diff --git "a/ini_pro.ipynb" "b/ini_pro.ipynb"
deleted file mode 100644--- "a/ini_pro.ipynb"
+++ /dev/null
@@ -1,3340 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import os\n",
-    "import shutil\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "import pandas as pd\n",
-    "import json "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create train-test folder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_path = 'kidney-ct-abnormality'\n",
-    "images_path = os.path.join(base_path, 'imagesTr')\n",
-    "all_images = [f for f in os.listdir(images_path) if f.endswith('.mha')]\n",
-    "train_files, test_files = train_test_split(all_images, test_size=0.2, random_state=219)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_dir = os.path.join(base_path, 'train')\n",
-    "test_dir = os.path.join(base_path, 'test')\n",
-    "os.makedirs(train_dir, exist_ok=True)\n",
-    "os.makedirs(test_dir, exist_ok=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def move_files(files, destination):\n",
-    "    for f in files:\n",
-    "        shutil.move(os.path.join(images_path, f), os.path.join(destination, f))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "move_files(train_files, train_dir)\n",
-    "move_files(test_files, test_dir)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Modify the json file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'/Users/ziyingye/Desktop/stats/24spring/STA663/kidney_CT_abnormality'"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "os.getcwd()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "json_file_path = 'dataset.json' \n",
-    "with open(json_file_path, 'r') as file:\n",
-    "    img_label = json.load(file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "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>image</th>\n",
-       "      <th>abnormality</th>\n",
-       "      <th>split</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>kidneyabnormalityKiTS-0000_0000.mha</td>\n",
-       "      <td>0</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>kidneyabnormalityKiTS-0001_0000.mha</td>\n",
-       "      <td>1</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>kidneyabnormalityKiTS-0002_0000.mha</td>\n",
-       "      <td>0</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>kidneyabnormalityKiTS-0003_0000.mha</td>\n",
-       "      <td>1</td>\n",
-       "      <td>test</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>kidneyabnormalityKiTS-0004_0000.mha</td>\n",
-       "      <td>1</td>\n",
-       "      <td>test</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>981</th>\n",
-       "      <td>kidneyabnormalityRUMC-0390_0000.mha</td>\n",
-       "      <td>0</td>\n",
-       "      <td>test</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>982</th>\n",
-       "      <td>kidneyabnormalityRUMC-0391_0000.mha</td>\n",
-       "      <td>0</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>983</th>\n",
-       "      <td>kidneyabnormalityRUMC-0392_0000.mha</td>\n",
-       "      <td>0</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>984</th>\n",
-       "      <td>kidneyabnormalityRUMC-0393_0000.mha</td>\n",
-       "      <td>0</td>\n",
-       "      <td>test</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>985</th>\n",
-       "      <td>kidneyabnormalityRUMC-0394_0000.mha</td>\n",
-       "      <td>0</td>\n",
-       "      <td>train</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>986 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                   image  abnormality  split\n",
-       "0    kidneyabnormalityKiTS-0000_0000.mha            0  train\n",
-       "1    kidneyabnormalityKiTS-0001_0000.mha            1  train\n",
-       "2    kidneyabnormalityKiTS-0002_0000.mha            0  train\n",
-       "3    kidneyabnormalityKiTS-0003_0000.mha            1   test\n",
-       "4    kidneyabnormalityKiTS-0004_0000.mha            1   test\n",
-       "..                                   ...          ...    ...\n",
-       "981  kidneyabnormalityRUMC-0390_0000.mha            0   test\n",
-       "982  kidneyabnormalityRUMC-0391_0000.mha            0  train\n",
-       "983  kidneyabnormalityRUMC-0392_0000.mha            0  train\n",
-       "984  kidneyabnormalityRUMC-0393_0000.mha            0   test\n",
-       "985  kidneyabnormalityRUMC-0394_0000.mha            0  train\n",
-       "\n",
-       "[986 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = img_label['training']\n",
-    "df = pd.DataFrame(data)\n",
-    "df.rename(columns={'Abnormality': 'abnormality'}, inplace=True)\n",
-    "df['image'] = df['image'].apply(lambda x: x[11:-4]+'_0000'+x[-4:])\n",
-    "test_images = [f for f in os.listdir('test') if f.endswith('.mha')]\n",
-    "df['split'] = df['image'].apply(lambda x: 'test' if x in test_images else 'train')\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "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></th>\n",
-       "      <th>count</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>split</th>\n",
-       "      <th>abnormality</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">test</th>\n",
-       "      <th>0</th>\n",
-       "      <td>79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>119</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">train</th>\n",
-       "      <th>0</th>\n",
-       "      <td>289</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>499</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                   count\n",
-       "split abnormality       \n",
-       "test  0               79\n",
-       "      1              119\n",
-       "train 0              289\n",
-       "      1              499"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_copy = df.copy()\n",
-    "df_copy['count']=1\n",
-    "df_copy.groupby(['split', 'abnormality']).sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'image': 'kidneyabnormalityKiTS-0000_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0001_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0002_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0003_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'test'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0004_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'test'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0005_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0006_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0007_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0008_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0009_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
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-       " {'image': 'kidneyabnormalityKiTS-0057_0000.mha',\n",
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-       " {'image': 'kidneyabnormalityKiTS-0063_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0064_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0065_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'test'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0066_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0067_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'test'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0068_0000.mha',\n",
-       "  'abnormality': 1,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityKiTS-0069_0000.mha',\n",
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-       "  'abnormality': 0,\n",
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-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0380_0000.mha',\n",
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-       " {'image': 'kidneyabnormalityRUMC-0381_0000.mha',\n",
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-       " {'image': 'kidneyabnormalityRUMC-0382_0000.mha',\n",
-       "  'abnormality': 0,\n",
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-       " {'image': 'kidneyabnormalityRUMC-0383_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0384_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0385_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0386_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0387_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'test'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0388_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0389_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0390_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'test'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0391_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0392_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0393_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'test'},\n",
-       " {'image': 'kidneyabnormalityRUMC-0394_0000.mha',\n",
-       "  'abnormality': 0,\n",
-       "  'split': 'train'}]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list_of_dicts = df.to_dict(orient='records')\n",
-    "list_of_dicts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.to_json('dataset_m.json', orient='records', lines=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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