Generation mode aktivated again

2022-07-04 00:20:08 +02:00
1 parent d6eb952226
commit bd29d92d64
@@ -44,6 +44,7 @@ repos:
    args: [--autofix]
  - id: pretty-format-yaml
    args: [--autofix]
+    exclude: (docker-compose.yaml$)

 - repo: https://github.com/jendrikseipp/vulture
  rev: v2.3  # or any later Vulture version
@@ -11,8 +11,20 @@ services:
    volumes:
    - ./experiments:/pyrate/experiments
    - ./data:/pyrate/data
-    # scale: 1
+    scale: 0

+  pyrate-gen:
+    build:
+      context: pyrate
+      dockerfile: pyrate.data_generator.dockerfile
+    restart: "unless-stopped"
+    volumes:
+    - ./data:/pyrate/data
+    environment:
+    - save_frequency=50
+    - seed_start=150
+    - continues=False
+    scale: 8

 volumes:
  experiments:
@@ -1,31 +0,0 @@
-version: '3.9'
-services:
-  pyrate:
-    build:
-      context: pyrate
-      dockerfile: pyrate.dockerfile
-    restart: "no"
-    container_name: pyrate
-    ports:
-    - "8888:8888"
-    volumes:
-    - ./experiments:/pyrate/experiments
-    - ./data:/pyrate/data
-    # scale: 1
-
-  pyrate-gen:
-    build:
-      context: pyrate
-      dockerfile: pyrate.data_generator.dockerfile
-    restart: "unless-stopped"
-    volumes:
-    - ./data:/pyrate/data
-    environment:
-    - save_frequency=50
-    - seed_start=150
-    - continues=False
-    scale: 0
-
-volumes:
-  experiments:
-  data:
@@ -68,29 +68,6 @@
     "name": "#%%\n"
    }
   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.64.0)\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 tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
   "outputs": [],
   "source": [
    "import os\n",
@@ -109,7 +86,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
@@ -125,7 +102,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
@@ -139,7 +116,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
@@ -154,7 +131,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
@@ -195,7 +172,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
@@ -205,11 +182,11 @@
    {
     "data": {
      "text/plain": [
-       "(array('d', [17.51127620448112, 6.747269675298146, 6.0133154595194, 18.947057950054642, 17.51127620448112]),\n",
-       " array('d', [-58.12293483990852, -54.65585353492957, -54.98056229087878, -63.39394014030877, -58.12293483990852]))"
+       "(array('d', [-86.18119184916772, -87.90889551960576, -87.947661741894, -86.99618756345707, -86.61726076535952, -86.3345620264375, -86.054807055551, -86.18119184916772]),\n",
+       " array('d', [-78.52226115759798, -78.73057445583323, -78.97513210182939, -79.9479803029265, -79.86728995594082, -79.67518404361331, -79.0727426090598, -78.52226115759798]))"
      ]
     },
-     "execution_count": 8,
+     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -247,7 +224,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
@@ -258,7 +235,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "POLYGON ((-11.633473233606555 -10.080092068385213, -14.904531044931492 -12.526815460291699, -14.902438460588852 -13.485855104001587, -10.070348885202387 -15.221857505500772, -9.06908919216602 -13.264510937603255, -9.067607303085786 -13.247542462365493, -11.633473233606555 -10.080092068385213))\n"
+      "POLYGON ((-95.10069931721466 92.77567449205789, -95.2897018683966 92.72655632786748, -95.32388941964963 92.7119739559533, -94.24521712866358 91.79478282904707, -95.10069931721466 92.77567449205789))\n"
     ]
    }
   ],
@@ -268,7 +245,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
@@ -310,7 +287,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
@@ -360,7 +337,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
@@ -371,12 +348,12 @@
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "986e8fd68b88482eb48b14821a38370b",
+       "model_id": "d0f274f8f9d14eac87e5ec988a8bdc64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
-       "  0%|          | 0/19 [00:00<?, ?it/s]"
+       "  0%|          | 0/150 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
@@ -384,7 +361,7 @@
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAHiCAYAAAAEZd6CAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAB41klEQVR4nO3dZ3RUVReA4fekN2roIlUQqQEDFhApKioqqKAoqNiwoWLvXRQr9l4QBSxYQKxIR/CTgHREBAGR3klv5/uxEwikJzNz587sZ61ZSabc2clkZt/T9jHWWpRSSikVWEKcDkAppZRSnqcJXimllApAmuCVUkqpAKQJXimllApAmuCVUkqpAKQJXimllApAmuCVUuVijBlqjJnrdBxKqZJpglcqQBhjuhlj5hlj9hljdhtjfjXGdPaDuBKMMQuNMal5XxOcjkmpYKAJXqkAYIypCkwBXgVqAkcBjwEZDscVAUwCPgFqAB8Bk/KuV0p5kSZ4pQJDSwBr7QRrbY61Ns1a+7O1dmn+HYwxVxljVhlj9hhjfjLGNC5wWytjzNS8lv9qY8xFBW6LN8ZMNsbsN8b8DjQvR1w9gDDgJWtthrX2FcAAvSr5+yqlSqEJXqnA8BeQY4z5yBhzljGmRsEbjTH9gPuBC4DawBxgQt5tscBUYDxQBxgEvGGMaZ338NeBdKA+cFXepeCxpxhj7i0mrjbAUnt4TeyledcrpbxIE7xSAcBaux/oBljgXWBHXqu7bt5drgeettaustZmA08BCXmt+HOA9dbaD6212dbaP4AvgYHGmFDgQuBha22KtXY50s1e8LnPsdaOKia0OGDfEdftA6pU+pdWSpVIE7xSASIveQ+11jYE2gINgJfybm4MvGyM2WuM2QvsRrrKj8q77YT82/JuHwzUQ1r7YcC/BZ5qQznCSgaqHnFdVeBAOY6hlKoATfBKBSBr7Z/AGCTRgyTo66y11Qtcoq218/Jum3XEbXHW2huAHUA2cHSBwzcqRygrgPbGGFPguvZ51yulvEgTvFIBIG+S3B3GmIZ5Px8NXAL8lneXt4D7jDFt8m6vZowZmHfbFKClMeYyY0x43qWzMeY4a20O8BXwqDEmJm9c/opyhDYTyAFuMcZEGmOG510/vTK/r1KqdJrglQoMB4ATgP8ZY1KQxL4cuAPAWvs18AzwqTFmf95tZ+XddgA4A5lctxnYmnffyLxjD0fG0rcivQIfFnxiY8wPxpj7iwrKWpsJ9AcuB/YiE/T6512vlPIic/jkVqWUUkoFAm3BK6WUUgHIIwneGPOBMWa7MWZ5getq5hXOWJP3tUbe9cYY84ox5m9jzFJjTCdPxKCUUkqpQzzVgh8DnHnEdfcC06y1LYBpeT+DjPu1yLsMA970UAxKKaWUyuORBG+tnY2sqy2oH4cKYnyETLTJv36sFb8B1Y0x9T0Rh1JKKaWEN8fg61prt+R9vxXIr6h1FIcXzdiUd51SSimlPCTMF09irbXGmHJN1zfGDEO68ImNjT2+VatWXolNKeVncnNh2zbYuhWslYs/MAbq15eLUj6wcOHCndba2hV9vDcT/DZjTH1r7Za8Lvjtedf/x+FVsRrmXXcYa+07wDsAiYmJNikpyYuhKqUcl5UF774LDzwAGRmS6P2JtbB3LxxzDEycCLUr/LmrVJkYY8pTFroQb3bRT+ZQxasrkD2h86+/PG82/YnAvgJd+UqpYJObC599Bk2awN13SxJNS3M6qqKlpsL8+dCqFcye7XQ0SpXIU8vkJgDzgWONMZuMMVcDo4DTjTFrgNPyfgb4HlgH/I3senWjJ2JQSrnQL79A69Zw9dWweTOkpDgdUemysmD3bjjzTHjkEcjJcToipYrkikp22kWvVIBZuBCGD4elS6VV7FYxMdCxI8yZI2P0yreuuw6uuAJOPtnpSLzCGLPQWptY0cf7ZJKdUkoBsGYN3HYbTJ/uv93w5ZGaCsuWQXY2hIc7HY1HZGVlsWnTJtLT050OpXTnny9/+1WrnI6kUqKiomjYsCHhHv4f0gSvlPK+LVvg/vvh00+liztQurWjo+HOOwMmuQNs2rSJKlWq0KRJE4y/90qkpckcjuOOczqSCrPWsmvXLjZt2kTTpk09emytRa+U8p59++Cuu6B5cxg3DtLTAye5g3TL33yz01F4VHp6OvHx8f6f3AEaNJCvLhhqLo4xhvj4eK/0mGiCV0p5Xno6PPMMNGwIr70mLa2sLKej8qyICJkcWL2605F4nCuSO0B8vHzNyHA2jkry1t9bE7xSynNycuD99yWxP/44JCdLsg9EISFwzz1ORxGwNm3aRL9+/WjRogXNmzfn1ltvJTMzkzFjxjB8+HC5U/7QyPbtxR7npZdeIrWCEzlnzpzJOeecU6HH+gNN8EqpyrMWvvkGmjWDW2+FXbvcPTu+NKGhMsHrqCCosl2vngxFeOpSr16pT2mt5YILLqB///6sWbOGv/76i+TkZB544IGiH+ClBO92muCVUpUzdy4kJMCQIbBxozvWsldWeDg8/LDTUfjGtm0+P9706dOJioriyiuvBCA0NJTRo0fzwQcfkJqayr///kuPHj1o0aIFj02YAEBKSgp9+/alQ4cOtG3bls8++4xXXnmFzZs307NnT3r27AnADTfcQGJiIm3atOGRRx45+JwLFizg5JNPpkOHDnTp0oUDBw4cFlNKSgpXXXUVXbp0oWPHjkyaNAl/p7PolVIVs2wZ3HIL/P57YLfWj2QMnHKKVLNTXrFixQqOP/74w66rWrUqjRo1Ijs7m99//53ly5cTExND58RE+nbowIa1a2nQoAHfffcdAPv27aNatWq8+OKLzJgxg1q1agEwcuRIatasSU5ODr1792bp0qW0atWKiy++mM8++4zOnTuzf/9+oqOjD3v+kSNH0qtXLz744AP27t1Lly5dOO2004iNjfXNH6UCtAXvVhs3unrmqHKx9ethwAA44QSYNSu4kjvI0rgnnnA6iqB2+umnEx8fT3R0NBdceCFz//mHdu3aMXXqVO655x7mzJlDtWrVinzs559/TqdOnejYsSMrVqxg5cqVrF69mvr169O5c2dATibCwg5v//7888+MGjWKhIQEevToQXp6Ohs3bvT671oZmuDd6NtvoXFjuPjiwJuZrPzXzp1w442y5njSJJkZH4wnma1aycmN8prWrVuzcOHCw67bv38/GzduJCwsrNCsc1O9Oi1bt2bRokW0a9eOBx98kMcff7zQcf/55x+ef/55pk2bxtKlS+nbt2+Zl6dZa/nyyy9ZvHgxixcvZuPGjRzn5+vvNcG7zcaNMHiwfD9lCvTsCUeMFSnlUcnJMt7cuDF88IHMis/OdjoqZ8TFwZNPOh1FwOvduzepqamMHTsWgJycHO644w6GDh1KTEwMU6dOZffu3aSlpfHNN9/QtWtXNm/eTExMDEOGDOGuu+5i0aJFAFSpUuXgePr+/fuJjY2lWrVqbNu2jR9++AGAY489li1btrBgwQIADhw4QPYR/+N9+vTh1VdfJb+8+x9//OGTv0VlaIJ3k6wsOOecQ12iaWmQlASdO8ve2Up5UmYmvPKKLHl7/nn5v3P5euNKq1NHNplRXmWM4euvv+aLL76gRYsWtGzZkqioKJ566ikAunTpwoUXXkj79u258MILSUxMZNmyZXTp0oWEhAQee+wxHnzwQQCGDRvGmWeeSc+ePenQoQMdO3akVatWXHrppXTt2hWAiIgIPvvsM26++WY6dOjA6aefXqhl/9BDD5GVlUX79u1p06YNDz30kG//KBWgm824yS23yBrjI8c8w8Kk4MPs2dCypTOxqcCRmyslZW+/XVrvwTArvizi4uDNN2W1QABbtWrV4V3P9ep5diZ93braIClCob87utlMcPnss6LH3LOzZR1o587w009w4om+j025n7Xw889SetUtW7f6UnQ0DBrkdBS+p8nYtbSL3k2WLYOBA+WD5kjWwv790Ls3TJ7s+9iUu/3+u0wcu/BC2fFNk/vhYmPhwQelt0wpl9AE7yZ16siGHbNnQ7t28qFzpNRUaWW89Zbv41Pus3o1nHUW9OgBCxZoYi9OSAhcc43TUShVLprg3SgxERYvhtdfl40uoqIOvz0tTcZP778/OJcxqdL99x9cdplUoPv5Z0hL40+geXgUjSKiS7y8ERJkrdioKCm/GxPjdCRKlUuQvVMDSEgIXHEFXHABPPKItNgzMmSCFEiSf/ll2LABPvpIuxaV2LNHirS89ZbM3Sgwp+NPYG/80UT2va3Yh6eu/pU5/5vIjblBtEzOGBgxwukolCo3bcG7XZUq8OKLsGQJnHrq4d32qanw9ddw+una9Rrs0tLgqafg6KNlJngx27eGhUcRUatRsZfQuHgHgndQeDhcfvmhbUmVchFN8IGiRQuYPh2++EJ2uMrvTkxLg/nzZQLVjh3Oxqh8Lzsb3nlH1rKPHCkneoG6fas3hIbKUJdSLqQJPtCcdRasWwcPPSRJPjxcuu5Xr5bx1rVrnY5Q+YK18OWX0LSpzMfYvTv4asZXVkgI9O0LjRo5HYmj6jdshDHGY5f6DUv+e95222289NJLB3/u06cP1xSY4HjHHXfw4osvMnnyZEaNGlWu32Xo0KFMnDixyNtGjBjB7NmzAbj66qvp0KED7du3Z8CAASQnJwPw6KOPctRRR5GQkECrVq244YYbyM0bFv3zzz9JSEigY8eOrF27lvHjxx889rJlyxg6dGi5YvUEHZgNRBERcO+9MkY/YoTUrk9Lgy1b4Pjj4ZdfZKKeCkwzZ8Lw4bIpjA7NVFxkJDz6qNNROG7rf//S+J4pHjvehmfOKfH2rl278vnnnzNixAhyc3PZuXMn+/fvP3j7vHnzGD16NCeeeCLnnXeeR2LatWsXv/3228ETi9GjR1O1alUAbr/9dl577TXuvfdeQE5A7rzzTnJzc+nevTuzZs2iZ8+efPPNNwwYMIAHH3yLine truncated
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
@@ -398,12 +375,12 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "57.23909246162316\n"
+      "100.1516289789457\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
@@ -417,25 +394,12 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "25831.169166603508\n",
-      "Error\n"
+      "75292.76193632427\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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truncated
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
@@ -449,25 +413,12 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "102.22298895814752\n",
-      "Error\n"
+      "18967.522924599307\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
@@ -481,12 +432,12 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "14540.683344260437\n"
+      "63200.63075818353\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
@@ -500,12 +451,12 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "19910.99035961719\n"
+      "28914.654143248754\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAHiCAYAAAAEZd6CAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAACIaElEQVR4nO3dd3hT1RsH8O9pupK27L33HgUKMgTZUwWRjchGRFQQFRT9KSqKE5woKgoqyFAQGSqyl0gZsveGsqGlTbrS8/vjbWgpHRn35t6bvJ/nyUObJveekjTvPee85z1CSgnGGGOM+ZYArRvAGGOMMeVxgGeMMcZ8EAd4xhhjzAdxgGeMMcZ8EAd4xhhjzAdxgGeMMcZ8EAd4xphLhBBDhBCbtW4HYyx3HOAZ8xFCiPuFEFuFELFCiBtCiC1CiMY6aJcUQiQIIeLTb99o3SbG/EGg1g1gjHlOCJEPwHIATwJYCCAYQEsASVq2K5P6UsrjWjeCMX/CPXjGfEM1AJBSzpdS2qWUNinlX1LKvY4HCCGGCSEOCSFuCiH+FEKUz/SzGkKI1ek9/yNCiD6ZflZYCLFMCBEnhPgXQGWv/maMMbdwgGfMNxwFYBdCzBFCdBFCFMz8QyFEdwAvA+gJoCiATQDmp/8sDMBqAPMAFAPQD8AXQoha6U//HEAigJIAhqXfMh97uRBiUh7t2yiEuCSE+FUIUcH9X5Mx5izBtegZ8w1CiJoAJgJoD6AEgJUARkopLwshVgFYLKX8Nv2xAQDiAdQE0BTAWClly0zH+grARQBvgYJ7XSnl4fSfvQ2glZTyfifb1QrAPwAs6cdrDSBSSpnq8S/NGMsR9+AZ8xFSykNSyiFSyjIA6gAoBWBG+o/LA/hYCHFLCHELwA0AAkDp9J/d5/hZ+s8Hgi4SioJydc5lOtUZF9u1UUqZLKW8BeBZABVBFxaMMRVxkh1jPkhKeVgI8T2AJ9LvOgdgqpTyp6yPTZ+L3yCl7JDNz0wAUgGUBXA4/e5ynjYPdHHBGFMR9+AZ8wHpSXIThBBl0r8vC6A/aGgcAL4E8JIQonb6z/MLIXqn/2w5gGpCiEFCiKD0W2MhRE0ppR3ArwBeF0JY0uflB7vQrtpCiEghhEkIEQ7gQwAXABxS4vdmjOWMAzxjvuE2gPsAbBdCJIAC+34AEwBASrkEwLsAfhZCxKX/rEv6z24D6AhKrrsI4FL6Y0PSjz0WQHj6/d8D+C7ziYUQq4QQL+fQruIAFgCIA3ASQAUAD0opUzz9hRljueMkO8YYY8wHcQ+eMcYY80GKBHghxGwhxBUhxP5M9xVKL5xxLP3fgun3CyHEJ0KI40KIvUKIhkq0gTHGGGMZlOrBfw+gc5b7JgFYI6WsCmBN+vcAzftVTb+NAjBToTYwxhhjLJ0iAV5KuRG0rjaz7gDmpH89B0CPTPfPleQfAAWEECWVaAdjjDHGiJpz8MWllDHpX18CZdMCVFgjc9GM8+n3McYYY0whXil0I6WUQgiX0vWFEKNAQ/gICwtrVKNGDVXaxhjLQ3IycOQIkJICGGXVTUAA0KCB1q1gzCM7d+68JqUs6u7z1Qzwl4UQJaWUMelD8FfS778AqorlUCb9vrtIKWcBmAUAUVFRMjo6WsWmMsaytWsX0L49kJpqnODusGMHILhgHjMuIYRLZaGzUnOIfhkyKl4NBvBbpvsfT8+mbwogNtNQPmNML1asAFq2BG7eBNLStG6NawIDgdhYrVvBmKaUWiY3H8A2ANWFEOeFEMMBTAPQQQhxDLS71bT0h68EVbQ6DuBrAGOUaANjTEFffAH07g1YrVq3xD3BwcC1a1q3gjFNKTJEL6Xsn8OP2mXzWAngKSXOyxhTwfXrwPjxNPduVCYTBfgqVbRuCWOaMexucikpKTh//jwSExO1borfCA0NRZkyZRAUFKR1U5iabDYa4jZygJeSe/DM7xk2wJ8/fx4RERGoUKECBCfSqE5KievXr+P8+fOoWLGi1s1harLbjZ+cZrdzgGd+z7C16BMTE1G4cGEO7l4ihEDhwoV5xMQfpKUZL2M+q6QkDvDM7xk2wAPg4O5l/P/tJ0qVAsqVA0JC8n6sXqWmApcuad0KxjRl6ACvB+fPn0f37t1RtWpVVK5cGc8++yySk5Px/fffY+zYsU4fZ8aMGbC6mbG8fv16PPjgg249l7F7hITQGvKmTQGLRevWuO/CPeU1GPMrvhPgS5SgeUOlbiVK5HlKKSV69uyJHj164NixYzh69Cji4+MxefJkl5vvSYBnTHHh4cDffwO9ehk3yF+9qnULGNOU7wT4y5e9fry1a9ciNDQUQ4cOBQCYTCZMnz4ds2fPhtVqxblz59C6dWtUrVoVU6ZMAQAkJCSgW7duqF+/PurUqYMFCxbgk08+wcWLF9GmTRu0adMGAPDkk08iKioKtWvXxmuvvXbnnDt27EDz5s1Rv359NGnSBLdv376rTQkJCRg2bBiaNGmCBg0a4LfffgNjbgkMBL7/HnjxRWMFeSHoAuWVV7RuCWOaMmwWvR4cOHAAjRo1uuu+fPnyoVy5ckhNTcW///6L/fv3w2KxoHHjxujWrRvOnDmDUqVKYcWKFQCA2NhY5M+fHx999BHWrVuHIkWKAACmTp2KQoUKwW63o127dti7dy9q1KiBvn37YsGCBWjcuDHi4uJgNpvvOv/UqVPRtm1bzJ49G7du3UKTJk3Qvn17hIWFeec/hfkWIYDXXgMqVgRGj6YldHoWFAQULQqsXQtUr651axjTlO/04HWoQ4cOKFy4MMxmM3r27InNmzejbt26WL16NSZOnIhNmzYhf/782T534cKFaNiwIRo0aIADBw7g4MGDOHLkCEqWLInGjRsDoIuJwMC7r9H++usvTJs2DZGRkWjdujUSExNx9uxZ1X9X5uMef5xK14aHa92SnJnNQGQksHcvB3fGwAHeI7Vq1cLOnTvvui8uLg5nz55FYGDgPVnnQghUq1YNu3btQt26dfHKK6/gjTfeuOe4p06dwgcffIA1a9Zg79696Natm9PL06SU+OWXX7Bnzx7s2bMHZ8+eRc2aNd3/JRlzaNMG2LYNKFKEKsXpicUC9OwJbN4MFC6sdWsY0wUO8B5o164drFYr5s6dCwCw2+2YMGEChgwZAovFgtWrV+PGjRuw2WxYunQpWrRogYsXL8JiseCxxx7DCy+8gF27dgEAIiIi7synx8XFISwsDPnz58fly5exatUqAED16tURExODHTt2AABu376N1NTUu9rUqVMnfPrpp5Dp65h3797tlf8L5ifq1AH++w+oVEk/y+jMZuB//wN++IFq0DPGAHCA94gQAkuWLMGiRYtQtWpVVKtWDaGhoXj77bcBAE2aNMGjjz6KevXq4dFHH0VUVBT27duHJk2aIDIyElOmTMEr6YlAo0aNQufOndGmTRvUr18fDRo0QI0aNTBgwAC0aNECABAcHIwFCxbg6aefRv369dGhQ4d7evavvvoqUlJSUK9ePdSuXRuvvvqqd/9TmO8rVQrYuROIiqLgqiWLBfj5Z2DiRONX32NMYUIaoGJVdvvBHzp06O6h5xIllM2kL16cC2Vk457/d+a/UlJobn7ZMu/vOhcYCBQoQEv56tf37rkZ8xIhxE4pZZS7z/edHvylS1ReU6kbB3fGchcUBMybRzvPeXMZndkM1KgB7NvHwZ2xXPhOgGeMeZ8QwFtvAZ995p3heosF6NgR+Pdfp4pRMebPOMAzxjw3dCiwdCmgZr0FsxkYNw5YskT7uX/GDIAL3TDGAACHDx9Gt+49kZJlZUZWk154HmNGj7r3Bx07Alu2AG3bArGxtGWrUsxm4Ntvgf79lTsmYz6OAzxjDAAF+FsyFCFtRub4GOuRLdi0ZUv2AR6gOfE9e4AHHqDNXpKTPWuUyQRERAB//AHcd59nx2LMz3CAZ4zdERgShuAi5XL8edKFwwBu5X6QsmWB3buBzp1pzby75W1DQuhYa9bQ9rWMMZfwHDxjTHn58wMbNgAPPuhehr3FArRsSRcKHNyZWux2Gh2KjdW6JarwmQBfskw5CCEUu5Usk/uHyvjx4zFjxow733fq1AkjRoy48/2ECRPw0UcfYdmyZZg2bZpLv8uQIUOwePHibH82btw4bNy4EQAwfPhw1K9fH/Xq1UOvXr0QHx8PAHj99ddRunRpREZGokaNGnjyySeRlpYGgIZhIyMj0aBBA5w4cQLz5s27c+x9+/ZhyJAhLrWVsRwFBwMLFgBPP+1akLdYgBEj6INXz7XvmXGlpQELF9ImSt27A/360fJoH+MzQ/SXLpxD+YnLFTvemXcfzPXnLVq0wMKFCzFu3DikpaXh2rVriIuLu/PzrVu3Yvr06WjatCkefvhhRdp0/fp1/PPPP3cuLKZPn458+fIBAJ577jl89tlnmDRpEgC6AHn++eeRlpaGVq1aYcOGDWjTpg2WLl2KXr164ZVXXsH69esxb948DBgwAABQt25dnD9/HmfPnkU57jUxJQgBTJtGH6Tjx+c9XG82AzNmACNzzgNgzG1SAitX0mqMmBggIYHu37QJ+PprYFQOuSUG5TM9eG9r3rw5tm3bBoC2ja1Tpw4iIiJw8+ZNJCUl4dChQ2jYsCG+//57jB07FgD1zJ955hk0b94clSpVutNLl1Ji7NixqF69Otq3b48rV65ke85ffvkFnTt3vvO9I7hLKWGz2e7Z3AYAkpOTkZiYiIIFC2LlypWYMWMGZs6ciTZt2mDSpEnYtGkTIiMjMX36dADAQw89hJ9//lm5/yjGAOCJJ4DFi3PuyQtByXQrV3JwZ+qQEnj4YaBvX+D48YzgDtDX48cDR49q1z4VcIB3U6lSpRAYGIizZ89i69ataNasGe677z5s27YN0dHRqFu3LoKz2fgiJiYGmzdvxvLly+/0tpcsWYIjR47g4MGDmDt3LrZu3ZrtObds2XLP/vNDhw5FiRIlcPjwYTz99NNLine truncated
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
@@ -519,12 +470,12 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "97.28214880128124\n"
+      "186.09536925574923\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
@@ -538,12 +489,12 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "431.6330756470847\n"
+      "23898.229530915676\n"
     ]
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
@@ -557,25 +508,20 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "144.97608383310367\n",
-      "Error\n"
+      "63.479683711281005\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/pyrate/pyrate/plan/nearplanner/cost_functions.py:131: RuntimeWarning: overflow encountered in double_scalars\n",
+      "  grad = -cost * self.scale * self.safety_dist / dist**2 if dist > 0 else 0\n"
     ]
    },
    {
     "data": {
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-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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truncated
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
@@ -589,140 +535,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "534.5037544444888\n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
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-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
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-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
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-     "text": [
-      "274.2971367700994\n"
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-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
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-     "output_type": "display_data"
-    },
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "50510.53279699534\n"
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-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
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-     "text": [
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-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
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-     "data": {
-      "image/png": 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truncated
-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "59.99645908379837\n"
+      "16899.906925933017\n"
     ]
    }
   ],
@@ -796,7 +609,7 @@
    "\n",
    "\n",
    "# for seed in tqdm(range(0, 30, 1)):\n",
-    "for seed in tqdm(range(31, 50, 1)):\n",
+    "for seed in tqdm(range(150)):\n",
    "    wind_dir = 180\n",
    "    obstacles = generate_obstacles(seed)\n",
    "    destination = generate_destination(obstacles, seed)\n",
@@ -822,68 +635,6 @@
    "        print(route.cost)"
   ]
  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = [Point(73, 49), Point(70.2287350694941, 41.22346430945754), Point(70.22927795970757, 43.90628272112926)]\n",
-    "LineString(a).crosses(Point(70.9481331655341, 43.24219045432384))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_situation(\n",
-    "    obstacles=obstacles,\n",
-    "    destination=Point(70.9481331655341, 43.24219045432384),\n",
-    "    obstacle_color=\"RED\",\n",
-    "    route=TimingFrame(CartesianRoute.from_shapely(LineString(a))),\n",
-    "    title=f\"Seed: {seed}\",\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# result = snap(Point(70.9481331655341, 43.24219045432384), LineString(a), 0.5)\n",
-    "r = LineString(a)\n",
-    "c = Point(70.9481331655341, 43.24219045432384)\n",
-    "\n",
-    "### Create a buffer polygon around the interpolated point\n",
-    "buff = c.buffer(0.0001)\n",
-    "\n",
-    "### Split the line on the buffer\n",
-    "first_seg, _, last_seg = split(r, buff)\n",
-    "\n",
-    "### Stitch together the first segment, the interpolated point, and the last segment \n",
-    "r = LineString(list(first_seg.coords) + list(c.coords) + list(last_seg.coords))\n",
-    "\n",
-    "first_seg"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plot_situation(\n",
-    "    obstacles=obstacles,\n",
-    "    destination=Point(70.9481331655341, 43.24219045432384),\n",
-    "    obstacle_color=\"RED\",\n",
-    "    route=TimingFrame(CartesianRoute.from_shapely(result)),\n",
-    "    title=f\"Seed: {seed}\",\n",
-    ")"
-   ]
-  },
  {
   "cell_type": "code",
   "execution_count": null,
@@ -1085,100 +836,6 @@
   "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"
   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from shapely.wkt import loads\n",
-    "from shapely.wkt import loads\n",
-    "from shapely.validation import explain_validity\n",
-    "from shapely.geometry.polygon import LinearRing\n",
-    "\n",
-    "l = loads(\n",
-    "    \"LINESTRING (9603380.577551289 2719693.31939431, 9602238.01822002 2719133.882441244, 9601011.900844947 2718804.012436028, 9599670.800095448 2718931.680117098, 9599567.204161201 2717889.384686942, 9600852.184025297 2721120.409265322, 9599710.80929024 2720511.270897166, 9602777.832940497 2718125.875545334)\"\n",
-    ")\n",
-    "\n",
-    "print(l.is_simple)  # False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ring = LinearRing(list(l.coords))\n",
-    "\n",
-    "print(ring.is_simple)  # False\n",
-    "print(\n",
-    "    explain_validity(ring)\n",
-    ")  # Ring Self-intersection[9600397.5202531 2719977.17748099]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a, v = list((1,2,3))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from shapely.ops import snap\n",
-    "\n",
-    "square = Polygon([(1,1), (2, 1), (2, 2), (1, 2), (1, 1)])\n",
-    "\n",
-    "line = LineString([(0,0), (0.8, 0.8), (1.8, 0.95), (2.6, 0.5)])\n",
-    "\n",
-    "result = snap(line, square, 0.5)\n",
-    "\n",
-    "result.wkt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(square, line)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "line"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
  }
 ],
 "metadata": {