{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8040ca06",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6b6a12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "matplotlib.rcParams['figure.figsize'] = [16, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a249d23",
   "metadata": {},
   "outputs": [],
   "source": [
    "DEMO_DIR = Path('/home/anthony_liu/prompt-subtasks/promptrl/data')\n",
    "TRAIN_FILE = DEMO_DIR / 'train_data_viz.pkl'\n",
    "EVAL_ID_FILE = DEMO_DIR / 'eval_id_data_viz.pkl'\n",
    "EVAL_OOD_FILE = DEMO_DIR / 'eval_ood_data_viz.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bbab7b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_demo(path):\n",
    "    with open(path, 'rb') as f:\n",
    "        result = pickle.load(f)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3dda3d0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "199 train demos loaded.\n",
      "187 eval id demos loaded.\n",
      "192 eval ood demos loaded.\n"
     ]
    }
   ],
   "source": [
    "train_demos = load_demo(TRAIN_FILE)\n",
    "id_demos = load_demo(EVAL_ID_FILE)\n",
    "ood_demos = load_demo(EVAL_OOD_FILE)\n",
    "print(len(train_demos), 'train demos loaded.')\n",
    "print(len(id_demos), 'eval id demos loaded.')\n",
    "print(len(ood_demos), 'eval ood demos loaded.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1e14596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78 out of 199 train demos failed.\n",
      "64 out of 187 eval id demos failed.\n",
      "179 out of 192 ood id demos failed.\n"
     ]
    }
   ],
   "source": [
    "print(sum(len(demo['actions']) >= 52 for demo in train_demos), 'out of', len(train_demos), 'train demos failed.')\n",
    "print(sum(len(demo['actions']) >= 52 for demo in id_demos), 'out of', len(id_demos), 'eval id demos failed.')\n",
    "print(sum(len(demo['actions']) >= 52 for demo in ood_demos), 'out of', len(ood_demos), 'ood id demos failed.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "065179e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def truncate_string(s, max_len=80):\n",
    "    return '\\n'.join(s[i:i+max_len] for i in range(0, len(s), max_len))\n",
    "\n",
    "def viz_demo(demo):\n",
    "    print(f'Goal: \"{demo[\"goal\"]}\"')\n",
    "    obs = demo['obs']\n",
    "    for i in range(len(obs)):\n",
    "        N = obs[i].shape[0]\n",
    "        fig, ax = plt.subplots(1, N)\n",
    "        if N > 1:\n",
    "            for j in range(N):\n",
    "                ax[j].set_axis_off()\n",
    "                ax[j].imshow(obs[i][j])\n",
    "        else:\n",
    "            ax.set_axis_off()\n",
    "            ax.imshow(obs[i][0])\n",
    "        plt.title(truncate_string(demo['oracle'][i]) + '\\n' + demo['actions'][i])\n",
    "        plt.subplots_adjust(wspace=0, hspace=0)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "23f2b47c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Goal: \"Your task is to: put a hot potato in garbagecan\"\n"
     ]
    },
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    },
    {
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    {
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     },
     "metadata": {
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   ],
   "source": [
    "viz_demo(ood_demos[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68fc405d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 8944 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([len(d['actions']) for d in id_demos])\n",
    "plt.title('Demonstration length distribution')\n",
    "plt.xlabel('Demo length')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e6ceebe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 8624 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([d['obs'][0].shape[0] for d in train_demos])\n",
    "plt.title('Initial Observation $o_1$ number of frames distribution')\n",
    "plt.xlabel('$o_1$ number of frames')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0ef97ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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