DataAnalysis_csv.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T14:20:49.173276Z",
     "start_time": "2020-10-30T14:20:48.979959Z"
    }
   },
   "outputs": [],
   "source": [
    "# !mkdir ToGIF\n",
    "# !mkdir GIF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T13:37:36.348138Z",
     "start_time": "2020-10-30T13:37:26.509304Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e93b63faf52c42bb982559941c650393",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "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": [
    "# import csv\n",
    "# from tqdm.notebook import tqdm\n",
    "\n",
    "# index = 0\n",
    "\n",
    "# root_dir = \"data/argoverse_raw/train/\"\n",
    "# file_list = os.listdir(root_dir)\n",
    "# num_figname = 1\n",
    "\n",
    "# color = {\"AGENT\" : \"blue\", \"AV\" : \"orange\", \"OTHERS\" : \"gray\"}\n",
    "\n",
    "# trajectory = {}\n",
    "# trajectory[\"AV\"] = np.empty((0,2))\n",
    "# trajectory[\"AGENT\"] = np.empty((0,2))\n",
    "# trajectory[\"OTHERS\"] = np.empty((0,2))\n",
    "\n",
    "# with open(root_dir + file_list[index], newline='') as csvfile:\n",
    "#     spamreader = csv.reader(csvfile, delimiter=',', quotechar='|')\n",
    "#     time_step = -1\n",
    "#     for i, row in tqdm(enumerate(spamreader)):\n",
    "#         if i >0:\n",
    "#             if time_step != row[0]:\n",
    "#                 time_step = row[0]\n",
    "#                 try:\n",
    "#                     plt.plot(trajectory[\"AV\"][...,0],trajectory[\"AV\"][...,1],color=color[\"AV\"], zorder=1)\n",
    "#                     plt.plot(trajectory[\"AV\"][...,0],trajectory[\"AV\"][...,1],color=color[\"AGENT\"],zorder=1)\n",
    "# #                     print(\"AGENT: \", trajectory[\"AGENT\"])\n",
    "# #                     print(\"AV: \", trajectory[\"AV\"])\n",
    "#                 except:\n",
    "# #                     print(\"pass\")\n",
    "# #                     print(trajectory[\"AGENT\"])\n",
    "\n",
    "#                     pass\n",
    "# #                 plt.legend()\n",
    "#                 plt.savefig(\"ToGIF/\" + str(num_figname).zfill(3) + \".png\")\n",
    "#                 num_figname +=1\n",
    "#                 plt.cla()\n",
    "            \n",
    "#             plt.scatter(float(row[3]), float(row[4]), color = color[row[2]], label = row[2])\n",
    "#             trajectory[row[2]] = np.append(trajectory[row[2]],np.array([[float(row[3]), float(row[4])]]), axis=0)\n",
    "# #             if num_figname == 20:\n",
    "\n",
    "# #                 break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T20:25:02.951813Z",
     "start_time": "2020-11-02T20:24:56.752137Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import imageio\n",
    "from PIL import Image\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "index = 0\n",
    "\n",
    "root_dir = \"data/argoverse_raw/test/\"\n",
    "file_list = sorted(os.listdir(root_dir), key = lambda a: int(a.split('.')[0]))\n",
    "\n",
    "color = {\"AGENT\" : \"blue\", \"AV\" : \"orange\", \"OTHERS\" : \"gray\"}\n",
    "\n",
    "\n",
    "from argoverse.map_representation.map_api import ArgoverseMap\n",
    "\n",
    "am = ArgoverseMap()\n",
    "\n",
    "csv = pd.read_csv(root_dir + file_list[index])\n",
    "xmax,xmin = max(csv[\"X\"]), min(csv[\"X\"])\n",
    "ymax,ymin = max(csv[\"Y\"]), min(csv[\"Y\"])\n",
    "local_lane_polygons = am.find_local_lane_polygons([xmin, xmax, ymin, ymax], csv[\"CITY_NAME\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-02T20:25:02.955883Z",
     "start_time": "2020-11-02T20:25:02.953088Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "ans = 0\n",
    "for i in csv['OBJECT_TYPE']:\n",
    "    if i == \"AV\":\n",
    "        ans +=1\n",
    "        \n",
    "print(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-01T18:51:37.802334Z",
     "start_time": "2020-11-01T18:51:37.651795Z"
    }
   },
   "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": [
    "for l in local_lane_polygons:\n",
    "    x_l = l[...,0]\n",
    "    y_l = l[...,1]\n",
    "    plt.plot(x_l,y_l,linewidth=0.5, color = 'gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T18:20:11.036337Z",
     "start_time": "2020-10-30T18:11:26.165569Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "482b94ef2de34540b9ceff9b20ad3e98",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=337.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "./GIF/0_lane.gif is saved\n",
      "60\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2482d1e663964ed19d3a7b418047f480",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=647.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "./GIF/1_lane.gif is saved\n",
      "35\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38ca495e74854f348ce11e19588773f5",
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      "\n",
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      "\n",
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      "39\n"
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      "\n",
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      "\n",
      "./GIF/48_lane.gif is saved\n",
      "126\n"
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      "\n",
      "./GIF/49_lane.gif is saved\n"
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   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import imageio\n",
    "from PIL import Image\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "index = 0\n",
    "\n",
    "root_dir = \"argoverse_raw/train/\"\n",
    "file_list = sorted(os.listdir(root_dir), key = lambda a: int(a.split('.')[0]))\n",
    "\n",
    "color = {\"AGENT\" : \"blue\", \"AV\" : \"orange\", \"OTHERS\" : \"gray\"}\n",
    "\n",
    "\n",
    "from argoverse.map_representation.map_api import ArgoverseMap\n",
    "\n",
    "am = ArgoverseMap()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "for index in range(50):\n",
    "#     print(index)\n",
    "    trajectory = {}\n",
    "    trajectory[\"AV\"] = np.empty((0,2))\n",
    "    trajectory[\"AGENT\"] = np.empty((0,2))\n",
    "    trajectory[\"OTHERS\"] = np.empty((0,2))\n",
    "    num_figname = 1\n",
    "\n",
    "    csv = pd.read_csv(root_dir + file_list[index])\n",
    "\n",
    "    xmax,xmin = max(csv[\"X\"]), min(csv[\"X\"])\n",
    "    ymax,ymin = max(csv[\"Y\"]), min(csv[\"Y\"])\n",
    "    local_lane_polygons = am.find_local_lane_polygons([xmin, xmax, ymin, ymax], csv[\"CITY_NAME\"][0])\n",
    "    print(len(local_lane_polygons))\n",
    "    plt.xlim(xmin-20, xmax + 20)\n",
    "    plt.ylim(ymin-20, ymax + 20)\n",
    "\n",
    "    time_step = csv[\"TIMESTAMP\"][0]\n",
    "    X = csv[\"OBJECT_TYPE\"]\n",
    "    Y = csv[\"Y\"]\n",
    "\n",
    "    length = len(csv[\"TIMESTAMP\"])\n",
    "\n",
    "    for i in tqdm(range(length)):\n",
    "        x,y,OT = csv[\"X\"][i], csv[\"Y\"][i], csv[\"OBJECT_TYPE\"][i]\n",
    "        if time_step != csv[\"TIMESTAMP\"][i]:\n",
    "            for l in local_lane_polygons:\n",
    "                x_l = l[...,0]\n",
    "                y_l = l[...,1]\n",
    "                plt.plot(x_l,y_l,linewidth=0.5, color = 'gray')\n",
    "            try:\n",
    "                for j in [\"AV\", \"AGENT\"]:\n",
    "                    plt.plot(trajectory[j][...,0],trajectory[j][...,1],color=color[j], zorder=1)\n",
    "                    plt.scatter(trajectory[j][...,0][-1], trajectory[j][...,1][-1],color=color[j], zorder=1, label = j)\n",
    "\n",
    "                plt.scatter(trajectory[\"OTHERS\"][...,0], trajectory[\"OTHERS\"][...,1], color = color[\"OTHERS\"], label = \"OTHERS\")\n",
    "                trajectory[\"OTHERS\"] = np.empty((0,2))\n",
    "\n",
    "            except:\n",
    "                pass\n",
    "                print(\"pass\")\n",
    "                break\n",
    "            plt.legend()\n",
    "            plt.title(\"time_step: %.4f\"%time_step )\n",
    "\n",
    "            plt.savefig(\"ToGIF/\" + str(num_figname).zfill(3) + \".png\")\n",
    "            num_figname +=1\n",
    "            plt.cla()\n",
    "            plt.xlim(xmin-20, xmax + 20)\n",
    "            plt.ylim(ymin-20, ymax + 20)\n",
    "            time_step = csv[\"TIMESTAMP\"][i]\n",
    "\n",
    "        elif i == length - 1:\n",
    "            plt.scatter(x, y, color = color[OT], label = OT)\n",
    "            trajectory[OT] = np.append(trajectory[OT],np.array([[x, y]]), axis=0)\n",
    "            for l in local_lane_polygons:\n",
    "                x_l = l[...,0]\n",
    "                y_l = l[...,1]\n",
    "                plt.plot(x_l,y_l,linewidth=0.5, color = 'gray')\n",
    "            try:\n",
    "                for j in [\"AV\", \"AGENT\"]:\n",
    "                    plt.plot(trajectory[j][...,0],trajectory[j][...,1],color=color[j], zorder=1, )\n",
    "                    plt.scatter(trajectory[j][...,0][-1], trajectory[j][...,1][-1],color=color[j], zorder=1, label = j)\n",
    "\n",
    "                plt.scatter(trajectory[\"OTHERS\"][...,0], trajectory[\"OTHERS\"][...,1], color = color[\"OTHERS\"], label = \"OTHERS\")\n",
    "                trajectory[\"OTHERS\"] = np.empty((0,2))\n",
    "                trajectory[\"AV\"] = np.empty((0,2))\n",
    "                trajectory[\"AGENT\"] = np.empty((0,2))\n",
    "            except:\n",
    "                pass\n",
    "                print(\"pass\")\n",
    "                break\n",
    "            plt.legend()\n",
    "            plt.title(\"time_step: %.4f\"%time_step )\n",
    "            plt.savefig(\"ToGIF/\" + str(num_figname).zfill(3) + \".png\")\n",
    "            plt.close()\n",
    "            break\n",
    "        trajectory[OT] = np.append(trajectory[OT],np.array([[x, y]]), axis=0)\n",
    "\n",
    "\n",
    "\n",
    "    path = [f\"./ToGIF/{j}\" for j in sorted(os.listdir(\"./ToGIF\"), key= lambda a: int(a.split('.')[0]))]\n",
    "    paths = [Image.open(j) for j in path]\n",
    "    imageio.mimsave('./GIF/' + str(index) + '_lane.gif', paths, fps=22)\n",
    "    print('./GIF/' + str(index) + '_lane.gif is saved')\n",
    "    !rm ToGIF/*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T19:42:31.816473Z",
     "start_time": "2020-10-30T19:42:26.988708Z"
    },
    "scrolled": true
   },
   "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": [
    "local_lane_polygons = am.find_local_lane_polygons([xmin, xmax, ymin, ymax], csv[\"CITY_NAME\"][0])\n",
    "for l in local_lane_polygons:\n",
    "    x_l = l[...,0]\n",
    "    y_l = l[...,1]\n",
    "    plt.plot(x_l,y_l,linewidth=0.5, color = 'gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T19:42:15.120238Z",
     "start_time": "2020-10-30T19:42:15.116253Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[640.5452197174783, 715.5208021523538, 3802.248276968051, 4003.0806010870797]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[xmin, xmax, ymin, ymax]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T19:41:53.570436Z",
     "start_time": "2020-10-30T19:41:53.566907Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_lane_polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T17:43:56.830839Z",
     "start_time": "2020-10-30T17:43:50.618356Z"
    }
   },
   "outputs": [],
   "source": [
    "tracking_dataset_dir = '../argoverse-api/argoverse-tracking/sample/'\n",
    "\n",
    "from argoverse.map_representation.map_api import ArgoverseMap\n",
    "\n",
    "am = ArgoverseMap()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T17:43:56.904684Z",
     "start_time": "2020-10-30T17:43:56.832356Z"
    }
   },
   "outputs": [],
   "source": [
    "from argoverse.data_loading.argoverse_tracking_loader import ArgoverseTrackingLoader\n",
    "\n",
    "log_index = 0\n",
    "frame_index = 20\n",
    "idx = 1\n",
    "argoverse_loader = ArgoverseTrackingLoader(tracking_dataset_dir)\n",
    "log_id = argoverse_loader.log_list[log_index]\n",
    "argoverse_data = argoverse_loader[log_index]\n",
    "city_name = argoverse_data.city_name\n",
    "\n",
    "lidar_pts = argoverse_data.get_lidar(idx)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T17:46:16.388368Z",
     "start_time": "2020-10-30T17:46:16.119924Z"
    }
   },
   "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": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm.notebook import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import imageio\n",
    "from PIL import Image\n",
    "import matplotlib.image as mpimg\n",
    "\n",
    "index = 0\n",
    "\n",
    "root_dir = \"data/argoverse_raw/train/\"\n",
    "file_list = sorted(os.listdir(root_dir), key = lambda a: int(a.split('.')[0]))\n",
    "\n",
    "color = {\"AGENT\" : \"blue\", \"AV\" : \"orange\", \"OTHERS\" : \"gray\"}\n",
    "\n",
    "\n",
    "trajectory = {}\n",
    "trajectory[\"AV\"] = np.empty((0,2))\n",
    "trajectory[\"AGENT\"] = np.empty((0,2))\n",
    "trajectory[\"OTHERS\"] = np.empty((0,2))\n",
    "num_figname = 1\n",
    "\n",
    "csv = pd.read_csv(root_dir + file_list[index])\n",
    "\n",
    "xmax,xmin = max(csv[\"X\"]), min(csv[\"X\"])\n",
    "ymax,ymin = max(csv[\"Y\"]), min(csv[\"Y\"])\n",
    "\n",
    "plt.xlim(xmin-20, xmax + 20)\n",
    "plt.ylim(ymin-20, ymax + 20)\n",
    "\n",
    "time_step = csv[\"TIMESTAMP\"][0]\n",
    "X = csv[\"OBJECT_TYPE\"]\n",
    "Y = csv[\"Y\"]\n",
    "\n",
    "length = len(csv[\"TIMESTAMP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T17:46:22.694558Z",
     "start_time": "2020-10-30T17:46:22.680234Z"
    }
   },
   "outputs": [],
   "source": [
    "local_lane_polygons = am.find_local_lane_polygons([xmin, xmax, ymin, ymax], csv[\"CITY_NAME\"][0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T19:49:44.910147Z",
     "start_time": "2020-10-30T19:49:44.419953Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T20:16:07.875884Z",
     "start_time": "2020-10-30T20:16:07.869813Z"
    }
   },
   "outputs": [],
   "source": [
    "adjacency = torch.rand(10,10)\n",
    "num_agent_mask1 = torch.Tensor([[1,1,0.3,1,1]])\n",
    "num_agent_mask2 = torch.Tensor([[1,1,0.8,1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T20:21:48.057967Z",
     "start_time": "2020-10-30T20:21:48.054484Z"
    }
   },
   "outputs": [],
   "source": [
    "num_agent_mask = []\n",
    "for i in range(11):\n",
    "    num_agent_mask.append(torch.rand(1,11))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T20:21:48.261766Z",
     "start_time": "2020-10-30T20:21:48.252418Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_agent_mask 0:  tensor([[0.1013, 0.4769, 0.3640, 0.6623, 0.8702, 0.9721, 0.5173, 0.7390, 0.5709,\n",
      "         0.3192, 0.5662]])\n",
      "num_agent_mask 1:  tensor([[0.8349, 0.8712, 0.5421, 0.3144, 0.8351, 0.7285, 0.3044, 0.4971, 0.0654,\n",
      "         0.0495, 0.3548]])\n",
      "num_agent_mask 2:  tensor([[0.0880, 0.4524, 0.1552, 0.3093, 0.3516, 0.5038, 0.3321, 0.5478, 0.8967,\n",
      "         0.4128, 0.8510]])\n",
      "num_agent_mask 3:  tensor([[0.9656, 0.2808, 0.6689, 0.0536, 0.1490, 0.8947, 0.6106, 0.1925, 0.2094,\n",
      "         0.7012, 0.7778]])\n",
      "num_agent_mask 4:  tensor([[0.8585, 0.4115, 0.5463, 0.9295, 0.5451, 0.0750, 0.4689, 0.0067, 0.6010,\n",
      "         0.0909, 0.8923]])\n",
      "num_agent_mask 5:  tensor([[0.5660, 0.2109, 0.7820, 0.1541, 0.0398, 0.9452, 0.9394, 0.2608, 0.6562,\n",
      "         0.9395, 0.4706]])\n",
      "num_agent_mask 6:  tensor([[0.4320, 0.5874, 0.2214, 0.4471, 0.2218, 0.9894, 0.4767, 0.5168, 0.3923,\n",
      "         0.7884, 0.1844]])\n",
      "num_agent_mask 7:  tensor([[0.3965, 0.7434, 0.9210, 0.7469, 0.5152, 0.3394, 0.6785, 0.5253, 0.6807,\n",
      "         0.4082, 0.0179]])\n",
      "num_agent_mask 8:  tensor([[0.5948, 0.6417, 0.7088, 0.1641, 0.3146, 0.4157, 0.5128, 0.7927, 0.4017,\n",
      "         0.0665, 0.8086]])\n",
      "num_agent_mask 9:  tensor([[0.3335, 0.6212, 0.7437, 0.6118, 0.4929, 0.1271, 0.5978, 0.2348, 0.6827,\n",
      "         0.2247, 0.7201]])\n",
      "num_agent_mask 10:  tensor([[0.0294, 0.7123, 0.3915, 0.8241, 0.9953, 0.7797, 0.6366, 0.4018, 0.1594,\n",
      "         0.2440, 0.2143]])\n"
     ]
    }
   ],
   "source": [
    "num = 0\n",
    "for i in num_agent_mask:\n",
    "    print(\"num_agent_mask \" + str(num) + \": \", i)\n",
    "    num+=1\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T19:50:12.450351Z",
     "start_time": "2020-10-30T19:50:12.420297Z"
    }
   },
   "source": [
    "adjacency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T20:24:44.922274Z",
     "start_time": "2020-10-30T20:24:44.917249Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2.0340e-01, 9.8018e-01, 5.9276e-01, 5.6608e-01, 5.2921e-01, 7.3613e-01,\n",
       "         5.3161e-01, 6.1163e-01, 6.8068e-01, 5.5828e-01],\n",
       "        [6.0616e-01, 1.5218e-01, 9.9071e-02, 7.9985e-01, 5.1474e-01, 8.1126e-01,\n",
       "         9.6187e-01, 7.1541e-01, 4.0373e-01, 4.1584e-03],\n",
       "        [6.1968e-02, 5.8127e-01, 8.4022e-01, 9.5129e-01, 9.5905e-01, 4.6290e-01,\n",
       "         7.3913e-01, 6.0182e-01, 2.7469e-01, 1.7079e-01],\n",
       "        [3.7596e-01, 6.2782e-01, 1.2419e-01, 8.6973e-01, 2.5734e-01, 6.7242e-01,\n",
       "         8.9878e-01, 6.7018e-01, 1.9631e-01, 6.0446e-02],\n",
       "        [8.5386e-01, 5.0087e-01, 9.7318e-01, 9.5431e-01, 2.6323e-01, 5.6121e-01,\n",
       "         9.2826e-01, 9.0787e-01, 5.3491e-01, 2.7087e-02],\n",
       "        [1.6519e-01, 3.2959e-01, 9.0574e-01, 2.5885e-01, 2.5194e-01, 7.7683e-01,\n",
       "         9.8949e-01, 3.7601e-01, 9.9803e-01, 9.7052e-01],\n",
       "        [2.8935e-01, 7.8965e-01, 3.9247e-01, 5.8408e-01, 2.5208e-01, 5.4293e-01,\n",
       "         2.9844e-01, 2.0673e-01, 8.9610e-01, 2.2326e-01],\n",
       "        [3.5123e-01, 3.0286e-01, 5.4722e-01, 5.8823e-01, 6.8265e-01, 8.8148e-01,\n",
       "         2.2888e-01, 3.8877e-01, 5.6708e-01, 4.5865e-01],\n",
       "        [5.6058e-04, 8.9987e-02, 3.0183e-01, 2.1870e-01, 8.1464e-01, 7.8555e-01,\n",
       "         3.8133e-01, 5.3683e-01, 7.2945e-01, 7.5747e-01],\n",
       "        [5.4396e-01, 3.6374e-01, 4.8582e-01, 9.5815e-01, 8.9400e-02, 5.3166e-01,\n",
       "         6.7496e-03, 4.5861e-01, 5.8372e-01, 5.0994e-01]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjacency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T20:16:09.924443Z",
     "start_time": "2020-10-30T20:16:09.919963Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1.0000, 1.0000, 0.3000, 1.0000, 1.0000],\n",
       "        [1.0000, 1.0000, 0.8000, 1.0000, 1.0000]])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cat((num_agent_mask1,num_agent_mask2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T20:06:59.414209Z",
     "start_time": "2020-10-30T20:06:59.407167Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adjacency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-10-30T20:21:54.509963Z",
     "start_time": "2020-10-30T20:21:54.504185Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.1013, 0.4769, 0.3640, 0.6623, 0.8702, 0.9721, 0.5173, 0.7390, 0.5709,\n",
       "         0.3192, 0.5662],\n",
       "        [0.8349, 0.8712, 0.5421, 0.3144, 0.8351, 0.7285, 0.3044, 0.4971, 0.0654,\n",
       "         0.0495, 0.3548],\n",
       "        [0.0880, 0.4524, 0.1552, 0.3093, 0.3516, 0.5038, 0.3321, 0.5478, 0.8967,\n",
       "         0.4128, 0.8510],\n",
       "        [0.9656, 0.2808, 0.6689, 0.0536, 0.1490, 0.8947, 0.6106, 0.1925, 0.2094,\n",
       "         0.7012, 0.7778],\n",
       "        [0.8585, 0.4115, 0.5463, 0.9295, 0.5451, 0.0750, 0.4689, 0.0067, 0.6010,\n",
       "         0.0909, 0.8923],\n",
       "        [0.5660, 0.2109, 0.7820, 0.1541, 0.0398, 0.9452, 0.9394, 0.2608, 0.6562,\n",
       "         0.9395, 0.4706],\n",
       "        [0.4320, 0.5874, 0.2214, 0.4471, 0.2218, 0.9894, 0.4767, 0.5168, 0.3923,\n",
       "         0.7884, 0.1844],\n",
       "        [0.3965, 0.7434, 0.9210, 0.7469, 0.5152, 0.3394, 0.6785, 0.5253, 0.6807,\n",
       "         0.4082, 0.0179],\n",
       "        [0.5948, 0.6417, 0.7088, 0.1641, 0.3146, 0.4157, 0.5128, 0.7927, 0.4017,\n",
       "         0.0665, 0.8086],\n",
       "        [0.3335, 0.6212, 0.7437, 0.6118, 0.4929, 0.1271, 0.5978, 0.2348, 0.6827,\n",
       "         0.2247, 0.7201],\n",
       "        [0.0294, 0.7123, 0.3915, 0.8241, 0.9953, 0.7797, 0.6366, 0.4018, 0.1594,\n",
       "         0.2440, 0.2143]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cat(num_agent_mask,dim =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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