Remove .vscode/ and Code/ folders from remote, keep local copies

.gitignore

@@ -6,3 +6,5 @@ Multi_View/
 Code/
 zip_scene.py
 .gitattributes
+.vscode/
+Code/

.vscode/settings.json

@@ -1,3 +0,0 @@
-{
-    "python.analysis.autoImportCompletions": true
-}

Code/explore.ipynb

@@ -1,857 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "cc0b451f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import pandas as pd\n",
-    "\n",
-    "base = os.getcwd()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e3b39b13",
-   "metadata": {},
-   "source": [
-    "# Real"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "85e4d8c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'base: /Users/dsl/Desktop/Causal3D_Dataset/Code'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'data_path: /Users/dsl/Desktop/Causal3D_Dataset/Code/../Real'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Scene: Convex_len, Number of files: 10001, File types: {'.csv', '.png'}\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Scene: Seesaw, Number of files: 10001, File types: {'.csv', '.png'}\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Scene: Pendulum, Number of files: 10001, File types: {'.csv', '.png'}\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Scene: Water_flow, Number of files: 10001, File types: {'.csv', '.png'}\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Scene: Parabola, Number of files: 10001, File types: {'.csv', '.png'}\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Scene: Magnet, Number of files: 10001, File types: {'.csv', '.png'}\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Scene: Spring, Number of files: 10001, File types: {'.csv', '.png'}\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "\"Scene: Reflection, Number of files: 10001, File types: {'.csv', '.png'}\""
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from IPython.display import display\n",
-    "\n",
-    "base = os.getcwd()\n",
-    "data_path = os.path.join(base, \"../Real\")\n",
-    "scenes = os.listdir(data_path)\n",
-    "\n",
-    "display(f\"base: {base}\")\n",
-    "display(f\"data_path: {data_path}\")\n",
-    "all_scenes = [scene for scene in scenes if os.path.isdir(os.path.join(data_path, scene))]\n",
-    "# get the number of files and the corresponding type under the each scene\n",
-    "for scene in all_scenes:\n",
-    "    scene_path = os.path.join(data_path, scene)\n",
-    "    files = os.listdir(scene_path)\n",
-    "    file_types = set([os.path.splitext(file)[1] for file in files])\n",
-    "    display(f\"Scene: {scene}, Number of files: {len(files)}, File types: {file_types}\") \n",
-    "    # You see an empty string '' in file_types because some files in the directory do not have an extension.\n",
-    "    # For example, hidden files like '.DS_Store' or files without a dot will result in an empty extension from os.path.splitext.\n",
-    "    # To check which files have no extension:\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "6979c835",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Convex_len': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Convex_len/tabular.csv'],\n",
-       " 'Seesaw': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Seesaw/tabular.csv'],\n",
-       " 'Pendulum': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Pendulum/tabular.csv'],\n",
-       " 'Water_flow': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Water_flow/tabular.csv'],\n",
-       " 'Parabola': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Parabola/tabular.csv'],\n",
-       " 'Magnet': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Magnet/tabular.csv'],\n",
-       " 'Spring': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Spring/tabular.csv'],\n",
-       " 'Reflection': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Reflection/tabular.csv']}"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scene_csv_paths = {}\n",
-    "for scene in all_scenes:\n",
-    "    folder = os.path.join(data_path, scene)\n",
-    "    if not os.path.isdir(folder):\n",
-    "        continue\n",
-    "    csv_files = [f for f in os.listdir(folder) if f.endswith('.csv')]\n",
-    "    scene_csv_paths[scene] = [os.path.join(folder, f) for f in csv_files]\n",
-    "scene_csv_paths\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "c58bbbd3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'Convex_len': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Convex_len/tabular.csv'],\n",
-       " 'Seesaw': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Seesaw/tabular.csv'],\n",
-       " 'Pendulum': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Pendulum/tabular.csv'],\n",
-       " 'Water_flow': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Water_flow/tabular.csv'],\n",
-       " 'Parabola': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Parabola/tabular.csv'],\n",
-       " 'Magnet': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Magnet/tabular.csv'],\n",
-       " 'Spring': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Spring/tabular.csv'],\n",
-       " 'Reflection': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Real/Reflection/tabular.csv']}"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scene_csv_paths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "54888282",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "# === Function to convert filenames ===\n",
-    "def convert_filename(filename):\n",
-    "    if isinstance(filename, str) and filename.endswith('.png'):\n",
-    "        # Remove leading zeros from the number part\n",
-    "        number_part = filename.split('.')[0]\n",
-    "        new_number = str(int(number_part))\n",
-    "        return f\"{new_number}.png\"\n",
-    "    return filename  # if not a string or doesn't match, return as is\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "9b3afa52",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check each csv file, are there all of png files exists\n",
-    "for i in scene_csv_paths:\n",
-    "    df = pd.read_csv(scene_csv_paths[i][0])\n",
-    "    df['imgs'] = df['imgs'].apply(convert_filename)\n",
-    "    df.to_csv(scene_csv_paths[i][0], index=False)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "50723819",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# check each csv file, are there all of png files exists\n",
-    "for i in scene_csv_paths:\n",
-    "    df = pd.read_csv(scene_csv_paths[i][0])\n",
-    "    imgs = df['imgs'].tolist()\n",
-    "    for img in imgs:\n",
-    "        img_path = os.path.join(data_path, i, img)\n",
-    "        if not os.path.exists(img_path):\n",
-    "            print(f\"Image {img} does not exist in scene {i}.\")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d7f2bad",
-   "metadata": {},
-   "source": [
-    "# Hypothetic\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "d9c4b659",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scene: V4_linear, Number of files: 10003, File types: {'.png': 10001, '': 1, '.csv': 1}\n",
-      "Scene: V4_v_structure_linear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n",
-      "Scene: V3_fully_connected_linear, Number of files: 10002, File types: {'.png': 10000, '.md': 1, '.csv': 1}\n",
-      "Scene: V2_linear, Number of files: 10002, File types: {'.png': 10000, '.md': 1, '.csv': 1}\n",
-      "Scene: V2_nonlinear, Number of files: 10002, File types: {'.png': 10000, '.md': 1, '.csv': 1}\n",
-      "Scene: V5_linear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n",
-      "Scene: V3_v_structure_nonlinear, Number of files: 10002, File types: {'.png': 10000, '': 1, '.csv': 1}\n",
-      "Scene: V3_v_structure_linear, Number of files: 10002, File types: {'.png': 10000, '': 1, '.csv': 1}\n",
-      "Scene: V5_v_structure_nonlinear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n",
-      "Scene: V5_v_structure_linear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n",
-      "Scene: V4_v_strcuture_nonlinear, Number of files: 10001, File types: {'.png': 10000, '.csv': 1}\n"
-     ]
-    }
-   ],
-   "source": [
-    "hy_base_path = os.path.join(base, \"../Hypothetic\")\n",
-    "hy_scenes = os.listdir(hy_base_path)\n",
-    "for scene in hy_scenes:\n",
-    "    scene_path = os.path.join(hy_base_path, scene)\n",
-    "    if not os.path.isdir(scene_path) or scene.startswith('.'):\n",
-    "        continue\n",
-    "    files = os.listdir(scene_path)\n",
-    "    file_types = {}\n",
-    "    for file in files:\n",
-    "        ext = os.path.splitext(file)[1]\n",
-    "        file_types[ext] = file_types.get(ext, 0) + 1\n",
-    "    print(f\"Scene: {scene}, Number of files: {len(files)}, File types: {file_types}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "6444b695",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'V4_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_linear/tabular.csv'],\n",
-       " 'V4_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_v_structure_linear/tabular.csv'],\n",
-       " 'V3_fully_connected_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_fully_connected_linear/tabular.csv'],\n",
-       " 'V2_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V2_linear/tabular.csv'],\n",
-       " 'V2_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V2_nonlinear/tabular.csv'],\n",
-       " 'V5_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_linear/tabular.csv'],\n",
-       " 'V3_v_structure_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_v_structure_nonlinear/tabular.csv'],\n",
-       " 'V3_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_v_structure_linear/tabular.csv'],\n",
-       " 'V5_v_structure_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_v_structure_nonlinear/tabular.csv'],\n",
-       " 'V5_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_v_structure_linear/tabular.csv'],\n",
-       " 'V4_v_strcuture_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_v_strcuture_nonlinear/tabular.csv']}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hy_scene_csv_paths = {}\n",
-    "for scene in hy_scenes:\n",
-    "    scene_folder = os.path.join(hy_base_path, scene)\n",
-    "    if not os.path.isdir(scene_folder) or scene.startswith('.'):\n",
-    "        continue\n",
-    "    csvs = [f for f in os.listdir(scene_folder) if f.endswith('.csv')]\n",
-    "    hy_scene_csv_paths[scene] = [os.path.join(scene_folder, f) for f in csvs]\n",
-    "hy_scene_csv_paths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "4bc1be73",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hypothetic Scene: V4_linear, CSV: tabular.csv\n",
-      "Columns: ['volume_ball', 'height_cuboid', 'base_area_cuboid', 'base_area_cone', 'imgs']\n",
-      "\n",
-      "Hypothetic Scene: V4_v_structure_linear, CSV: tabular.csv\n",
-      "Columns: ['volumn_ball', 'height_cuboid', 'base_area_cuboid', 'base_area_cone', 'imgs']\n",
-      "\n",
-      "Hypothetic Scene: V3_fully_connected_linear, CSV: tabular.csv\n",
-      "Columns: ['iter', 'volume_ball', 'height_of_cuboid', 'base_area_cone', 'img_path']\n",
-      "\n",
-      "Hypothetic Scene: V2_linear, CSV: tabular.csv\n",
-      "Columns: ['iter', 'volume_ball', 'r_ball', 'volume_cube', 'edge_cube', 'img_path']\n",
-      "\n",
-      "Hypothetic Scene: V2_nonlinear, CSV: tabular.csv\n",
-      "Columns: ['iter', 'volume_ball', 'r_ball', 'scaled_volume_ball', 'volume_cube', 'edge_cube', 'img_path']\n",
-      "\n",
-      "Hypothetic Scene: V5_linear, CSV: tabular.csv\n",
-      "Columns: ['volumn_ball', 'height_cuboid', 'base_area_cuboid', 'base_area_cone', 'height_cone', 'imgs']\n",
-      "\n",
-      "Hypothetic Scene: V3_v_structure_nonlinear, CSV: tabular.csv\n",
-      "Columns: ['volume_ball', 'scaled_volume_ball', 'radius of ball', 'height_cylinder', 'radius of cone', 'basal_area_cone', 'imgs']\n",
-      "\n",
-      "Hypothetic Scene: V3_v_structure_linear, CSV: tabular.csv\n",
-      "Columns: ['volume_ball', 'height_cylinder', 'basal_area_cone', 'imgs', 'Unnamed: 4']\n",
-      "\n",
-      "Hypothetic Scene: V5_v_structure_nonlinear, CSV: tabular.csv\n",
-      "Columns: ['volumn_ball', 'scaled_volumn_ball', 'height_cuboid', 'base_area_cuboid', 'scaled_base_area_cuboid', 'base_area_cone', 'scaled_base_area_cone', 'height_cone', 'imgs']\n",
-      "\n",
-      "Hypothetic Scene: V5_v_structure_linear, CSV: tabular.csv\n",
-      "Columns: ['volumn_ball', 'height_cuboid', 'base_area_cuboid', 'base_area_cone', 'height_cone', 'imgs']\n",
-      "\n",
-      "Hypothetic Scene: V4_v_strcuture_nonlinear, CSV: tabular.csv\n",
-      "Columns: ['volumn_ball', 'scaled_volumn_ball', 'height_cuboid', 'base_area_cuboid', 'scaled_base_area_cuboid', 'base_area_cone', 'imgs']\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for scene, csv_paths in hy_scene_csv_paths.items():\n",
-    "    for csv_file in csv_paths:\n",
-    "        df = pd.read_csv(csv_file, nrows=0)\n",
-    "        print(f\"Hypothetic Scene: {scene}, CSV: {os.path.basename(csv_file)}\")\n",
-    "        print(\"Columns:\", list(df.columns))\n",
-    "        print()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "fefc0d47",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V3_fully_connected_linear', '/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_nonlinear', '/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_linear'])"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "process_path = [\"/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V3_fully_connected_linear\",\n",
-    "                \"/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_nonlinear\",\n",
-    "                \"/Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_linear\"]\n",
-    "dfs_dict = {}\n",
-    "for path in process_path:\n",
-    "    csv_file = os.path.join(path, \"tabular.csv\")\n",
-    "    if os.path.exists(csv_file):\n",
-    "        df = pd.read_csv(csv_file)\n",
-    "        dfs_dict[path] = df\n",
-    "dfs_dict.keys()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "e6c53182",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "DataFrame for: /Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V3_fully_connected_linear\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iter</th>\n",
-       "      <th>volume_ball</th>\n",
-       "      <th>height_of_cuboid</th>\n",
-       "      <th>base_area_cone</th>\n",
-       "      <th>imgs</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>4.461709</td>\n",
-       "      <td>2.230855</td>\n",
-       "      <td>4.461709</td>\n",
-       "      <td>00001.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>4.641952</td>\n",
-       "      <td>2.320976</td>\n",
-       "      <td>4.641952</td>\n",
-       "      <td>00002.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>5.732580</td>\n",
-       "      <td>2.866290</td>\n",
-       "      <td>5.732580</td>\n",
-       "      <td>00003.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>9.686783</td>\n",
-       "      <td>4.843392</td>\n",
-       "      <td>9.686783</td>\n",
-       "      <td>00004.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>2.608935</td>\n",
-       "      <td>1.304468</td>\n",
-       "      <td>2.608935</td>\n",
-       "      <td>00005.png</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   iter  volume_ball  height_of_cuboid  base_area_cone       imgs\n",
-       "0     1     4.461709          2.230855        4.461709  00001.png\n",
-       "1     2     4.641952          2.320976        4.641952  00002.png\n",
-       "2     3     5.732580          2.866290        5.732580  00003.png\n",
-       "3     4     9.686783          4.843392        9.686783  00004.png\n",
-       "4     5     2.608935          1.304468        2.608935  00005.png"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape: (10000, 5)\n",
-      "------------------------------------------------------------\n",
-      "DataFrame for: /Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_nonlinear\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iter</th>\n",
-       "      <th>volume_ball</th>\n",
-       "      <th>r_ball</th>\n",
-       "      <th>scaled_volume_ball</th>\n",
-       "      <th>volume_cube</th>\n",
-       "      <th>edge_cube</th>\n",
-       "      <th>imgs</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>8.369589</td>\n",
-       "      <td>1.259520</td>\n",
-       "      <td>-0.260683</td>\n",
-       "      <td>5.797285</td>\n",
-       "      <td>1.796421</td>\n",
-       "      <td>00001.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>8.748098</td>\n",
-       "      <td>1.278228</td>\n",
-       "      <td>-0.201078</td>\n",
-       "      <td>5.879111</td>\n",
-       "      <td>1.804834</td>\n",
-       "      <td>00002.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>11.038418</td>\n",
-       "      <td>1.381251</td>\n",
-       "      <td>0.159586</td>\n",
-       "      <td>5.923759</td>\n",
-       "      <td>1.809391</td>\n",
-       "      <td>00003.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>19.342245</td>\n",
-       "      <td>1.665224</td>\n",
-       "      <td>1.467218</td>\n",
-       "      <td>0.620362</td>\n",
-       "      <td>0.852868</td>\n",
-       "      <td>00004.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>4.478764</td>\n",
-       "      <td>1.022562</td>\n",
-       "      <td>-0.873384</td>\n",
-       "      <td>3.853417</td>\n",
-       "      <td>1.567769</td>\n",
-       "      <td>00005.png</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   iter  volume_ball    r_ball  scaled_volume_ball  volume_cube  edge_cube  \\\n",
-       "0     1     8.369589  1.259520           -0.260683     5.797285   1.796421   \n",
-       "1     2     8.748098  1.278228           -0.201078     5.879111   1.804834   \n",
-       "2     3    11.038418  1.381251            0.159586     5.923759   1.809391   \n",
-       "3     4    19.342245  1.665224            1.467218     0.620362   0.852868   \n",
-       "4     5     4.478764  1.022562           -0.873384     3.853417   1.567769   \n",
-       "\n",
-       "        imgs  \n",
-       "0  00001.png  \n",
-       "1  00002.png  \n",
-       "2  00003.png  \n",
-       "3  00004.png  \n",
-       "4  00005.png  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape: (10000, 7)\n",
-      "------------------------------------------------------------\n",
-      "DataFrame for: /Users/dsl/Desktop/Causal3D_Dataset/Hypothetic/V2_linear\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iter</th>\n",
-       "      <th>volume_ball</th>\n",
-       "      <th>r_ball</th>\n",
-       "      <th>volume_cube</th>\n",
-       "      <th>edge_cube</th>\n",
-       "      <th>imgs</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>7.118523</td>\n",
-       "      <td>1.193348</td>\n",
-       "      <td>10.677784</td>\n",
-       "      <td>2.202049</td>\n",
-       "      <td>00001.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>7.440114</td>\n",
-       "      <td>1.211054</td>\n",
-       "      <td>11.160170</td>\n",
-       "      <td>2.234723</td>\n",
-       "      <td>00002.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>9.386024</td>\n",
-       "      <td>1.308572</td>\n",
-       "      <td>14.079037</td>\n",
-       "      <td>2.414669</td>\n",
-       "      <td>00003.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>16.441156</td>\n",
-       "      <td>1.577422</td>\n",
-       "      <td>24.661734</td>\n",
-       "      <td>2.910770</td>\n",
-       "      <td>00004.png</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>3.812784</td>\n",
-       "      <td>0.969136</td>\n",
-       "      <td>5.719176</td>\n",
-       "      <td>1.788317</td>\n",
-       "      <td>00005.png</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   iter  volume_ball    r_ball  volume_cube  edge_cube       imgs\n",
-       "0     1     7.118523  1.193348    10.677784   2.202049  00001.png\n",
-       "1     2     7.440114  1.211054    11.160170   2.234723  00002.png\n",
-       "2     3     9.386024  1.308572    14.079037   2.414669  00003.png\n",
-       "3     4    16.441156  1.577422    24.661734   2.910770  00004.png\n",
-       "4     5     3.812784  0.969136     5.719176   1.788317  00005.png"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Shape: (10000, 6)\n",
-      "------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "for path, df in dfs_dict.items():\n",
-    "    print(f\"DataFrame for: {path}\")\n",
-    "    display(df.head())\n",
-    "    print(f\"Shape: {df.shape}\")\n",
-    "    print(\"-\" * 60)\n",
-    "    # if 'img_path' in df.columns:\n",
-    "    #     df = df.rename(columns={'img_path': 'imgs'})\n",
-    "    #     # Update the original CSV file\n",
-    "    #     csv_file = os.path.join(path, \"tabular.csv\")\n",
-    "    #     df.to_csv(csv_file, index=False)\n",
-    "    #     print(f\"Updated column name and saved: {csv_file}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "835862a9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'V4_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_linear/tabular.csv'],\n",
-       " 'V4_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_v_structure_linear/tabular.csv'],\n",
-       " 'V3_fully_connected_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_fully_connected_linear/tabular.csv'],\n",
-       " 'V2_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V2_linear/tabular.csv'],\n",
-       " 'V2_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V2_nonlinear/tabular.csv'],\n",
-       " 'V5_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_linear/tabular.csv'],\n",
-       " 'V3_v_structure_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_v_structure_nonlinear/tabular.csv'],\n",
-       " 'V3_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V3_v_structure_linear/tabular.csv'],\n",
-       " 'V5_v_structure_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_v_structure_nonlinear/tabular.csv'],\n",
-       " 'V5_v_structure_linear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V5_v_structure_linear/tabular.csv'],\n",
-       " 'V4_v_strcuture_nonlinear': ['/Users/dsl/Desktop/Causal3D_Dataset/Code/../Hypothetic/V4_v_strcuture_nonlinear/tabular.csv']}"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hy_scene_csv_paths"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "f5d085eb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Scene: V4_linear, all images exist.\n",
-      "Scene: V4_v_structure_linear, all images exist.\n",
-      "Scene: V3_fully_connected_linear, all images exist.\n",
-      "Scene: V2_linear, all images exist.\n",
-      "Scene: V2_nonlinear, all images exist.\n",
-      "Scene: V5_linear, all images exist.\n",
-      "Scene: V3_v_structure_nonlinear, all images exist.\n",
-      "Scene: V3_v_structure_linear, all images exist.\n",
-      "Scene: V5_v_structure_nonlinear, all images exist.\n",
-      "Scene: V5_v_structure_linear, all images exist.\n",
-      "Scene: V4_v_strcuture_nonlinear, all images exist.\n"
-     ]
-    }
-   ],
-   "source": [
-    "for scene, csv_paths in hy_scene_csv_paths.items():\n",
-    "    for csv_file in csv_paths:\n",
-    "        df = pd.read_csv(csv_file)\n",
-    "        img_col = 'imgs' if 'imgs' in df.columns else 'img_path'\n",
-    "        img_dir = os.path.dirname(csv_file)\n",
-    "        missing_imgs = []\n",
-    "        for img_file in df[img_col]:\n",
-    "            img_path = os.path.join(img_dir, img_file)\n",
-    "            if not os.path.exists(img_path):\n",
-    "                missing_imgs.append(img_file)\n",
-    "        if missing_imgs:\n",
-    "            print(f\"Scene: {scene}, Missing images: {len(missing_imgs)}\")\n",
-    "            print(missing_imgs[:10])  # show up to 10 missing images\n",
-    "        else:\n",
-    "            print(f\"Scene: {scene}, all images exist.\")"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "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": "3.13.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}