{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fa932b90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              action  timestamp  frame_index  \\\n",
      "0  [-0.5927936, 0.8208263, 0.6505836, -0.2179081,...   0.000000            0   \n",
      "1  [-0.5927936, 0.8208263, 0.6505836, -0.2179081,...   0.033333            1   \n",
      "2  [-0.5927106, 0.82168543, 0.65093744, -0.212548...   0.066667            2   \n",
      "3  [-0.5927106, 0.82168543, 0.65093744, -0.212548...   0.100000            3   \n",
      "4  [-0.30210578, 0.5522966, 1.0746062, -0.0602206...   0.133333            4   \n",
      "\n",
      "   episode_index  index  task_index  next.done  \\\n",
      "0              0      0           0      False   \n",
      "1              0      1           0      False   \n",
      "2              0      2           0      False   \n",
      "3              0      3           0      False   \n",
      "4              0      4           0      False   \n",
      "\n",
      "                                   observation.state  \n",
      "0  [-0.24483089, -0.033253297, 0.018052276, 0.485...  \n",
      "1  [-0.24517907, -0.033261817, 0.018360289, 0.485...  \n",
      "2  [-0.24528621, -0.033261817, 0.018413857, 0.485...  \n",
      "3  [-0.24631737, -0.033261817, 0.018534383, 0.484...  \n",
      "4  [-0.24674593, -0.033261817, 0.018480817, 0.484...  \n"
     ]
    }
   ],
   "source": [
    "import pyarrow.parquet as pq\n",
    "import pandas as pd\n",
    "\n",
    "df = pq.read_table(\"/mnt/dataset/vnwy44/data/g1_humanoid_everyday/data/chunk-000/episode_000000.parquet\").to_pandas()\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a15c3a9",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'observation.state.arm_joint_position'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m/opt/conda/envs/hlmvla/lib/python3.10/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3811\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mpandas/_libs/index.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/index.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7096\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'observation.state.arm_joint_position'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mobservation.state.arm_joint_position\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobservation.state.arm_joint_position\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mleft_arm:\u001b[39m\u001b[38;5;124m'\u001b[39m, df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobservation.state.arm_joint_position\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m:\u001b[38;5;241m7\u001b[39m])\n",
      "File \u001b[0;32m/opt/conda/envs/hlmvla/lib/python3.10/site-packages/pandas/core/frame.py:4113\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4112\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4113\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4114\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4115\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m/opt/conda/envs/hlmvla/lib/python3.10/site-packages/pandas/core/indexes/base.py:3819\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3815\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3816\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3817\u001b[0m     ):\n\u001b[1;32m   3818\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3819\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3820\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3821\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3822\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3823\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3824\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'observation.state.arm_joint_position'"
     ]
    }
   ],
   "source": [
    "print(df['observation.state.arm_joint_position'][0].shape)\n",
    "print(df['observation.state.arm_joint_position'][0])\n",
    "print('left_arm:', df['observation.state.arm_joint_position'][0][0:7])\n",
    "print('right_arm:', df['observation.state.arm_joint_position'][0][7:14])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ddda817",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(43,)\n",
      "[-2.45179072e-01 -3.32618169e-02  1.83602888e-02  4.85666841e-01\n",
      " -2.75243163e-01  3.17346938e-02 -2.69646049e-01  1.73765942e-02\n",
      " -1.85477752e-02  4.70395207e-01 -2.13140875e-01 -9.84287169e-03\n",
      "  8.83865112e-04  0.00000000e+00  0.00000000e+00  1.10925987e-01\n",
      "  2.54175305e-01 -1.02944495e-02  1.02115190e+00  1.81237444e-01\n",
      " -5.74169047e-02 -8.21523443e-02  9.74437371e-02 -1.75810441e-01\n",
      "  3.19858976e-02  9.96332526e-01 -1.86270818e-01  1.45728176e-04\n",
      "  1.17963124e-02 -4.85885084e-01  6.90665364e-01  8.01368773e-01\n",
      " -1.40621871e-01 -3.67568552e-01 -1.29589722e-01 -3.60160351e-01\n",
      " -7.54963279e-01 -8.05062532e-01 -8.22540641e-01  1.52483419e-01\n",
      "  2.98312038e-01  1.03842743e-01  2.14183524e-01]\n",
      "left_arm: [-0.24517907 -0.03326182  0.01836029  0.48566684 -0.27524316  0.03173469\n",
      " -0.26964605]\n",
      "left_hand: [ 0.01737659 -0.01854778  0.4703952  -0.21314088 -0.00984287  0.00088387]\n",
      "right_arm: [ 0.          0.          0.11092599  0.2541753  -0.01029445  1.0211519\n",
      "  0.18123744]\n",
      "right_hand: [-0.0574169  -0.08215234  0.09744374 -0.17581044  0.0319859   0.9963325 ]\n",
      "head: [-1.8627082e-01  1.4572818e-04  1.1796312e-02]\n",
      "waist: [-0.48588508  0.69066536  0.8013688  -0.14062187]\n"
     ]
    }
   ],
   "source": [
    "print(df['observation.state'][1].shape)\n",
    "print(df['observation.state'][1])\n",
    "print('left_arm:', df['observation.state'][1][0:7])\n",
    "print('left_hand:', df['observation.state'][1][7:13])\n",
    "print('right_arm:', df['observation.state'][1][13:20])\n",
    "print('right_hand:', df['observation.state'][1][20:26])\n",
    "print('head:', df['observation.state'][1][26:29])\n",
    "print('waist:', df['observation.state'][1][29:33])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "533727a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(515,)\n",
      "(28,)\n",
      "[-0.5927936   0.8208263   0.6505836  -0.2179081  -0.29809755 -0.2035483\n",
      " -0.2839325  -0.699009   -0.8601557  -0.65025127  0.22987589  0.37633625\n",
      "  0.15428075  0.2985542  -0.21572664  0.47379622  0.01677799  1.2442846\n",
      "  0.1994059  -0.06384778 -0.13770401 -0.28145826 -0.25516176  0.01253347\n",
      "  1.2214912  -0.16791004 -0.02470203  0.18534912]\n",
      "left_arm: [-0.5927936   0.8208263   0.6505836  -0.2179081  -0.29809755 -0.2035483\n",
      " -0.2839325 ]\n",
      "left_hand: [-0.699009]\n",
      "right_arm: [-0.8601557  -0.65025127  0.22987589  0.37633625  0.15428075  0.2985542\n",
      " -0.21572664]\n",
      "right_hand: [0.47379622]\n",
      "head: [0.01677799 1.2442846  0.1994059 ]\n",
      "waist: [-0.06384778 -0.13770401 -0.28145826 -0.25516176]\n"
     ]
    }
   ],
   "source": [
    "print(df['action'].shape)\n",
    "print(df['action'][0].shape)\n",
    "print(df['action'][0])\n",
    "print('left_arm:', df['action'][0][0:7])\n",
    "print('left_hand:', df['action'][0][7:8])\n",
    "print('right_arm:', df['action'][0][8:15])\n",
    "print('right_hand:', df['action'][0][15:16])\n",
    "print('head:', df['action'][0][16:19])\n",
    "print('waist:', df['action'][0][19:23])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d15d20d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 把每一步的 state 堆成一个 [T, 33] 的数组\n",
    "states = np.stack(df['observation.state'].to_numpy())   # 或 .values\n",
    "print(states.shape)\n",
    "actions = np.stack(df['action'].to_numpy())             # [T, 23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d71debb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1336,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "joint = 0  # 左臂第一个关节\n",
    "\n",
    "delta_state = np.diff(states[:, joint])\n",
    "print(delta_state.shape)\n",
    "\n",
    "plt.plot(states[:, joint], label='state')\n",
    "plt.plot(actions[:, joint], label='action')\n",
    "plt.plot(delta_state, label='Δ state')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "838687e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1336,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "joint = 6  # 左臂第一个关节\n",
    "\n",
    "delta_state = np.diff(states[:, joint])\n",
    "print(delta_state.shape)\n",
    "\n",
    "plt.plot(states[:, joint], label='state')\n",
    "plt.plot(actions[:, joint], label='action')\n",
    "plt.plot(delta_state, label='Δ state')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hlmvla",
   "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": "3.10.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}