{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/reina/anaconda3/envs/RSNA/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
      "  return f(*args, **kwds)\n",
      "/home/reina/anaconda3/envs/RSNA/lib/python3.6/importlib/_bootstrap.py:219: ImportWarning: can't resolve package from __spec__ or __package__, falling back on __name__ and __path__\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import glob, pylab, pandas as pd\n",
    "import pydicom, numpy as np\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "import matplotlib.pylab as plt\n",
    "import os\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(action='once')\n",
    "import pickle\n",
    "from tqdm import tqdm, tqdm_notebook\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib notebook\n",
    "from helper import *\n",
    "import time\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "from defenitions import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = '/media/nvme/data/RSNA/'\n",
    "train_images_dir=data_dir+'stage_1_train_images/'\n",
    "test_images_dir=data_dir+'stage_1_test_images/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_base_df = pd.read_csv(data_dir+'stage_1_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4045572"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_base_df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>ID</th>\n",
       "      <th>Label</th>\n",
       "      <th>Sub_type</th>\n",
       "      <th>PatientID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_28fbab7eb_epidural</td>\n",
       "      <td>0.5</td>\n",
       "      <td>epidural</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_28fbab7eb_intraparenchymal</td>\n",
       "      <td>0.5</td>\n",
       "      <td>intraparenchymal</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_28fbab7eb_intraventricular</td>\n",
       "      <td>0.5</td>\n",
       "      <td>intraventricular</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_28fbab7eb_subarachnoid</td>\n",
       "      <td>0.5</td>\n",
       "      <td>subarachnoid</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_28fbab7eb_subdural</td>\n",
       "      <td>0.5</td>\n",
       "      <td>subdural</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              ID  Label          Sub_type  PatientID\n",
       "0          ID_28fbab7eb_epidural    0.5          epidural  28fbab7eb\n",
       "1  ID_28fbab7eb_intraparenchymal    0.5  intraparenchymal  28fbab7eb\n",
       "2  ID_28fbab7eb_intraventricular    0.5  intraventricular  28fbab7eb\n",
       "3      ID_28fbab7eb_subarachnoid    0.5      subarachnoid  28fbab7eb\n",
       "4          ID_28fbab7eb_subdural    0.5          subdural  28fbab7eb"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_base_df['Sub_type'] = train_base_df['ID'].str.split(\"_\", n = 3, expand = True)[2]\n",
    "train_base_df['PatientID'] = train_base_df['ID'].str.split(\"_\", n = 3, expand = True)[1]\n",
    "train_base_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid',\n",
       "       'subdural', 'any'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_types=train_base_df.Sub_type.unique()\n",
    "sub_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ae4afd5b0d824567b34dd59b1b81e430",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dfs =[]\n",
    "for sub_type in tqdm_notebook(sub_types):\n",
    "    df = train_base_df[train_base_df.Sub_type==sub_type][['PatientID','Label']].copy()\n",
    "    df=df.rename(columns={\"Label\": sub_type}).reset_index(drop=True)\n",
    "    dfs.append(df)\n",
    "train_df=dfs[0]\n",
    "for df in tqdm_notebook(dfs[1:]):\n",
    "    train_df=test_df.merge(df,on='PatientID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fe21e059b0f4fff9decf2a356119816",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=78545), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "PID = np.zeros(train_df.shape[0],dtype=object)\n",
    "StudyI = np.zeros(train_df.shape[0],dtype=object)\n",
    "SeriesI = np.zeros(train_df.shape[0],dtype=object)\n",
    "WindowCenter = np.zeros(train_df.shape[0],dtype=object)\n",
    "WindowWidth = np.zeros(train_df.shape[0],dtype=object)\n",
    "ImagePositionX = np.zeros(train_df.shape[0],dtype=np.float)\n",
    "ImagePositionY = np.zeros(train_df.shape[0],dtype=np.float)\n",
    "ImagePositionZ = np.zeros(train_df.shape[0],dtype=np.float)\n",
    "\n",
    "for i,row in tqdm_notebook(train_df.iterrows(),total=train_df.shape[0]):\n",
    "    ds = pydicom.dcmread(train_images_dir + 'ID_{}.dcm'.format(row['PatientID']))\n",
    "    SeriesI[i]=ds.SeriesInstanceUID\n",
    "    PID[i]=ds.PatientID\n",
    "    StudyI[i]=ds.StudyInstanceUID \n",
    "    WindowCenter[i]=ds.WindowCenter\n",
    "    WindowWidth[i]=ds.WindowWidth\n",
    "    ImagePositionX[i]=float(ds.ImagePositionPatient[0])\n",
    "    ImagePositionY[i]=float(ds.ImagePositionPatient[1])\n",
    "    ImagePositionZ[i]=float(ds.ImagePositionPatient[2])\n",
    "train_df['SeriesI']=SeriesI\n",
    "train_df['PID']=PID\n",
    "train_df['StudyI']=StudyI\n",
    "train_df['WindowCenter']=WindowCenter\n",
    "train_df['WindowWidth']=WindowWidth\n",
    "train_df['ImagePositionZ']=ImagePositionZ\n",
    "train_df['ImagePositionX']=ImagePositionX\n",
    "train_df['ImagePositionY']=ImagePositionY\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.to_csv(data_dir+'train.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>ID</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_28fbab7eb_epidural</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_28fbab7eb_intraparenchymal</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_28fbab7eb_intraventricular</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_28fbab7eb_subarachnoid</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_28fbab7eb_subdural</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              ID  Label\n",
       "0          ID_28fbab7eb_epidural    0.5\n",
       "1  ID_28fbab7eb_intraparenchymal    0.5\n",
       "2  ID_28fbab7eb_intraventricular    0.5\n",
       "3      ID_28fbab7eb_subarachnoid    0.5\n",
       "4          ID_28fbab7eb_subdural    0.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission=pd.read_csv(data_dir+'stage_1_sample_submission.csv')\n",
    "sample_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Label</th>\n",
       "      <th>Sub_type</th>\n",
       "      <th>PatientID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID_28fbab7eb_epidural</td>\n",
       "      <td>0.5</td>\n",
       "      <td>epidural</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID_28fbab7eb_intraparenchymal</td>\n",
       "      <td>0.5</td>\n",
       "      <td>intraparenchymal</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID_28fbab7eb_intraventricular</td>\n",
       "      <td>0.5</td>\n",
       "      <td>intraventricular</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID_28fbab7eb_subarachnoid</td>\n",
       "      <td>0.5</td>\n",
       "      <td>subarachnoid</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID_28fbab7eb_subdural</td>\n",
       "      <td>0.5</td>\n",
       "      <td>subdural</td>\n",
       "      <td>28fbab7eb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              ID  Label          Sub_type  PatientID\n",
       "0          ID_28fbab7eb_epidural    0.5          epidural  28fbab7eb\n",
       "1  ID_28fbab7eb_intraparenchymal    0.5  intraparenchymal  28fbab7eb\n",
       "2  ID_28fbab7eb_intraventricular    0.5  intraventricular  28fbab7eb\n",
       "3      ID_28fbab7eb_subarachnoid    0.5      subarachnoid  28fbab7eb\n",
       "4          ID_28fbab7eb_subdural    0.5          subdural  28fbab7eb"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_base_df=sample_submission.copy()\n",
    "test_base_df['Sub_type'] = test_base_df['ID'].str.split(\"_\", n = 3, expand = True)[2]\n",
    "test_base_df['PatientID'] = test_base_df['ID'].str.split(\"_\", n = 3, expand = True)[1]\n",
    "test_base_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ids=test_df.PatientID.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['epidural', 'intraparenchymal', 'intraventricular', 'subarachnoid',\n",
       "       'subdural', 'any'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_types=test_base_df.Sub_type.unique()\n",
    "sub_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f153620a2b61441bb7cb7f1acf392844",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=6), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "189e8ac701514c2c963161608d43a459",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "dfs =[]\n",
    "for sub_type in tqdm_notebook(sub_types):\n",
    "    df = test_base_df[test_base_df.Sub_type==sub_type][['PatientID','Label']].copy()\n",
    "    df=df.rename(columns={\"Label\": sub_type}).reset_index(drop=True)\n",
    "    dfs.append(df)\n",
    "test_df=dfs[0]\n",
    "for df in tqdm_notebook(dfs[1:]):\n",
    "    test_df=test_df.merge(df,on='PatientID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.5</td>\n",
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       "      <td>0.5</td>\n",
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      ],
      "text/plain": [
       "   PatientID  epidural  intraparenchymal  intraventricular  subarachnoid  \\\n",
       "0  28fbab7eb       0.5               0.5               0.5           0.5   \n",
       "1  877923b8b       0.5               0.5               0.5           0.5   \n",
       "2  a591477cb       0.5               0.5               0.5           0.5   \n",
       "3  42217c898       0.5               0.5               0.5           0.5   \n",
       "4  a130c4d2f       0.5               0.5               0.5           0.5   \n",
       "\n",
       "   subdural  any  \n",
       "0       0.5  0.5  \n",
       "1       0.5  0.5  \n",
       "2       0.5  0.5  \n",
       "3       0.5  0.5  \n",
       "4       0.5  0.5  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5fe21e059b0f4fff9decf2a356119816",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=78545), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "PID = np.zeros(test_df.shape[0],dtype=object)\n",
    "StudyI = np.zeros(test_df.shape[0],dtype=object)\n",
    "SeriesI = np.zeros(test_df.shape[0],dtype=object)\n",
    "WindowCenter = np.zeros(test_df.shape[0],dtype=object)\n",
    "WindowWidth = np.zeros(test_df.shape[0],dtype=object)\n",
    "ImagePositionX = np.zeros(test_df.shape[0],dtype=np.float)\n",
    "ImagePositionY = np.zeros(test_df.shape[0],dtype=np.float)\n",
    "ImagePositionZ = np.zeros(test_df.shape[0],dtype=np.float)\n",
    "\n",
    "for i,row in tqdm_notebook(test_df.iterrows(),total=test_df.shape[0]):\n",
    "    ds = pydicom.dcmread(test_images_dir + 'ID_{}.dcm'.format(row['PatientID']))\n",
    "    SeriesI[i]=ds.SeriesInstanceUID\n",
    "    PID[i]=ds.PatientID\n",
    "    StudyI[i]=ds.StudyInstanceUID \n",
    "    WindowCenter[i]=ds.WindowCenter\n",
    "    WindowWidth[i]=ds.WindowWidth\n",
    "    ImagePositionX[i]=float(ds.ImagePositionPatient[0])\n",
    "    ImagePositionY[i]=float(ds.ImagePositionPatient[1])\n",
    "    ImagePositionZ[i]=float(ds.ImagePositionPatient[2])\n",
    "test_df['SeriesI']=SeriesI\n",
    "test_df['PID']=PID\n",
    "test_df['StudyI']=StudyI\n",
    "test_df['WindowCenter']=WindowCenter\n",
    "test_df['WindowWidth']=WindowWidth\n",
    "test_df['ImagePositionZ']=ImagePositionZ\n",
    "test_df['ImagePositionX']=ImagePositionX\n",
    "test_df['ImagePositionY']=ImagePositionY\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.to_csv(data_dir+'test.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "1+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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