON ((-46.23706006792075 -76.7569948...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [43.20648551245758, 31.2114102262...</td>\n",
       "      <td>18967.522925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>{'0': POLYGON ((-77.97638439917915 -70.2390972...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [4.691900284503645, -5.4114328014...</td>\n",
       "      <td>28914.654143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'0': POLYGON ((-71.45682729091783 -138.627922...</td>\n",
       "      <td>-67.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-42.539218405821984, 15.14880405...</td>\n",
       "      <td>186.095369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>{'0': POLYGON ((-76.20025009472265 -92.9434076...</td>\n",
       "      <td>-67.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-7.80975254664349, 3.41866699781...</td>\n",
       "      <td>23898.229531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50045</th>\n",
       "      <td>{'0': POLYGON ((-86.63193290264695 -93.5319244...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-9.17985022292322, 0.74185570341...</td>\n",
       "      <td>695.38234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50046</th>\n",
       "      <td>{'0': POLYGON ((2.518895755683328 -96.87282498...</td>\n",
       "      <td>-71.0</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-54.61671323674942, -33.84002165...</td>\n",
       "      <td>67.928607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50047</th>\n",
       "      <td>{'0': POLYGON ((-4.460598846031621 -99.2649725...</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>-47.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-36.0, -47.0]]</td>\n",
       "      <td>36.544878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50048</th>\n",
       "      <td>{'0': POLYGON ((-90.6998307775452 -75.58510795...</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-48.0, -42.0]]</td>\n",
       "      <td>37.990761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50049</th>\n",
       "      <td>{'0': POLYGON ((-73.30908588454162 -74.1477834...</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-8.34785332097252, 2.56320973960...</td>\n",
       "      <td>34269.035908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38430 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               obstacles  destination_x  \\\n",
       "seed                                                                      \n",
       "0      {'0': POLYGON ((-17.62168766659423 -98.3692662...          -66.0   \n",
       "2      {'0': POLYGON ((-46.23706006792075 -76.7569948...           73.0   \n",
       "4      {'0': POLYGON ((-77.97638439917915 -70.2390972...           47.0   \n",
       "5      {'0': POLYGON ((-71.45682729091783 -138.627922...          -67.0   \n",
       "6      {'0': POLYGON ((-76.20025009472265 -92.9434076...          -67.0   \n",
       "...                                                  ...            ...   \n",
       "50045  {'0': POLYGON ((-86.63193290264695 -93.5319244...           69.0   \n",
       "50046  {'0': POLYGON ((2.518895755683328 -96.87282498...          -71.0   \n",
       "50047  {'0': POLYGON ((-4.460598846031621 -99.2649725...          -36.0   \n",
       "50048  {'0': POLYGON ((-90.6998307775452 -75.58510795...          -48.0   \n",
       "50049  {'0': POLYGON ((-73.30908588454162 -74.1477834...          -48.0   \n",
       "\n",
       "       destination_y image                                              route  \\\n",
       "seed                                                                            \n",
       "0              -54.0  <NA>  [[0.0, 0.0], [-6.514627334268863, -5.502693040...   \n",
       "2               49.0  <NA>  [[0.0, 0.0], [43.20648551245758, 31.2114102262...   \n",
       "4               54.0  <NA>  [[0.0, 0.0], [4.691900284503645, -5.4114328014...   \n",
       "5               37.0  <NA>  [[0.0, 0.0], [-42.539218405821984, 15.14880405...   \n",
       "6               55.0  <NA>  [[0.0, 0.0], [-7.80975254664349, 3.41866699781...   \n",
       "...              ...   ...                                                ...   \n",
       "50045          -61.0  <NA>  [[0.0, 0.0], [-9.17985022292322, 0.74185570341...   \n",
       "50046          -58.0  <NA>  [[0.0, 0.0], [-54.61671323674942, -33.84002165...   \n",
       "50047          -47.0  <NA>                       [[0.0, 0.0], [-36.0, -47.0]]   \n",
       "50048          -42.0  <NA>                       [[0.0, 0.0], [-48.0, -42.0]]   \n",
       "50049           72.0  <NA>  [[0.0, 0.0], [-8.34785332097252, 2.56320973960...   \n",
       "\n",
       "               cost  \n",
       "seed                 \n",
       "0        100.151629  \n",
       "2      18967.522925  \n",
       "4      28914.654143  \n",
       "5        186.095369  \n",
       "6      23898.229531  \n",
       "...             ...  \n",
       "50045     695.38234  \n",
       "50046     67.928607  \n",
       "50047     36.544878  \n",
       "50048     37.990761  \n",
       "50049  34269.035908  \n",
       "\n",
       "[38430 rows x 6 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collected_data = collected_data.loc[collected_data[\"cost\"] < DATA_UPPER_LIMIT_QUANTIL]\n",
    "collected_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:26.837920Z",
     "start_time": "2022-07-11T18:34:26.504277Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "collected_data[\"cost\"].plot.hist(log=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:28.494500Z",
     "start_time": "2022-07-11T18:34:26.842407Z"
    },
    "scrolled": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x1800 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "collected_data[collected_data[\"cost\"] < DATA_UPPER_LIMIT_QUANTIL]\n",
    "\n",
    "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.generated_obstacles,\n",
    "        obstacle_color=\"RED\",\n",
    "        route=row.route_generated,\n",
    "        title=f\"Cost: {row.cost:.3f}\",\n",
    "        legend=count == 0,\n",
    "    )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Visualize Complexity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:28.506644Z",
     "start_time": "2022-07-11T18:34:28.497676Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_route_points(data):\n",
    "    df = data[\"route\"].apply(lambda r: r.shape[0] - 1)\n",
    "    df.name = \"route complexity\"\n",
    "    return df\n",
    "\n",
    "\n",
    "route_points = get_route_points(collected_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:28.623493Z",
     "start_time": "2022-07-11T18:34:28.510989Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "route_points.plot.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:28.634493Z",
     "start_time": "2022-07-11T18:34:28.627196Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38430 - 2451 = 35979 if only routes with less then 15 course changes remain.\n"
     ]
    }
   ],
   "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",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:28.773895Z",
     "start_time": "2022-07-11T18:34:28.638505Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "get_route_points(collected_data).plot.hist(bins=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:28.789074Z",
     "start_time": "2022-07-11T18:34:28.777273Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     15146\n",
       "2      1139\n",
       "3       331\n",
       "4      1056\n",
       "5      3490\n",
       "6       826\n",
       "7      1070\n",
       "8      1389\n",
       "9      1554\n",
       "10     1764\n",
       "11     2028\n",
       "12     2067\n",
       "13     1791\n",
       "14     1333\n",
       "15      995\n",
       "Name: route complexity, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_route_points(collected_data).value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Dropping routes that are to easy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:28.801868Z",
     "start_time": "2022-07-11T18:34:28.793309Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Limiting simple cases to 5.0% of the total routes. Reducing simple routes to 11.3% of theire volume.\n"
     ]
    }
   ],
   "source": [
    "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(1, 1)\n",
    "    if 1 in values.index\n",
    "    else 1\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 theire volume.\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:28.945307Z",
     "start_time": "2022-07-11T18:34:28.806425Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "get_route_points(collected_data).plot.hist(bins=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:28.962854Z",
     "start_time": "2022-07-11T18:34:28.949690Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     1744\n",
       "2     1139\n",
       "3      331\n",
       "4     1056\n",
       "5     3490\n",
       "6      826\n",
       "7     1070\n",
       "8     1389\n",
       "9     1554\n",
       "10    1764\n",
       "11    2028\n",
       "12    2067\n",
       "13    1791\n",
       "14    1333\n",
       "15     995\n",
       "Name: route complexity, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_route_points(collected_data).value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:29.030033Z",
     "start_time": "2022-07-11T18:34:28.966774Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obstacles</th>\n",
       "      <th>destination_x</th>\n",
       "      <th>destination_y</th>\n",
       "      <th>image</th>\n",
       "      <th>route</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'0': POLYGON ((-17.62168766659423 -98.3692662...</td>\n",
       "      <td>-66.0</td>\n",
       "      <td>-54.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-6.514627334268863, -5.502693040...</td>\n",
       "      <td>100.151629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'0': POLYGON ((-46.23706006792075 -76.7569948...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [43.20648551245758, 31.2114102262...</td>\n",
       "      <td>18967.522925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'0': POLYGON ((-71.45682729091783 -138.627922...</td>\n",
       "      <td>-67.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-42.539218405821984, 15.14880405...</td>\n",
       "      <td>186.095369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'0': POLYGON ((10.806865516434499 -102.670968...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [10.886352485821806, -16.87002927...</td>\n",
       "      <td>63.479684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'0': POLYGON ((-38.740101054728726 -89.986420...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-8.211437427025228, -1.293253961...</td>\n",
       "      <td>16899.906926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50039</th>\n",
       "      <td>{'0': POLYGON ((-80.21298069840438 -87.2502584...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [5.67318252835214, -5.67318252835...</td>\n",
       "      <td>5162.824624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50041</th>\n",
       "      <td>{'0': POLYGON ((-18.017612906524075 -91.647295...</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-20.01287183186477, -22.10557708...</td>\n",
       "      <td>36.50201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50043</th>\n",
       "      <td>{'0': POLYGON ((-55.5210778390028 -66.95232495...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [3.868462226776941, 3.86846222677...</td>\n",
       "      <td>284.832436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50046</th>\n",
       "      <td>{'0': POLYGON ((2.518895755683328 -96.87282498...</td>\n",
       "      <td>-71.0</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-54.61671323674942, -33.84002165...</td>\n",
       "      <td>67.928607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50049</th>\n",
       "      <td>{'0': POLYGON ((-73.30908588454162 -74.1477834...</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-8.34785332097252, 2.56320973960...</td>\n",
       "      <td>34269.035908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22577 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               obstacles  destination_x  \\\n",
       "seed                                                                      \n",
       "0      {'0': POLYGON ((-17.62168766659423 -98.3692662...          -66.0   \n",
       "2      {'0': POLYGON ((-46.23706006792075 -76.7569948...           73.0   \n",
       "5      {'0': POLYGON ((-71.45682729091783 -138.627922...          -67.0   \n",
       "7      {'0': POLYGON ((10.806865516434499 -102.670968...           67.0   \n",
       "8      {'0': POLYGON ((-38.740101054728726 -89.986420...           58.0   \n",
       "...                                                  ...            ...   \n",
       "50039  {'0': POLYGON ((-80.21298069840438 -87.2502584...           74.0   \n",
       "50041  {'0': POLYGON ((-18.017612906524075 -91.647295...          -28.0   \n",
       "50043  {'0': POLYGON ((-55.5210778390028 -66.95232495...           47.0   \n",
       "50046  {'0': POLYGON ((2.518895755683328 -96.87282498...          -71.0   \n",
       "50049  {'0': POLYGON ((-73.30908588454162 -74.1477834...          -48.0   \n",
       "\n",
       "       destination_y image                                              route  \\\n",
       "seed                                                                            \n",
       "0              -54.0  <NA>  [[0.0, 0.0], [-6.514627334268863, -5.502693040...   \n",
       "2               49.0  <NA>  [[0.0, 0.0], [43.20648551245758, 31.2114102262...   \n",
       "5               37.0  <NA>  [[0.0, 0.0], [-42.539218405821984, 15.14880405...   \n",
       "7              -52.0  <NA>  [[0.0, 0.0], [10.886352485821806, -16.87002927...   \n",
       "8               61.0  <NA>  [[0.0, 0.0], [-8.211437427025228, -1.293253961...   \n",
       "...              ...   ...                                                ...   \n",
       "50039           31.0  <NA>  [[0.0, 0.0], [5.67318252835214, -5.67318252835...   \n",
       "50041          -36.0  <NA>  [[0.0, 0.0], [-20.01287183186477, -22.10557708...   \n",
       "50043           28.0  <NA>  [[0.0, 0.0], [3.868462226776941, 3.86846222677...   \n",
       "50046          -58.0  <NA>  [[0.0, 0.0], [-54.61671323674942, -33.84002165...   \n",
       "50049           72.0  <NA>  [[0.0, 0.0], [-8.34785332097252, 2.56320973960...   \n",
       "\n",
       "               cost  \n",
       "seed                 \n",
       "0        100.151629  \n",
       "2      18967.522925  \n",
       "5        186.095369  \n",
       "7         63.479684  \n",
       "8      16899.906926  \n",
       "...             ...  \n",
       "50039   5162.824624  \n",
       "50041      36.50201  \n",
       "50043    284.832436  \n",
       "50046     67.928607  \n",
       "50049  34269.035908  \n",
       "\n",
       "[22577 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collected_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:29.039493Z",
     "start_time": "2022-07-11T18:34:29.033596Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "del chance_limit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Memory consumption"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:29.105061Z",
     "start_time": "2022-07-11T18:34:29.043526Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>obstacles</th>\n",
       "      <th>destination_x</th>\n",
       "      <th>destination_y</th>\n",
       "      <th>image</th>\n",
       "      <th>route</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>{'0': POLYGON ((-17.62168766659423 -98.3692662...</td>\n",
       "      <td>-66.0</td>\n",
       "      <td>-54.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-6.514627334268863, -5.502693040...</td>\n",
       "      <td>100.151629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>{'0': POLYGON ((-46.23706006792075 -76.7569948...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [43.20648551245758, 31.2114102262...</td>\n",
       "      <td>18967.522925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>{'0': POLYGON ((-71.45682729091783 -138.627922...</td>\n",
       "      <td>-67.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-42.539218405821984, 15.14880405...</td>\n",
       "      <td>186.095369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>{'0': POLYGON ((10.806865516434499 -102.670968...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>-52.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [10.886352485821806, -16.87002927...</td>\n",
       "      <td>63.479684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>{'0': POLYGON ((-38.740101054728726 -89.986420...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-8.211437427025228, -1.293253961...</td>\n",
       "      <td>16899.906926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50039</th>\n",
       "      <td>{'0': POLYGON ((-80.21298069840438 -87.2502584...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [5.67318252835214, -5.67318252835...</td>\n",
       "      <td>5162.824624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50041</th>\n",
       "      <td>{'0': POLYGON ((-18.017612906524075 -91.647295...</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-20.01287183186477, -22.10557708...</td>\n",
       "      <td>36.50201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50043</th>\n",
       "      <td>{'0': POLYGON ((-55.5210778390028 -66.95232495...</td>\n",
       "      <td>47.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [3.868462226776941, 3.86846222677...</td>\n",
       "      <td>284.832436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50046</th>\n",
       "      <td>{'0': POLYGON ((2.518895755683328 -96.87282498...</td>\n",
       "      <td>-71.0</td>\n",
       "      <td>-58.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-54.61671323674942, -33.84002165...</td>\n",
       "      <td>67.928607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50049</th>\n",
       "      <td>{'0': POLYGON ((-73.30908588454162 -74.1477834...</td>\n",
       "      <td>-48.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>[[0.0, 0.0], [-8.34785332097252, 2.56320973960...</td>\n",
       "      <td>34269.035908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22577 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               obstacles  destination_x  \\\n",
       "seed                                                                      \n",
       "0      {'0': POLYGON ((-17.62168766659423 -98.3692662...          -66.0   \n",
       "2      {'0': POLYGON ((-46.23706006792075 -76.7569948...           73.0   \n",
       "5      {'0': POLYGON ((-71.45682729091783 -138.627922...          -67.0   \n",
       "7      {'0': POLYGON ((10.806865516434499 -102.670968...           67.0   \n",
       "8      {'0': POLYGON ((-38.740101054728726 -89.986420...           58.0   \n",
       "...                                                  ...            ...   \n",
       "50039  {'0': POLYGON ((-80.21298069840438 -87.2502584...           74.0   \n",
       "50041  {'0': POLYGON ((-18.017612906524075 -91.647295...          -28.0   \n",
       "50043  {'0': POLYGON ((-55.5210778390028 -66.95232495...           47.0   \n",
       "50046  {'0': POLYGON ((2.518895755683328 -96.87282498...          -71.0   \n",
       "50049  {'0': POLYGON ((-73.30908588454162 -74.1477834...          -48.0   \n",
       "\n",
       "       destination_y image                                              route  \\\n",
       "seed                                                                            \n",
       "0              -54.0  <NA>  [[0.0, 0.0], [-6.514627334268863, -5.502693040...   \n",
       "2               49.0  <NA>  [[0.0, 0.0], [43.20648551245758, 31.2114102262...   \n",
       "5               37.0  <NA>  [[0.0, 0.0], [-42.539218405821984, 15.14880405...   \n",
       "7              -52.0  <NA>  [[0.0, 0.0], [10.886352485821806, -16.87002927...   \n",
       "8               61.0  <NA>  [[0.0, 0.0], [-8.211437427025228, -1.293253961...   \n",
       "...              ...   ...                                                ...   \n",
       "50039           31.0  <NA>  [[0.0, 0.0], [5.67318252835214, -5.67318252835...   \n",
       "50041          -36.0  <NA>  [[0.0, 0.0], [-20.01287183186477, -22.10557708...   \n",
       "50043           28.0  <NA>  [[0.0, 0.0], [3.868462226776941, 3.86846222677...   \n",
       "50046          -58.0  <NA>  [[0.0, 0.0], [-54.61671323674942, -33.84002165...   \n",
       "50049           72.0  <NA>  [[0.0, 0.0], [-8.34785332097252, 2.56320973960...   \n",
       "\n",
       "               cost  \n",
       "seed                 \n",
       "0        100.151629  \n",
       "2      18967.522925  \n",
       "5        186.095369  \n",
       "7         63.479684  \n",
       "8      16899.906926  \n",
       "...             ...  \n",
       "50039   5162.824624  \n",
       "50041      36.50201  \n",
       "50043    284.832436  \n",
       "50046     67.928607  \n",
       "50049  34269.035908  \n",
       "\n",
       "[22577 rows x 6 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collected_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:29.149676Z",
     "start_time": "2022-07-11T18:34:29.111848Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'246.8 kB'"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_image_maps(row, route_type: Literal[\"dot\", \"line\"]):\n",
    "    img = np.expand_dims(\n",
    "        np.asarray(\n",
    "            generate_image_from_map(\n",
    "                obstacles=row.generated_obstacles,\n",
    "                destination=Point(row.destination_x, row.destination_y),\n",
    "                route=row.route_generated,\n",
    "                route_type=route_type,\n",
    "                seed=row.name,\n",
    "            )\n",
    "        ),\n",
    "        axis=0,\n",
    "    )\n",
    "    img = img // 0xFF\n",
    "    return img\n",
    "\n",
    "\n",
    "generated = collected_data.head().apply(generate_image_maps, axis=1, args=(\"dot\",))\n",
    "humanize.naturalsize(generated.memory_usage(deep=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:29.160218Z",
     "start_time": "2022-07-11T18:34:29.154190Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "if \"image\" in collected_data.columns:\n",
    "    del collected_data[\"image\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:30.030972Z",
     "start_time": "2022-07-11T18:34:29.164607Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "DATA_WITH_IMG_PATH: Final[str] = \"data/collected_and_filtered.pickle\"\n",
    "if os.path.exists(DATA_WITH_IMG_PATH) and not GENERATE_NEW:\n",
    "    collected_data = pd.read_pickle(DATA_WITH_IMG_PATH)\n",
    "else:\n",
    "    collected_data.to_pickle(DATA_WITH_IMG_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.027458Z",
     "start_time": "2022-07-11T18:34:30.033852Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f515ae374e374de7aba6b584b54b9a62",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/22577 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_series = collected_data.progress_apply(\n",
    "    generate_image_maps, axis=1, args=(\"line\",)\n",
    ")\n",
    "\n",
    "#  collected_data[\"image_lines\"] = collected_data.apply(\n",
    "#      generate_image_maps, axis=1, args=(\"line\",)\n",
    "#  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.085664Z",
     "start_time": "2022-07-11T18:34:35.032718Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "collected_routes = np.concatenate(image_series)\n",
    "del image_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.095020Z",
     "start_time": "2022-07-11T18:34:35.088433Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.1 GB'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humanize.naturalsize(sys.getsizeof(collected_routes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.107314Z",
     "start_time": "2022-07-11T18:34:35.099032Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collected_routes.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.124351Z",
     "start_time": "2022-07-11T18:34:35.111145Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'collected_routes': '1.1 GB',\n",
       " 'collected_data': '27.4 MB',\n",
       " 'route_points': '2.7 MB',\n",
       " 'generated': '246.9 kB',\n",
       " 'row': '2.0 kB',\n",
       " 'CartesianRoute': '1.1 kB',\n",
       " 'LineString': '1.1 kB',\n",
       " 'Point': '1.1 kB',\n",
       " 'TimingFrame': '1.1 kB',\n",
       " 'Polygon': '904 Bytes'}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "memory = sorted(\n",
    "    [\n",
    "        (x, sys.getsizeof(globals().get(x)))\n",
    "        for x in dir()\n",
    "        if not x.startswith(\"_\") and x not in sys.modules\n",
    "    ],\n",
    "    key=lambda x: x[1],\n",
    "    reverse=True,\n",
    ")\n",
    "memory = {name: humanize.naturalsize(mem) for name, mem in memory[:10]}\n",
    "memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.811285Z",
     "start_time": "2022-07-11T18:34:35.128881Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "COLLECTED_ROUTES_DUMP = \"data/collected_routes_np.pickle\"\n",
    "with open(COLLECTED_ROUTES_DUMP, \"wb\") as f:\n",
    "    pickle.dump(collected_routes, f)\n",
    "\n",
    "# with open(COLLECTED_ROUTES_DUMP,'rb') as f: collected_routes = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "[Pix2Pix Tensorflow](https://www.tensorflow.org/tutorials/generative/pix2pix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.846820Z",
     "start_time": "2022-07-11T18:34:35.815078Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-12 22:54:12.025009: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-07-12 22:54:12.025051: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-07-12 22:54:12.025069: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (6c2cdca2902f): /proc/driver/nvidia/version does not exist\n",
      "2022-07-12 22:54:12.025335: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.engine.sequential.Sequential at 0x7fe5c15cfa60>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Source: https://www.tensorflow.org/tutorials/generative/pix2pix\n",
    "def downsample(filters, size, apply_batchnorm=True):\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\n",
    "\n",
    "\n",
    "downsample(64, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.859119Z",
     "start_time": "2022-07-11T18:34:35.852272Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 128, 3)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collected_routes[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.873070Z",
     "start_time": "2022-07-11T18:34:35.862530Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([1, 128, 128, 3])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.expand_dims(collected_routes[0], 0).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.948246Z",
     "start_time": "2022-07-11T18:34:35.877228Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 64, 64, 3)\n"
     ]
    }
   ],
   "source": [
    "down_model = downsample(3, 4)\n",
    "tf.cast(tf.expand_dims(collected_routes[1], 0), \"float16\", name=None)\n",
    "\n",
    "down_result = down_model(\n",
    "    tf.cast(tf.expand_dims(collected_routes[1], 0), \"float16\", name=None)\n",
    ")\n",
    "print(down_result.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:35.959098Z",
     "start_time": "2022-07-11T18:34:35.953165Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# Source: https://www.tensorflow.org/tutorials/generative/pix2pix\n",
    "def upsample(filters, size, apply_dropout=False):\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:36.012492Z",
     "start_time": "2022-07-11T18:34:35.963668Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([1, 128, 128, 3])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "up_model = upsample(3, 4)\n",
    "up_result = up_model(down_result)\n",
    "up_result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:36.888819Z",
     "start_time": "2022-07-11T18:34:36.015415Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Generator():\n",
    "    OUTPUT_CHANNELS: Final[int] = 1\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(512, 4),  # (batch_size, 4, 4, 512)\n",
    "    #    downsample(512, 4),  # (batch_size, 2, 2, 512)\n",
    "    #    downsample(512, 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(512, 4, apply_dropout=True),  # (batch_size, 4, 4, 1024)\n",
    "    #    upsample(512, 4, apply_dropout=True),  # (batch_size, 4, 4, 1024)\n",
    "    #    upsample(512, 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",
    "    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(256, 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(256, 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",
    "        OUTPUT_CHANNELS,\n",
    "        4,\n",
    "        strides=2,\n",
    "        padding=\"same\",\n",
    "        kernel_initializer=initializer,\n",
    "        activation=\"tanh\",\n",
    "    )  # (batch_size, 256, 256, 3)\n",
    "\n",
    "    x = inputs\n",
    "\n",
    "    # Downsampling 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",
    "    # Upsampling 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",
    "    return tf.keras.Model(inputs=inputs, outputs=x)\n",
    "\n",
    "\n",
    "generator = Generator()\n",
    "tf.keras.utils.plot_model(generator, show_shapes=True, dpi=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pydot in /usr/local/lib/python3.10/dist-packages (1.4.2)\n",
      "Requirement already satisfied: pyparsing>=2.1.4 in /usr/lib/python3/dist-packages (from pydot) (2.4.7)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install pydot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pydotplus in /usr/local/lib/python3.10/dist-packages (2.0.2)\n",
      "Requirement already satisfied: pyparsing>=2.0.1 in /usr/lib/python3/dist-packages (from pydotplus) (2.4.7)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install pydotplus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:36.909543Z",
     "start_time": "2022-07-11T18:34:36.893252Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "generator.compile(\n",
    "    optimizer=tf.keras.optimizers.RMSprop(),  # Optimizer\n",
    "    # Loss function to minimize\n",
    "    loss=\"mean_squared_error\",\n",
    "    # tf.keras.losses.SparseCategoricalCrossentropy(),\n",
    "    # List of metrics to monitor\n",
    "    metrics=[\n",
    "        \"binary_crossentropy\",\n",
    "        \"mean_squared_error\",\n",
    "        \"mean_absolute_error\",\n",
    "    ],  # root_mean_squared_error\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:36.922590Z",
     "start_time": "2022-07-11T18:34:36.913165Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'tfa' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [61]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m tqdm_callback \u001b[38;5;241m=\u001b[39m \u001b[43mtfa\u001b[49m\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mTQDMProgressBar(\n\u001b[1;32m      2\u001b[0m     leave_epoch_progress\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, leave_overall_progress\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, show_epoch_progress\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m      3\u001b[0m )\n\u001b[1;32m      5\u001b[0m early_stop \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(\n\u001b[1;32m      6\u001b[0m     monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmean_squared_error\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      7\u001b[0m     min_delta\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0005\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     11\u001b[0m     restore_best_weights\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m     12\u001b[0m )\n\u001b[1;32m     14\u001b[0m tf_board \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mTensorBoard(\n\u001b[1;32m     15\u001b[0m     log_dir\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./log_dir\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     16\u001b[0m     histogram_freq\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     23\u001b[0m     embeddings_metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m     24\u001b[0m )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'tfa' is not defined"
     ]
    }
   ],
   "source": [
    "tqdm_callback = tfa.callbacks.TQDMProgressBar(\n",
    "    leave_epoch_progress=False, leave_overall_progress=True, show_epoch_progress=True\n",
    ")\n",
    "\n",
    "early_stop = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor=\"mean_squared_error\",\n",
    "    min_delta=0.0005,\n",
    "    patience=2,\n",
    "    verbose=0,\n",
    "    mode=\"auto\",\n",
    "    restore_best_weights=True,\n",
    ")\n",
    "\n",
    "tf_board = tf.keras.callbacks.TensorBoard(\n",
    "    log_dir=\"./log_dir\",\n",
    "    histogram_freq=100,\n",
    "    write_graph=False,\n",
    "    write_images=False,\n",
    "    write_steps_per_second=True,\n",
    "    update_freq=\"epoch\",\n",
    "    profile_batch=(20, 40),\n",
    "    embeddings_freq=0,\n",
    "    embeddings_metadata=None,\n",
    ")\n",
    "\n",
    "reduce_learing_rate = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "    monitor=\"some metric\", factor=0.2, patience=5, min_lr=000.1, verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:37.387799Z",
     "start_time": "2022-07-11T18:34:36.925978Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:37.398038Z",
     "start_time": "2022-07-11T18:34:37.391964Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "collected_routes[:, :, :, :2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:37.769969Z",
     "start_time": "2022-07-11T18:34:37.402840Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_dataset = tf.data.Dataset.from_tensor_slices(\n",
    "    (collected_routes[:, :, :, :2], collected_routes[:, :, :, 2])\n",
    ")\n",
    "# test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:37.780494Z",
     "start_time": "2022-07-11T18:34:37.772901Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:37.789908Z",
     "start_time": "2022-07-11T18:34:37.784588Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "SHUFFLE_BUFFER_SIZE = 100\n",
    "# train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T18:34:37.800259Z",
     "start_time": "2022-07-11T18:34:37.794044Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "train_dataset = train_dataset.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T20:34:10.831132Z",
     "start_time": "2022-07-11T18:34:37.804670Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "history = generator.fit(\n",
    "    train_dataset,\n",
    "    epochs=20,\n",
    "    batch_size=512,\n",
    "    use_multiprocessing=True,\n",
    "    workers=5,\n",
    "    callbacks=[early_stop, tf_board],\n",
    "    # tqdm_callback,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T20:35:09.893719Z",
     "start_time": "2022-07-11T20:35:09.785795Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.plot(history.history[\"loss\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T20:36:41.233512Z",
     "start_time": "2022-07-11T20:36:41.228150Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "collected_routes[0:1, :, :, :2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T20:40:42.106870Z",
     "start_time": "2022-07-11T20:38:39.288499Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "predicted = generator.predict(\n",
    "    collected_routes[:100, :, :, :2],\n",
    "    batch_size=None,\n",
    "    verbose=\"auto\",\n",
    "    steps=None,\n",
    "    callbacks=None,\n",
    "    max_queue_size=10,\n",
    "    workers=3,\n",
    "    use_multiprocessing=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T20:40:42.284261Z",
     "start_time": "2022-07-11T20:40:42.275481Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "predicted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T20:40:42.419205Z",
     "start_time": "2022-07-11T20:40:42.290807Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.imshow(predicted[1, :, :, 0], interpolation=\"nearest\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T20:40:42.270774Z",
     "start_time": "2022-07-11T20:40:42.111264Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "for pos in range(5):\n",
    "    plt.imshow(\n",
    "        predicted[pos, :, :, 0] * 0xFF + collected_routes[pos, :, :, 0] * 20,\n",
    "        interpolation=\"nearest\",\n",
    "    )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T20:34:11.274201Z",
     "start_time": "2022-07-11T20:34:11.274188Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# tf.keras.utils.plot_model(generator)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-11T16:47:19.020872Z",
     "start_time": "2022-07-11T16:47:17.607427Z"
    },
    "pycharm": {
     "name": "#%% raw\n"
    }
   },
   "source": [
    "!pip install pydot pydotplus graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "@article{article,\n",
    "author = {Jang, Hoyun and Lee, Inwon and Seo, Hyoungseock},\n",
    "year = {2017},\n",
    "month = {09},\n",
    "pages = {4109-4117},\n",
    "title = {Effectiveness of CFRP rudder aspect ratio for scale model catamaran racing yacht test},\n",
    "volume = {31},\n",
    "journal = {Journal of Mechanical Science and Technology},\n",
    "doi = {10.1007/s12206-017-0807-8}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Ich würde auch zu 1. tendieren, stimme Ihnen aber zu, dass das Thema sehr umfangreich ist. Könnte man sich nicht einen Teilbereich herauspicken? Ich verstehe nicht viel vom Segeln, daher lassen Sie mich kurz zusammenfassen, was Sie vorhaben: - Sie generieren Trainingsdaten mit dem existierenden aber langsamen GD Algorithmus. Ich nehme an, es handelt sich um lokale Routen in einem relativ kleinen Kartenausschnitt. Lässt es die Laufzeit zu, dass Sie eine große Menge an Routen berechnen. - Sie haben dann eine Karte und als Ausgabe eine Liste der Wendepunkte - Warum wollen Sie daraus eine Heatmap berechnen? Diesen Schritt habe ich noch nicht verstanden - Wenn Sie aus einer Karte eine Heatmap trainieren wollen und dafür genügend Beispiele haben, könnnten GANs hilfreich sein: https://arxiv.org/abs/1611.07004 Ich würde Ihnen raten, das Problem möglichst so zu reduzieren, dass es im Rahmen des Moduls noch handhabbar bleibt. Alles Weitere kann man sich auch für spätere Arbeiten aufbewahren. Das 2. Thema ist auch ok. Aber vielleicht nicht ganz so spannend. Ich überlasse Ihnen die Entscheidung. Freundliche Grüße Heiner Giefers"
   ]
  }
 ],
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