{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pickle \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import random\n",
    "import os\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# collect[-1]['Observation'] = sstgcnn_['obs'][key]\n",
    "# collect[-1]['GroundTruth'] = sstgcnn_['tarj'][key]\n",
    "# collect[-1]['S-GAN'] = sgan_['pred'][key]\n",
    "# collect[-1]['S-STGCNN'] = sstgcnn_['pred'][key]\n",
    "# collect[-1]['S-Implicit'] = simplicit_['pred'][key]\n",
    "# collect[-1]['Trajectron++'] = trajpp_['pred'][key]\n",
    "# collect[-1]['ExpertTraj'] = expertraj_['pred'][key]\n",
    "dset = 'eth' #change dest to 'eth', 'zara1', 'hotel' \n",
    "with open('./aligned/vis_'+dset+'.pkl', 'rb') as f:\n",
    "    data = pickle.load(f)\n",
    "output_dir= './' + dset + 'VisZoomed'\n",
    "if not os.path.exists(output_dir):\n",
    "  # Create a new directory because it does not exist \n",
    "  os.makedirs(output_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eth: 0\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "kdeplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 2 keyword-only arguments) were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 29\u001b[0m\n\u001b[1;32m     24\u001b[0m         ax[i]\u001b[39m.\u001b[39mplot(data[key][\u001b[39m'\u001b[39m\u001b[39mObservation\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m0\u001b[39m],\n\u001b[1;32m     25\u001b[0m                    data[key][\u001b[39m'\u001b[39m\u001b[39mObservation\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m1\u001b[39m], \u001b[39m'\u001b[39m\u001b[39m--o\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m     26\u001b[0m         ax[i]\u001b[39m.\u001b[39mplot(data[key][\u001b[39m'\u001b[39m\u001b[39mGroundTruth\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m0\u001b[39m],\n\u001b[1;32m     27\u001b[0m                    data[key][\u001b[39m'\u001b[39m\u001b[39mGroundTruth\u001b[39m\u001b[39m'\u001b[39m][\u001b[39m1\u001b[39m], \u001b[39m'\u001b[39m\u001b[39m--x\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m---> 29\u001b[0m sns\u001b[39m.\u001b[39;49mkdeplot(data[key][\u001b[39m'\u001b[39;49m\u001b[39mS-Implicit\u001b[39;49m\u001b[39m'\u001b[39;49m][:, \u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mreshape(\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m),\n\u001b[1;32m     30\u001b[0m         data[key][\u001b[39m'\u001b[39;49m\u001b[39mS-Implicit\u001b[39;49m\u001b[39m'\u001b[39;49m][:, \u001b[39m1\u001b[39;49m]\u001b[39m.\u001b[39;49mreshape(\u001b[39m-\u001b[39;49m\u001b[39m1\u001b[39;49m),\n\u001b[1;32m     31\u001b[0m         shade\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m     32\u001b[0m         shade_lowest\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m     33\u001b[0m         color\u001b[39m=\u001b[39;49mcmap[\u001b[39m0\u001b[39;49m],\n\u001b[1;32m     34\u001b[0m         alpha\u001b[39m=\u001b[39;49m\u001b[39m0.8\u001b[39;49m,\n\u001b[1;32m     35\u001b[0m         label\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mS-Implicit\u001b[39;49m\u001b[39m'\u001b[39;49m,\n\u001b[1;32m     36\u001b[0m         ax\u001b[39m=\u001b[39;49max[\u001b[39m0\u001b[39;49m])\n\u001b[1;32m     38\u001b[0m sns\u001b[39m.\u001b[39mkdeplot(data[key][\u001b[39m'\u001b[39m\u001b[39mS-GAN\u001b[39m\u001b[39m'\u001b[39m][:, \u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m),\n\u001b[1;32m     39\u001b[0m         data[key][\u001b[39m'\u001b[39m\u001b[39mS-GAN\u001b[39m\u001b[39m'\u001b[39m][:, \u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m),\n\u001b[1;32m     40\u001b[0m         shade\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     44\u001b[0m         label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mS-GAN\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m     45\u001b[0m         ax\u001b[39m=\u001b[39max[\u001b[39m1\u001b[39m])\n\u001b[1;32m     46\u001b[0m sns\u001b[39m.\u001b[39mkdeplot(data[key][\u001b[39m'\u001b[39m\u001b[39mS-STGCNN\u001b[39m\u001b[39m'\u001b[39m][:, \u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m),\n\u001b[1;32m     47\u001b[0m         data[key][\u001b[39m'\u001b[39m\u001b[39mS-STGCNN\u001b[39m\u001b[39m'\u001b[39m][:, \u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m),\n\u001b[1;32m     48\u001b[0m         shade\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     52\u001b[0m         label\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mS-STGCNN\u001b[39m\u001b[39m'\u001b[39m,\n\u001b[1;32m     53\u001b[0m         ax\u001b[39m=\u001b[39max[\u001b[39m2\u001b[39m])\n",
      "\u001b[0;31mTypeError\u001b[0m: kdeplot() takes from 0 to 1 positional arguments but 2 positional arguments (and 2 keyword-only arguments) were given"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = ['g', 'y', 'b', 'k', 'r']\n",
    "\n",
    "indices= {}\n",
    "indices['eth'] = list(range(200))\n",
    "# indices['eth'] = [0, 95]\n",
    "# indices['hotel'] = [419,701]\n",
    "# indices['zara1'] = [2031,315,1290]\n",
    "\n",
    "\n",
    "for key in indices[dset]:\n",
    "    print('{}: {}'.format(dset, key))\n",
    "    fig, ax = plt.subplots(1, 5, figsize=(20, 5))  #,sharex=True, sharey=True)\n",
    "    for i in range(5):\n",
    "        if i == 0:\n",
    "            ax[i].plot(data[key]['Observation'][0],\n",
    "                       data[key]['Observation'][1],\n",
    "                       '--o',\n",
    "                       label='Observation')\n",
    "            ax[i].plot(data[key]['GroundTruth'][0],\n",
    "                       data[key]['GroundTruth'][1],\n",
    "                       '--x',\n",
    "                       label='GroundTruth')\n",
    "        else:\n",
    "            ax[i].plot(data[key]['Observation'][0],\n",
    "                       data[key]['Observation'][1], '--o')\n",
    "            ax[i].plot(data[key]['GroundTruth'][0],\n",
    "                       data[key]['GroundTruth'][1], '--x')\n",
    "\n",
    "    sns.kdeplot(data[key]['S-Implicit'][:, 0].reshape(-1),\n",
    "            data[key]['S-Implicit'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=cmap[0],\n",
    "            alpha=0.8,\n",
    "            label='S-Implicit',\n",
    "            ax=ax[0])\n",
    "\n",
    "    sns.kdeplot(data[key]['S-GAN'][:, 0].reshape(-1),\n",
    "            data[key]['S-GAN'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=cmap[1],\n",
    "            alpha=0.8,\n",
    "            label='S-GAN',\n",
    "            ax=ax[1])\n",
    "    sns.kdeplot(data[key]['S-STGCNN'][:, 0].reshape(-1),\n",
    "            data[key]['S-STGCNN'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=cmap[2],\n",
    "            alpha=0.8,\n",
    "            label='S-STGCNN',\n",
    "            ax=ax[2])\n",
    "    sns.kdeplot(data[key]['Trajectron++'][:, 0].reshape(-1),\n",
    "            data[key]['Trajectron++'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=cmap[3],\n",
    "            alpha=0.8,\n",
    "            label='Trajectron++',\n",
    "            ax=ax[3])\n",
    "    sns.kdeplot(data[key]['ExpertTraj'][:, 0].reshape(-1),\n",
    "            data[key]['ExpertTraj'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=cmap[4],\n",
    "            alpha=0.8,\n",
    "            label='ExpertTraj',\n",
    "            ax=ax[4])\n",
    "\n",
    "    x_limit = ax[0].get_xlim()\n",
    "    y_limit = ax[0].get_ylim()\n",
    "    for i in range(5):\n",
    "        ax[i].xaxis.set_tick_params(labelsize=14)\n",
    "        ax[i].yaxis.set_tick_params(labelsize=14)\n",
    "        ax[i].set_xlim(x_limit)\n",
    "        ax[i].set_ylim(y_limit)\n",
    "\n",
    "    lines_labels = [ax.get_legend_handles_labels() for ax in fig.axes]\n",
    "    lines, labels = [sum(lol, []) for lol in zip(*lines_labels)]\n",
    "    fig.legend(lines,\n",
    "           labels,\n",
    "           loc=\"upper center\",\n",
    "           ncol=7,\n",
    "           fontsize=15,\n",
    "           bbox_to_anchor=(0.5, 1.1))\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(output_dir+ '/case_' + dset + '_' + str(key) + '.png',\n",
    "            dpi=300,\n",
    "            bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
