{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "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": {
    "tags": []
   },
   "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": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "color_map = [(1., 0.83, 0),(0.494, 0.184, 0.556)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eth: 0\n",
      "(1200,)\n",
      "(1200,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_820237/2311059677.py:32: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-Implicit'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:32: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-Implicit'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:41: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-GAN'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:41: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-GAN'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:49: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-STGCNN'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:49: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-STGCNN'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:57: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['Trajectron++'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:57: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['Trajectron++'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:65: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['ExpertTraj'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:65: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['ExpertTraj'][:, 0].reshape(-1),\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eth: 1\n",
      "(1200,)\n",
      "(1200,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_820237/2311059677.py:32: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-Implicit'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:32: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-Implicit'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:41: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-GAN'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:41: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-GAN'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:49: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-STGCNN'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:49: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['S-STGCNN'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:57: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['Trajectron++'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:57: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['Trajectron++'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:65: UserWarning: \n",
      "\n",
      "`shade_lowest` has been replaced by `thresh`; setting `thresh=0.05.\n",
      "This will become an error in seaborn v0.13.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['ExpertTraj'][:, 0].reshape(-1),\n",
      "/tmp/ipykernel_820237/2311059677.py:65: FutureWarning: \n",
      "\n",
      "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n",
      "This will become an error in seaborn v0.14.0; please update your code.\n",
      "\n",
      "  sns.kdeplot(x = data[key]['ExpertTraj'][:, 0].reshape(-1),\n",
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    },
    {
     "data": {
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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",
    "    print(np.array(data[key]['S-Implicit'][:, 0].reshape(-1)).shape)\n",
    "    print(np.array(data[key]['S-Implicit'][:, 1].reshape(-1)).shape)\n",
    "    \n",
    "    sns.kdeplot(x = data[key]['S-Implicit'][:, 0].reshape(-1),\n",
    "            y = data[key]['S-Implicit'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=color_map[0],\n",
    "            alpha=0.8,\n",
    "            label='S-Implicit',\n",
    "            ax=ax[0])\n",
    "\n",
    "    sns.kdeplot(x = data[key]['S-GAN'][:, 0].reshape(-1),\n",
    "            y = data[key]['S-GAN'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=color_map[1],\n",
    "            alpha=0.8,\n",
    "            label='S-GAN',\n",
    "            ax=ax[1])\n",
    "    sns.kdeplot(x = data[key]['S-STGCNN'][:, 0].reshape(-1),\n",
    "            y = data[key]['S-STGCNN'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=color_map[0],\n",
    "            alpha=0.8,\n",
    "            label='S-STGCNN',\n",
    "            ax=ax[2])\n",
    "    sns.kdeplot(x = data[key]['Trajectron++'][:, 0].reshape(-1),\n",
    "            y = data[key]['Trajectron++'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=color_map[1],\n",
    "            alpha=0.8,\n",
    "            label='Trajectron++',\n",
    "            ax=ax[3])\n",
    "    sns.kdeplot(x = data[key]['ExpertTraj'][:, 0].reshape(-1),\n",
    "            y = data[key]['ExpertTraj'][:, 1].reshape(-1),\n",
    "            shade=True,\n",
    "            shade_lowest=False,\n",
    "            color=color_map[0],\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": "scit",
   "language": "python",
   "name": "scit"
  },
  "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
}